Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Dalili Shoaei, Arash
Doctor of Philosophy - PhD
University of British Columbia
Cell/particle separation has a wide range of applications in environmental monitoring (e.g., air/water purification), food industry (e.g., microfiltration for improving food quality), oil and gas (e.g., particle separation in cyclones), and medicine/biotechnology (e.g., isolation of circulating tumor cells). Conventional procedures for separation purposes require costly infrastructure and several operational steps which are time-consuming and associated with human error. In the past two decades, there has been a tendency to develop microfluidic techniques, so-called lab-on-a-chip (LOC), to miniaturize and automate these procedures. Differences in the physical, electrical and biological properties of target cells/particles from the other components present in the fluid samples are crucial in the development of such microfluidic-based separation techniques. One of these techniques, which uses dielectrophoresis (DEP) force, is capable of the electric-based manipulation of noncharged particles in a non-uniform electric. Despite the general success, DEP still has not reached its full potential for high throughput operations. This thesis aims at studying the effect of several parameters on the performance of DEP-based microfluidic devices. In essence, prior to the separation process, particles need to be focused in a narrow region. A common approach is sheath-assisted focusing, which is numerically studied in this thesis to report the effective factors on the focusing width. COMSOL Multiphysics modeling is used to find the effect of the geometry of the electrodes (width and spacing) on the resulting DEP force. The effect of the applied voltage, channel height on the DEP-based manipulation of particles is investigated and it is shown that higher voltages and lower channel heights lead to more effective DEP manipulation. Furthermore, the effect of the length and width of the channel, the deflection angle of the electrodes, the volumetric throughput, and the particle size on the DEP-induced displacement of particles is studied and used to develop a model to predict the displacement. The applicability of these findings is shown by testing the platform with NIH 3T3 mouse fibroblast cells. Finally, the developed device is used for sheath-assisted and sheathless separation of three sizes of particles, and the throughput, as well as the separation efficiency of each design, is reported.
DEVELOPING A LAB-ON-A-CHIP PLATFORM FOR MANIPULATION AND SEPARATION OF MICROPARTICLES by Arash Dalili Shoaei B.Sc., Amirkabir University of Technology, 2012 M.Sc., Amirkabir University of Technology, 2015 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) December 2020 © Arash Dalili, 2020 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Developing a Lob-On-a-Chip platform for manipulation and separation of microparticles submitted by Arash Dalili Shoaei in partial fulfillment of the requirements of the degree of Doctor of Philosophy . Dr. Mina Hoorfar, School of Engineering, The University of British Columbia Supervisor Dr. Homayoun Najjaran, School of Engineering, The University of British Columbia Supervisory Committee Member Dr. Keekyoung Kim, Department of Mechanical and Manufacturing Engineering, The University of Calgary Supervisory Committee Member Dr. Liwei Wang, School of Engineering, The University of British Columbia University Examiner Dr. Ali Dolatabadi, Department of Mechanical and Industrial Engineering, Concordia University External Examiner iii Abstract Cell/particle separation has a wide range of applications in environmental monitoring (e.g., air/water purification), food industry (e.g., microfiltration for improving food quality), oil and gas (e.g., particle separation in cyclones), and medicine/biotechnology (e.g., isolation of circulating tumor cells). Conventional procedures for separation purposes require costly infrastructure and several operational steps which are time-consuming and associated with human error. In the past two decades, there has been a tendency to develop microfluidic techniques, so-called lab-on-a-chip (LOC), to miniaturize and automate these procedures. Differences in the physical, electrical and biological properties of target cells/particles from the other components present in the fluid samples are crucial in the development of such microfluidic-based separation techniques. One of these techniques, which uses dielectrophoresis (DEP) force, is capable of the electric-based manipulation of noncharged particles in a non-uniform electric. Despite the general success, DEP still has not reached its full potential for high throughput operations. This thesis aims at studying the effect of several parameters on the performance of DEP-based microfluidic devices. In essence, prior to the separation process, particles need to be focused in a narrow region. A common approach is sheath-assisted focusing, which is numerically studied in this thesis to report the effective factors on the focusing width. COMSOL Multiphysics modeling is used to find the effect of the geometry of the electrodes (width and spacing) on the resulting DEP force. The effect of the applied voltage, channel height on the DEP-based manipulation of particles is investigated and it is shown that higher voltages and lower channel heights lead to more effective DEP manipulation. Furthermore, the effect of the length and width of the channel, the deflection angle of the electrodes, the volumetric throughput, and the particle size on the DEP-induced displacement of particles is studied and used to develop a model to predict the displacement. The applicability of these findings is shown by testing the platform with NIH 3T3 mouse fibroblast cells. Finally, the developed device is used for sheath-assisted and sheathless separation of three sizes of particles, and the throughput, as well as the separation efficiency of each design, is reported. iv Lay Summary Point of care devices for early diagnosis are becoming popular for clinical and domestic users. Examples are the pregnancy kits or the clinical handheld devices for bacteria detection in saliva or urine. A common platform used in these technologies is microfluidics where the fluid of interest is passed through micron-sized channels including sorting and detection units in their downstream. Microfluidics is attractive for cell sorting for biomedical applications. An example is the isolation of circulating tumor cells, which enables diagnosing and monitoring the progression of cancer through a non-invasive liquid biopsy. Among these techniques, dielectrophoresis (DEP), an electric-based method, is thriving as it is cost-effective, easy to fabricate and operate, and reliable. Despite the recent advances, the DEP platforms are not optimized to reach their potential in their efficiency. This thesis aims to enhance the performance of these systems by carrying out numerical and experimental studies. v Preface The research presented in this thesis is the original work performed by the author. This thesis was supervised by Dr. Mina Hoorfar at the Advanced Thermo-Fluidic Laboratory (ATFL) in the School of Engineering, University of British Columbia. Parts of this thesis have been published in journals and presented in conferences. The details of the publications and the author’s contributions in them are explained below: a. Refereed journal publications • “A review of sorting, separation, and isolation of cells and microbeads for biomedical applications: microfluidic approaches”, A. Dalili, E. Samiei, M. Hoorfar, Analyst, 144 (1), 87-113, 2019. Featured on the Cover of the Journal. Contribution: Developing the idea, performing the review, performing major parts of analysis, writing the manuscript. • Parametric study on the geometrical parameters of a lab-on-a-chip platform with tilted planar electrodes for continuous dielectrophoretic manipulation of microparticles, A. Dalili, E. Taatizadeh, H. Tahmooressi, N. Tasnim, P. Rellstab, M. Shaunessi, H. Najjaran, M. Hoorfar, Scientific Reports, 10 (1), 1-11, 2020. Contribution: Developing the idea, design of experiment, conducting parts of the experiments, performing major parts of data analysis, writing the manuscript. • Dielectrophoretic manipulation of particles on a microfluidics platform with planar tilted electrodes, A. Dalili, H. Montazerian, K. Sakthivel, N. Tasnim, M. Hoorfar, Sensors and Actuators B: Chemical, 2020. In Press. Contribution: Developing the idea, design of experiment, conducting major parts of the experiments, performing major parts of data analysis, writing the manuscript. • Sheath-assisted focusing of microparticles on lab-on-a-chip platforms, A. Dalili, M. Sattari, N. Tasnim, M. Hoorfar, Electrophoresis, 2020. In press. vi Contribution: Developing the idea, conducting the experiments, performing parts of data analysis, writing major parts of the manuscript. • Sheath-assisted versus sheathless dielectrophoretic particle separation, A. Dalili, M Hoorfar, submitted paper. Contribution: Developing the idea, design of experiment, conducting the experiments, performing data analysis, writing the original manuscript. b. Conference contributions: • Characterization of the electrodes of DEP-based micro-separator, A. Dalili, E. Taatizadeh, M. Hoorfar, Proceedings of The Canadian Society for Mechanical Engineering International Congress, Toronto, 2018. Contribution: Developing the idea, design of experiment, performing the data analysis, writing the manuscript. • Acoustophoretic-based microfluidic platform for sorting extracellular vesicles, E Taatizadeh, A. Dalili, N. Tasnim, C. Garnis, M. Daugaard, I. Li, M. Hoorfar, International Society for Extracellular Vesicles 2019, Kyoto, Japan. Contribution: Contributed to the development of the basic idea and fabrication of the device, performing parts of the experiments. • Multi-size continuous particle separation on a dielectrophoresis-based microfluidics chip, A. Dalili, M. Hoorfar, Dielectrophoresis 2021, Flagstaff, USA. Contribution: Developing the idea, design of experiment, device fabrication, performing the data analysis, writing the manuscript. c. Other contributions: • Piezoresistive sensing in chopped carbon fiber embedded PDMS yarns, H. Montazerian, A. Dalili, AS. Milani, M. Hoorfar, Composites Part B: Engineering, 2019, 164, 648-658. Contribution: Contributed to the development of the basic idea, performing parts of the vii experiments, reviewing and editing the manuscript. • Graphene‐Coated Spandex Sensors Embedded into Silicone Sheath for Composites Health Monitoring and Wearable Applications, H Montazerian, A Rashidi, A Dalili, H Najjaran, AS Milani, M Hoorfar, Small, 15 (17), 1804991, 2019. Contribution: contributed to the development of the basic idea, performing parts of the experiments, reviewing and editing the manuscript. • Graphene/poly (methyl methacrylate) electrochemical impedance-transduced chemiresistor for detection of volatile organic compounds in aqueous medium, A Yavarinasab, S Janfaza, N Tasnim, H Tahmooressi, A Dalili, M Hoorfar, Analytica Chimica Acta, 2020. In press. Contribution: Contributed to the development of the basic idea and lead the device fabrication, review and editing the manuscript. • Micron-sized particle separation with standing surface acoustic wave—Experimental and numerical approaches, E Taatizadeh, A Dalili, P. Rellstab, H Tahmouressi, A Ravishankara, N Tasnim, H Najjaran, I Lee, M Hoorfar, submitted paper. Contribution to the development of the basic idea and fabrication of the device, performing parts of the experiments and data analysis, reviewing and editing manuscript. • A graphene-based chemical sensor for hydrogen sulfide measurement in water, A. Yavarinasab, S Janfaza, MM Montazeri, Nishat T, Ali D , P Kadota, P Markin, A Dalili, E Taatizadeh, H Tahmooressi, M Hoorfar, Proceedings of The IEEE SENSORS 2019, 1-4, Montreal, Canada. Contribution to the development of the basic idea and lead the device fabrication. viii Table of Contents Abstract .............................................................................................................................................. iii Lay Summary .................................................................................................................................... iv Preface ................................................................................................................................................ v Table of Contents ............................................................................................................................. viii List of Tables .................................................................................................................................... xii List of Figures .................................................................................................................................. xiii Acknowledgements......................................................................................................................... xvii Dedication ...................................................................................................................................... xviii Chapter 1: Introduction........................................................................................................................ 1 1.1 Separation methods .............................................................................................................. 1 1.1.1 Microfiltration................................................................................................................ 2 1.1.2 Inertia-based ................................................................................................................. 5 1.1.3 Deterministic lateral displacement................................................................................. 8 1.1.4 Pinched flow fractionation ........................................................................................... 10 1.1.5 Acoustophoresis ........................................................................................................... 12 1.1.6 Dielectrophoresis ....................................................................................................... 16 1.1.7 Magnetophoresis .......................................................................................................... 21 1.1.8 Optical ......................................................................................................................... 24 1.1.9 Hybrid systems ............................................................................................................ 27 1.2 Commercialization of LOC cell sorters.............................................................................. 31 1.3 Motivation .......................................................................................................................... 32 1.4 Objectives ........................................................................................................................... 34 1.5 Thesis organization ............................................................................................................ 35 Chapter 2: Theoretical background ................................................................................................... 37 2.1 Overview ............................................................................................................................ 37 ix 2.2 Theory of DEP ................................................................................................................... 37 2.3 Particle trajectories ............................................................................................................. 41 Chapter 3: Device fabrication ............................................................................................................ 42 3.1 Overview ............................................................................................................................ 42 3.2 Electrode deposition and patterning ................................................................................... 43 3.3 Channel fabrication ............................................................................................................ 44 3.4 Bonding PDMS to glass ..................................................................................................... 45 Chapter 4: Sheath-assisted focusing of microparticles on lab-on-a-chip platforms .......................... 47 4.1 Overview ............................................................................................................................ 47 4.2 Design ................................................................................................................................ 51 4.3 Numerical simulation ......................................................................................................... 51 4.3.1 Governing equations .................................................................................................... 51 4.3.2 Numerical procedure ................................................................................................... 53 4.3.3 Mesh study ................................................................................................................... 54 4.4 Experimental procedure ..................................................................................................... 55 4.4.1 Device fabrication ........................................................................................................ 55 4.4.2 Experiments and data acquisition ................................................................................ 56 4.4.3 Validation of the simulation ........................................................................................ 57 4.5 Results and discussion ........................................................................................................ 58 4.6 Summary ............................................................................................................................ 66 Chapter 5: Parametric study of electrode geometry .......................................................................... 68 5.1 Overview ............................................................................................................................ 68 5.2 Simulation of the electric field ........................................................................................... 68 5.3 Results ................................................................................................................................ 71 5.4 Summary ............................................................................................................................ 75 Chapter 6: Parametric study of effective throughput of the device ................................................... 77 x 6.1 Overview ............................................................................................................................ 77 6.2 Methods .............................................................................................................................. 79 6.2.1 Fabrication of the device ............................................................................................. 79 6.3 Sample preparation and experimental procedure ............................................................... 80 6.4 Design of the device ........................................................................................................... 81 6.5 Results ................................................................................................................................ 83 6.6 Summary ............................................................................................................................ 87 Chapter 7: Dielectrophoretic manipulation of particles..................................................................... 89 7.1 Overview ............................................................................................................................ 89 7.2 Design, material, and method ............................................................................................. 91 7.2.1 Design .......................................................................................................................... 91 7.2.2 Device fabrication, sample preparation, and experimental setup ................................ 92 7.2.3 Experimental factors and level .................................................................................... 95 7.3 Data acquisition .................................................................................................................. 95 7.4 Results and discussion ........................................................................................................ 96 7.4.1 Effect of channel width .............................................................................................. 100 7.4.2 Effect of flow rate ...................................................................................................... 100 7.4.3 Effect of channel length ............................................................................................. 102 7.4.4 Effect of the deflection angle of the electrodes ......................................................... 104 7.4.5 Effect of particle size ................................................................................................. 106 7.5 Applications in particle focusing and separation ............................................................. 107 7.6 Applications in cell manipulation .................................................................................... 108 7.7 Summary .......................................................................................................................... 110 Chapter 8: Particle focusing and separation .................................................................................... 112 8.1 Overview .......................................................................................................................... 112 8.2 Generalized linear model ................................................................................................. 114 xi 8.3 Design .............................................................................................................................. 115 8.4 Experiments ...................................................................................................................... 117 8.4.1 Device fabrication ...................................................................................................... 117 8.4.2 Sample preparation .................................................................................................... 119 8.5 Results .............................................................................................................................. 119 8.5.1 Binary particle separation .......................................................................................... 119 8.5.2 Ternary particle separation ........................................................................................ 120 8.6 Discussions ....................................................................................................................... 124 8.7 Summary .......................................................................................................................... 125 Chapter 9: Conclusions.................................................................................................................... 126 9.1 Summary .......................................................................................................................... 126 9.1.1 Sheath-assisted focusing of particles ......................................................................... 126 9.1.2 Effect of interdigitated electrodes .............................................................................. 127 9.1.3 Effect of electric parameters ...................................................................................... 127 9.1.4 Effect of geometrical and operational parameters ..................................................... 128 9.1.5 Particle separation ...................................................................................................... 128 9.2 Contributions to the field .................................................................................................. 129 9.3 Future works ..................................................................................................................... 130 References ................................................................................................................................... 132 xii List of Tables Table 1-1 A list of a few commercialized microfluidics-based cell sorters with their mechanisms, applications, and throughput .................................................................................................................. 32 Table 4-1 The range of the parameters that are studied in this research. ............................................... 51 Table 7-1 The summary of the studied factors and their levels. ............................................................ 95 xiii List of Figures Figure 1-1 A weir-type microfilter in a cross-flow mode. ....................................................................... 4 Figure 1-2 Schematic of different types of microfilters. .......................................................................... 5 Figure 1-3 Inertia-based microfluidic devices. ........................................................................................ 7 Figure 1-4 schematics of microchannels with a series of arc grooves and serpentine channels. ............. 8 Figure 1-5 Schematic of DLD devices. ................................................................................................... 9 Figure 1-6 Schematic of the first pinched flow fractionation device. .................................................... 11 Figure 1-7 Schematic of a standing surface acoustic wave device. ....................................................... 15 Figure 1-8 A DEP microseparator for the continuous sorting of stem cells and osteoblasts ................. 18 Figure 1-9 Continuous DEP-based cell separation. ............................................................................... 19 Figure 1-10 The interaction of hydrodynamic and DEP forces on positioning microparticles. ............ 20 Figure 1-11 chematic of a 3D DEP based device used for the continuous separation of colloidal particles. ................................................................................................................................................. 21 Figure 1-12 On-chip free flow magnetophoresis. .................................................................................. 23 Figure 1-13 A magnetophoretic-based device for the separation of blood cells.................................... 24 Figure 1-14 A LOC using optical forces for sseparation purposes. ....................................................... 26 Figure 1-15 Schematic of an optical tweezer. ........................................................................................ 27 Figure 1-16 Schematic of a DEP-Magnetic Activated Cell Sorter. ....................................................... 28 Figure 1-17 A combination of MOFF and DEP, which is used for the separation of breast cancer cells from blood samples. ............................................................................................................................... 30 Figure 2-1 The CM factor for polystyrene particles as a function of the frequency. ............................ 40 Figure 3-1 The microfabrication of the developed chip. ....................................................................... 42 Figure 3-2 Examples of photolithography masks for electrode patterning. ........................................... 43 Figure 3-3 Examples of the photolithography masks for the fabrication of SU8 molds. ...................... 45 Figure 3-4 The fabrication procedure of PDMS channels. .................................................................... 45 Figure 3-5 The bonding of the PDMS channel and glass substrate. ...................................................... 46 xiv Figure 4-1 The schematic of the propsed DEP device. ......................................................................... 51 Figure 4-2 The G1(s) and G2(s) functions, which were used to define the wall-induced lift force. ....... 53 Figure 4-3 Velocity profiles in different numbers of elements. ............................................................. 55 Figure 4-4 The fabricated experimental device. .................................................................................... 56 Figure 4-5 The initial position of the particles before being affected by the DEP force. ...................... 57 Figure 4-6 The experimental and numerical results for the normalized focusing width of microparticles......................................................................................................................................... 58 Figure 4-7 Example results of the simulation of the fluid flow. Streamlines are presented for a variety of conditions. ......................................................................................................................................... 59 Figure 4-8 The Pareto chart for the standardized effect of the studied parameters on the focusing width. ..................................................................................................................................................... 60 Figure 4-9 The residual plots for the regression model for the prediction of the focusing width.......... 61 Figure 4-10 The normalized focusing width of the particles as a function of the flow rate ratio, flow rate, particle size. ................................................................................................................................... 62 Figure 4-11 The Pareto chart for the standardized effect of the parameters on the standard deviation of positions of the particles. ....................................................................................................................... 63 Figure 4-12 The interaction plots of the factors that have significantly contributed to the STD of the position of the particles. ......................................................................................................................... 64 Figure 4-13 The residual plots for the generalized linear regression model that is developed for the prediction of the STD of the particle position. ...................................................................................... 65 Figure 4-14 The standard deviation of the lateral position of the particles as a function of the junction angle of the particle inlet, width ratio, flow rate ratio, and particle size. .............................................. 66 Figure 5-1 Geometry of the simulation performed using COMSOL Multiphysics software. ............... 69 Figure 5-2 An example of the meshed geometry used for the simulation of the electric field ............. 70 Figure 5-3 Presentation of the electric field. .......................................................................................... 72 xv Figure 5-4 The combined effect of the electrode spacing and electrode width on the induced DEP force. ...................................................................................................................................................... 73 Figure 5-5 DEP force as a function of the electrode spacing for each one of the studied channel heights. ................................................................................................................................................... 74 Figure 5-6 The effect of the channel height on DEP force for the chosen electrode configuration. .... 75 Figure 6-1 Examples of the fabricated devices. ..................................................................................... 80 Figure 6-2 The experimental setup. ....................................................................................................... 81 Figure 6-3 A schematic of the proposed DEP-based micro-separator with all its elements. ................. 83 Figure 6-4 T he effect of the applied voltage squared on the effective throughput. .............................. 85 Figure 6-5 The effect of the channel height on the effective throughput. ............................................. 86 Figure 6-6 The effect of the electrode angle on the effective flow rate. ................................................ 87 Figure 7-1 A schematic of the device presenting the geometrical parameters of the device. ................ 91 Figure 7-2 Fabrication of the LOC devices. .......................................................................................... 93 Figure 7-3 Representation of the experimental setup including the microscope, syringe pumps, and function generator. ................................................................................................................................. 94 Figure 7-4 Sample images of how the DMV works. ............................................................................. 96 Figure 7-5 The trajectory and distribution of position of 15-𝜇m polystyrene particles ......................... 97 Figure 7-6 The residual plots showing the validity of the ANOVA test. .............................................. 98 Figure 7-7 The main effect plot showing the effect of each parameter on the position of the particles, and the interaction plots. ........................................................................................................................ 99 Figure 7-8 The distribution of the particles affected by nDEP at different flow rates and particle sizes. ............................................................................................................................................................. 101 Figure 7-9 The displacements of particles in 7 mm and 14 mm channels at different flow rates. ...... 102 Figure 7-10 The effect of the particle size and flow rate on the lateral position of the particles. ........ 103 Figure 7-11 The effect of the particle size and flow rate on the displacement along the width of 7-mm and 14-mm channels. ........................................................................................................................... 104 xvi Figure 7-12 The relative change in the position of particles passing over electrodes deflected at 10° compared to electrodes deflected at 45°. ............................................................................................. 106 Figure 7-13 The CM factor response of the fibroblasts (3T3) cells based on the single-shell model for different conductivities of the media. .................................................................................................. 108 Figure 7-14 The normalized position of NIH 3T3 mouse fibroblast cells. .......................................... 109 Figure 8-1 The positions of microparticles as a function of the length of the DEP region, the deflection angle of the electrodes, volumetric flow rate, and particle size. .......................................................... 115 Figure 8-2 The schematics of the proposed DEP separators. .............................................................. 117 Figure 8-3 The fabrication of the devices. ........................................................................................... 118 Figure 8-4 The sheathless DEP microseparator. .................................................................................. 118 Figure 8-5 The sheath-assisted DEP microseparator. .......................................................................... 119 Figure 8-6 Binary separation of microparticles. .................................................................................. 120 Figure 8-7 Ternary separation of microparticles using the sheath-assisted microseparator. ............... 121 Figure 8-8 Ternary separation of microparticles using the sheathless microseparator. ....................... 122 Figure 8-9 The separation efficiency of the (a) sheath-assisted, (b) sheath-less, and (c) weak sheath-assisted microseparators. ..................................................................................................................... 124 Figure 9-1 A schematic of the proposed design for multi-cell separation. .......................................... 131 xvii Acknowledgements I would like to express my sincere gratitude to my dear supervisor, Dr. Mina Hoorfar, for her support in every step of my PhD studies and always trying to provide the best resources for all these studies. Her approachable, caring personality makes her a great supervisor who is always willing to listen and help. Her passion has always inspired me to not get frustrated in hard days. I would like to thank my committee members Dr. Najjaran and Dr. Kim for their kind advisory throughout my research. I am also grateful to all my previous mentors and educators who have helped me to flourish and grow. Many thanks to our lab manager, Nishat Tasnim, for doing such a great job in keeping the lab well organized and friendly. Special thanks to my colleagues, Dr. Sajjad Janfaza, Dr. Ehsan Samiei, Hossein Montazerian, Erfan Taatizade, Amirmohammad Sattari, Hamed Tahmouressi, Pamela Rellstab, Hanbyul Kim, and Matthew Shaunessi. I am so grateful for the collaborative environment that you guys created. Many thanks to my dear friends who made this PhD journey so joyful and lovely. None of this could have happened without the strong support from my family. Many thanks to my parents, Mohammad Dalili and Parvin Moradi, who have endlessly and nonconditionally supported me and nourished me with their love. Thanks a lot to my sisters Shiva and Atoosa for always and everywhere supporting and helping me. Special gratitude goes to my beloved wife, Sorayya Kazemi, for her love, support, and patience. I am so lucky to have her in my life. xviii Dedication This dissertation is dedicated to the Love of my life Sorayya. 1 Chapter 1: Introduction1 1.1 Separation methods Several biomedical-related research studies focus on specific cell types or analytes attached to microparticles which exist in the samples containing multiple other components that are not of interest 1. In such studies, the target components are required to be purified from the original sample or sorted based on their properties to be used or studied individually 2, 3. Depending on the application, these target components exist in moderate or low populations in the samples. An example of the moderate population is cancer immunotherapy, in which certain immune cells such as natural killer cells are separated from the body sample, cultured and or biologically modified, and then injected back to the patient 4, 5. Circulating tumor cells are rare cells existing in low numbers in the blood or lymphatic samples taken from cancer patients and are an indication of cancer metastasis, disease progression or survival 6, 7. In some applications also microparticles functionalized with specific receptors to certain analytes or cell surface markers are used to capture and purify the target components 8, 9. Conventionally, there have been laboratory-based strategies to isolate, sort or separate these target cells or microparticles which have been more or less successful. However, they require long processing hours, large volumes of samples and reagents and have been associated with human error as many of the steps are performed by skilled personnel 10. In recent years, advances in the development of lab-on-a-chip (LOC) devices have made it possible to perform an entire or partial analytical assay on a miniature device, barely the size of a smartphone 11-13. These devices have been applied to a wide range of applications including diagnosis 12, 14, 15, proteomics 16 and cell-based studies 17, 18. The success in downscaling the analytical assays and development of such compact LOC platforms has been achieved by the aid 1 Parts of this chapter have been published in Analyst (A. Dalli, Analyst (2019), 144 (1), 87-113). 2 of microfluidics, which is a powerful tool for manipulating fluids in the form of either a continuous flow (through pre-fabricated microchannels) or discrete droplets (on so-called digital microfluidics (DMF) devices) 19-22. Since the introduction of LOC, several microfluidic-based techniques have been developed to achieve separation or isolation of cells or microparticles from their original sample with the aim to enhance the efficiency and accuracy of these processes compared with the conventional laboratory-based procedures 23-26. Based on the physical and biological properties of the target components, these separation or isolation methods function based on different mechanisms including filtration, inertial forces, deterministic lateral flow, pinched flow fractionation, acoustic separation, dielectrophoresis (DEP), magnetic and optical forces. 1.1.1 Microfiltration Microfiltration is possibly the most well-known method for separating cells and particles. As a passive method of separation, this technique does not rely on any external forces to operate. This method separates particles according to their size and can be divided into three main categories: membranes 27-35, pillars 36-40 and weirs 41, 42. Microfiltration devices can also be classified according to the direction of the flow: dead end or cross-flow filters 1. In the case of dead-end, the flow is perpendicular to the filter plane; whereas in the case of cross-flow filtration, the flow is along the filter structure 43. Generally, dead-end filtration is a more efficient method for capturing large particles; however, clogging is a major problem associated with these filters. On the other hand, cross-flow filtration avoids clogging by washing away large particles29. The use of membrane filters for separating micro and nano particles dates back to late 1980s 44, 45, when Stemme and Kittilsland 45 fabricated a two membrane filter structure in silicon, which was able to separate particles down to 50 nm. The early micromembranes 44, 45 were applied as a dead-end type. However, there has recently been more attention to the cross-flow configuration. As an example, a microfluidic device consisting of two PDMS channels, which 3 are separated by a porous polycarbonate, was fabricated by Aran et al. 46. The polycarbonate with 200 nm pores functioned as a membrane to separate plasma from the rest of the blood sample. Since the blood cells cannot permeate through the membrane, the plasma passes through it at a rate that depends on the pressure in the main channel, size of the pores and flow resistance. By parallelizing 32 similar channels, this device can separate 2.5 mL of pure plasma in 4 hours. In addition to the membrane microfilters, weir and pillar microfilters have been widely utilized in both dead-end and cross-flow modes for the separation of particles (e.g., blood components 47). An example of a weir-type microfilter is shown in Figure 1-1. The capillary action is the operating mechanism for separating plasma from the rest of the blood. In this device, blood flows through a middle channel connected to two lateral channels used for collecting plasma. There are gaps in the middle and lateral channels, which prevent RBCs and WBCs from passing to the lateral channels 43, as the gaps in the weir-based devices provide filtration. In a pillar-type microfilter, the distance between the pillars is important for the separation of the particles. For example, a multilevel pillar microfilter, capable of separating different components of blood, was studied. Initially, an array of pillars with a 6 𝜇m gap was used to separates RBCs and plasma from WBCs. Afterward, the second array with a gap of 2.5 𝜇m was used to prevent the passage of the RBCs through the filter. This way, more than 95% of the RBCs were removed from the initial blood sample while the device recovered 27.4% of WBCs 37. 4 Figure 1-1 A weir-type microfilter in a cross-flow mode, which is used for the separation of plasma (Reproduced from 43 with permission from The Royal Society of Chemistry, copyright 2005.) Ji et al. 48 concluded that the pillar-type microfilters are the most suitable type for on-chip genomic analysis. They fabricated four different types of devices: a weir-type microfilter in a dead-end mode, a membrane microfilter in a dead-end mode, and pillar-type microfilters in both dead-end and cross-flow forms (all of which are shown in Figure 1-2). They compared the capability of each device for the separation of RBCs and WBCs. Considering the efficiency of separation and ease of fabrication, they suggested the pillar-type microfilters as the most efficient as compared to the weir and membrane types. Also, the pillar microfilter was shown to be more efficient, when used in a cross-flow mode rather than a dead-end one. According to Chiu et al. 49, further enhancement in the separation efficiency and throughput of cross-flow filters can be achieved by implementing sheath flows. In essence, the sheath flow pushes the particles towards the filter, and hence increases the chance of the smaller particles to be separated. To overcome the clogging problem of particles, Yoon et al. 50 proposed the addition of mechanical oscillation to the fluid flow in a pillar type microfilter. As the flow vibrates, larger particles (which are in contact with the filter) cannot move but the smaller particles pass through the filter. They managed to separate 20 and 5 𝜇m polystyrene particles with 100% efficiency. 5 Figure 1-2 Schematic of (a) weir-based filter, (b) pillar-based filter, (c) pillar filter in a cross- flow mode, (d) dead-end membrane filter (Reproduced from 48 with permission from Springer Nature, copyright 2008.) 1.1.2 Inertia-based The effect of the inertial lift force on particles was first found accidentally by Segre, observing the focusing behavior of the particles flowing inside a pipe 51. Later in the 1960s and 1970s, other studies 51-53 provided a better understanding of this phenomenon. However, this technique was not used in microfluidic devices until 2007, when Di Carlo et al. 54 fabricated the first microfluidic device of this kind. They studied particle focusing in straight as well as curved microchannels. For the straight channels, the channel Reynolds number ( 𝑅𝑐 ) and particle Reynolds number (𝑅𝑝) were used to describe the effect of the inertial forces on particles. 𝑅𝑐 =𝑈𝑚𝐷ℎ𝜗 (1.1) 𝑅𝑝 = 𝑅𝑐𝑎2𝐷ℎ2 (1.2) In the above relations, 𝑈𝑚 is the maximum channel velocity, 𝐷ℎ is the hydraulic diameter, 𝜗 is the kinematic viscosity and 𝑎 is the particle diameter. For the particle’s Reynolds number of order of 1 or higher, the inertial forces are large enough to affect the particles. The magnitude 6 of the lift force exerted on the particles flowing between two infinite parallel plates can be calculated using Equation (1.3) 55: 𝐹 =𝜇2𝜌𝑅𝑝2𝑓𝑐(𝑅𝑐 , 𝑥𝑐) (1.3) where 𝑥𝑐 is the position of the particle. Deformable particles experience an extra lift force, away from the channel’s wall. The balance between the deformability-induced lift force and inertial induced lift force leads to the specific equilibrium position of the particles in the channel 56. For the case of the curved channels, a secondary flow is also created due to the centrifugal force. This flow is called the Dean flow, which is a rotational flow perpendicular to the main flow. As a result of this Dean flow, an additional drag force is exerted on the particles, which can sort them according to their size. The Dean number is a dimensionless number defined to characterize this type of flow: 𝐷𝑒 = 𝑅𝑒√𝐷ℎ2𝑟 (1.4) In this equation, 𝑟 is the radius of the curvature of the channel. Generating a secondary flow is not limited to the curved channels 54, 57-59, different geometries of channels were also suggested to produce a secondary flow. Among them, spiral channels have been used in several studies 60-72. Kuntaegowdanahalli et al. 64 used a spiral microchannel [see Figure 1-3(a)] to investigate the effect of the channel height and Dean number on particles positions. They managed to separate 10 𝜇m, 15 𝜇m and 20 𝜇m particles with an efficiency of 90%. Shen et al. 67 showed that by adding micro-obstacles in the serpentine channels [see Figure 1-3(b)], sheathless separation of different particles (including polymeric particles, CTCs, and blood cells) is possible. This device can achieve up to 99.8% particle focusing with a throughput as high as 3 ml/min. 7 Figure 1-3 (a) A spiral channel to separate particles according to their sizes (Reproduced from 64 with permission of The Royal Society of Chemistry, copyright 2009). (b) Micro-obstacles change the velocity field and lead to a higher dean flow acceleration. As a result, high-efficient, high-throughput sheathless separation is achieved. (Reproduced from 67 with permission from The Royal Society of Chemistry, copyright 2017.) The secondary flow can also be generated in a series of contraction and extraction channels 73-76, microchannel with slanted groove arrays on the top surface 77-79, and serpentine channels 80-83. The groove-based microchannels, shown in Figure 1-4(a), can also cause the secondary flow due to pressure difference in the transverse direction of the channel 78. The secondary flow causes the lateral rotation of the flow in the channel. Depending on the direction of the rotation and the magnitude of the secondary flow, the position of the particles can be affected. To maximize the magnitude of the secondary flow, which can lead to a higher resolution of the particle focusing, the ratio of the channel’s height to the groove’s height should be 1 84. An example of serpentine channels can be seen in Figure 1-4(b). The small footprint and ease of parallelization are the advantages of this type of structure 85. Zhang et al. parallelized 8 serpentine channels to separate plasma from a diluted blood sample. 8 Figure 1-4 (a) A schematic of a microchannel with a series of arc grooves. The interaction of the particles with the grooves can focus the particles into one single line (Reproduced from 78 with permission from nature, copyright 2017.) (b) A serpentine channel used for separating 3 𝜇m red and 10 𝜇m green particles. At the beginning of the channel, the particles are mixed. As they flow further in the channel, each type is focused on a separate line. As a result, the particles can be separated at the outlet (Reprinted by permission from Springer Nature81, Copyright 2014.) 1.1.3 Deterministic lateral displacement In 2004, Huang et al. 86 reported a novel method, referred to as deterministic lateral displacement (DLD), for the continuous separation of particles according to their sizes. In laminar flows, particles follow the streamline that passes through their center of mass. To take advantage of this characteristic, a periodic array of obstacles is used for separation. Each row of arrays is shifted from the last row by a certain distance. Since the arrays are not in the same direction as the flow, the flow produces curvy streamlines. As streamlines pass through the gaps between the arrays, their position relative to the arrays changes. Depending on the size of the posts, the distance between each post, and the length that they are shifted after each row, a critical 9 diameter can be found for any DLD microdevice. As a result, particles smaller than this critical diameter can be separated from those that are larger than the critical diameter. A schematic of a DLD design can be seen in Figure 1-5. Figure 1-5 A schematic of a DLD design. (A) Geometrical parameters are shown. (B) Based on the geometrical parameters shown in (A), streamlines are affected. (C) Particles follow the streamline that passes through their center of mass (Reproduced from 86 with permission from AAAS, copyright 2004.) This method has been used for several biological purposes including: the isolation of rare cells 87, 88, enrichment of CTCS 89, separation of viable and nonviable mammalian cells 90, and separation of WBCs 91 and RBCs 92, 93 from the blood. A phenomenon that can reduce the efficiency of separation in DLD devices is diffusion. Diffusion causes particles to move laterally with respect to their streamlines and mix with the other group of particles. The Stokes-Einstein equation provides the diffusion coefficient for spherical particles: 𝐷 =𝑘𝑇6𝜋𝜇𝑎 (1.5) where k is the Boltzmann constant, T is the temperature, μ is the viscosity, and 𝑎 is the diameter of the particle. For larger particles, the diffusion coefficient is lower, hence, the diffusion does not affect the trajectory of the particles. However, for smaller particles, the effect of the diffusion should be considered. More details about the theory related to this method can be found in a review paper by McGrath et al. 94. Based on the theory and experiments conducted by different researchers, several 10 criteria have been suggested regarding the critical diameter, post shape, array geometry, post size, edge correction, as well as the inlet and outlet design94. The early DLD devices used circular posts 86, and they have still been used in different studies 86, 95-99. A problem associated with these posts is that at the top of the circular posts stagnation occurs, which potentially can trap particles leading to clogging of the system 94. While increasing the gap between the posts can be helpful 100, another way to reduce the clogging effect is trying different geometries including quadrilateral 101, triangular 87, 99, 102, 103, and streamlined 104 designs. In addition to the clogging issue, it is shown that the posts are not suitable for the separation of deformable and non-spherical cells 105. To overcome these problems, Al-Fandi et al. 106 proposed diamond and airfoil posts and modeled them in addition to the conventional circular posts. The results of their simulation showed that using the airfoil posts was a better choice for separating non-spherical deformable cells compared to the circular or diamond posts. In essence, the airfoil posts experience a lower drag force compared to the circular posts due to their geometry. As a result, the cells are dragged less towards them, and hence deformability becomes a less important factor. 1.1.4 Pinched flow fractionation Pinched flow fractionation (PFF) is another passive method, which was proposed by Yamada et al. 107 in 2004. Similar to the DLD, this technique uses the laminar flow for the size-based separation of particles. PFF devices usually contain a T junction, see Figure 1-6, which is composed of two inlet channels that merge to the third narrow channel through a pinched segment. One of the inlet streams contains the particles and the other one is the sheath flow. The ratio of the two flow rates must be adjusted in a way to assure that the sheath flow pushes all the particles to the wall of the pinched segment. According to the size of the particles, their center of mass is placed at different distances from the wall. As mentioned earlier, in a laminar flow, the particles tend to flow along the streamline passing through their center of mass. Hence, the particles follow different streamlines in a PFF device, since the center of mass of the particles is 11 located at different lateral positions. The lateral distance of the streamline is amplified when the flow passes through the pinched segment and enters a broadened segment. Therefore, a small difference in the position of the particles is amplified and the particles are separated. Several parameters affecting the separation performance include the ratio of the flow rates, pinched section width, boundary angle, total flow rate, and geometry of the part that collects particles. Figure 1-6 A schematic of the first pinched flow fractionation device. (a) In the pinched segment, the particles are pushed to the wall. (b) The center of mass of the particles is on different streamlines, depending on their size. The distance between the streamlines increases in the wider part and the particles are separated. (Reprinted with permission from 107, Copyright 2004, American Chemical Society.) In the primary design 107 of such devices, symmetric channels were used for the outlets to equalize the resistance in all of the channels. One problem associated with this type of separation is that particles considerably smaller than the width of the channel cannot be separated since they all follow the path to the same outlet. To overcome this problem and increase the displacement of particles after passing the pinched segment, asymmetric pinched flow fractionation was proposed 108, in which one of the outlet 12 channels was broadened or shortened to change the resistance. This so-called drain channel causes the asymmetric distribution of the flow which changes the steam lines accordingly. It has been shown 108 that this method is capable of separating 1 𝜇m and 2.1 𝜇m particles at a flow rate of 20 𝜇L/h and an efficiency of 80%. In addition to the separation efficiency, the throughput of the device can be significantly increased by taking advantage of inertial forces in the channel. This technique, which is called inertial enhanced pinched flow fractionation, achieves an order of magnitude higher throughput than the conventional PFF 109. To increase the effectiveness of the inertial induced lift force on the particles, Lu and Xu 109, 110 elongated the pinched segment. This way, the lift force increases the lateral distance of the particles, enhancing the separation efficiency of this method. Since the particles are not meant to be pushed to the channel’s wall anymore, the ratio of the flow containing the particle mixture to the sheath fluid flow rate can be increased, which leads to higher throughput. Later, Lu and Xuan 110 implemented a viscous solution in their design to take advantage of the combined effect of the inertial lift and elastic forces. They referred to it as elasto-inertial pinched flow fractionation and mentioned that it can separate particles even smaller than 1 𝜇m, which makes it unique from inertia-based separation techniques. 1.1.5 Acoustophoresis Acoustic waves are used as an active method for the separation of cells and particles. The magnitude of the acoustic radiation force, which is applied to suspended particles, can be calculated using equation (1.6) 111: 𝐹 = −(𝜋𝑝02𝑉𝑝𝛽𝑚2𝜆)𝜑(𝛽, 𝜌)sin⁡(2𝑘𝑥) (1.6) In this equation, subscript p and m represent particle and medium, respectively. 𝑝0 is the acoustic pressure amplitude, 𝑉𝑝 is the volume of the particle, 𝛽 is the compressibility of the ambient medium, 𝜆 is the wave length, k is the wave number and 𝜑 is the acoustic contrast factor. The acoustic contrast factor is calculated using Equation (1.7): 13 𝜑(𝛽, 𝜌) =5𝜌𝑝 − 2𝜌𝑚2𝜌𝑝 + 𝜌𝑚−𝛽𝑝𝛽𝑚 (1.7) Standing acoustic waves, which have been used in many studies to manipulate particles, consist of fixed pressure nodes and antinodes. The minimum amplitude of the pressure along a standing acoustic wave is on the pressure node; while the maximum pressure is on the antinode. The sign of the acoustic contrast factor, which depends on the density and compressibility of the particle and medium, is the decisive factor in determining the sign, and consequently the direction of the acoustic force. A positive contrast, which is usually the case for solid particles, means that the acoustic radiation force pushes the particles towards the pressure nodes. On the other hand, a negative acoustic contrast factor, which is usually the case for bubbles, results in the movement of the particles towards the pressure antinodes. Based on Equation (1.6), separation of particles can be achieved according to the size 112, 113, density 114, and compressibility 115. The combined effect of density and compressibility can be observed in terms of the acoustic contrast factor. Also, the role of the medium’s density is shown in a study by Peterson et al. 116. Different methods have been applied to separate particles as a result of the differences in the aforementioned characteristics. One method is to separate particles having opposite signs for their acoustic contrast factors. To achieve the opposite signs, it is important to choose the right medium. This method is desirable because particles can be pushed towards different lateral positions. However, the use of this method is limited, since the acoustic contrast factor of most of the particles is positive. Thus, many studies that use acoustic waves for separation rely on the difference in the magnitude of the acoustic radiation force, which is applied to different particles. A common procedure for the separation of the particles based on the intensity of the acoustic radiation force is to focus them along the wall of the channel and then expose the particles to the acoustic radiation force. The acoustic field is orthogonal to the flow, so the particles move laterally towards the pressure nodes, which are usually at the center of the channel. By adjusting 14 the flow rate, width, and length of the channel and the intensity of acoustic waves, different particles can be guided towards different outlets. The feasibility of this concept has been proven by Johnson and Feke 117 who separated 100 and 170 𝜇m polystyrene particles. Chen et al. 113 showed that this method is capable of high throughput separation of platelets from whole blood. Their device showed the capability of removing more than 85% of WBCs and RBCs while recovering more than 80% of platelets with a flow rate of 10 mL/min. The studies reviewed so far use bulk acoustic waves (BAWs). BAWs microfluidics chips are usually comprised of a bulky transducer that is attached to the wall of the channel. The transducer generates acoustic waves, which are supposed to propagate from the outside of the channel to form a standing wave. As a result, the channel has a high acoustic reflection quality. However, this technique limits the choice of the materials (e.g. glass and silicon) used for the channel. For instance, PDMS cannot be used for this method. To solve this problem, two transducers bonded to both sides of the channels can be used. However, due to their complex design, such devices have not been popular 118. Using surface acoustic waves (SAWs), on the other hand, will not only solves this problem but also provides a more precise, energy-efficient, and easier solution for mass production 119. For SAW devices, two interdigitated transducers (IDTs) are patterned on a piezoelectric surface at two different sides of the channel. Several studies have used SAWs for the separation of particles either in the form of standing surface acoustic wave (SSAW) 112, 120-122, or traveling surface acoustic wave (TSAW) 123-128. Nam et al. 129 used standing surface acoustic waves (SSAWs) for the separation of particles according to their size (see Figure 1-7). Using the hydrodynamic force applied by sheath flows, they first focused the particles at the centre of the channel. Next, they adjusted the SSAW so that the pressure nodes are positioned at the walls and the pressure antinode is at the centre of the channel. 15 Figure 1-7 Schematic of a standing surface acoustic wave device. The sheath flow focuses all the particles to the middle of the channel. The larger particles experience larger acoustic force towards the channel wall (Reprinted by permission from Springer Nature 129, Copyright 2011.) The wavelength of acoustic waves is set as half of the channel width for most of the SSAWs techniques, as it is desired to have the pressure nodes on the side walls. The wavelength used in these types of experiments is typically larger than the particles. However, when the wavelength is close to the particle size, the acoustic radiation force applied to the particles increases nonlinearly. This means that by implementing smaller wavelengths more precise separation can be achieved based on the particle size. The application of traveling surface acoustic waves (TSAW) instead of SSAW can make it possible to use smaller wavelengths while only one IDT is required to be deposited for the actuation 130. Also for the fabrication, the IDT does not need to be parallel with the channel 131. Using this method Destgeer et al. 131 were able to separate 3 𝜇m and 10 𝜇m particles with 100% efficiency. In another study, Ma et al. 130 used TSAW to separate polystyrene and polymethylmethacrylate according to their mechanical properties. Wang et al. 132 combined SSAW and TSAW to achieve sheathless isolation of CTCs from 16 the blood sample. More detailed information about the applications of SAWs can be found in the literature 111, 119, 133, 134. 1.1.6 Dielectrophoresis When a particle is in a non-uniform electric field, it is polarized and experiences a force from the electric field. This phenomenon is referred to as dielectrophoresis (DEP). The DEP force can be calculated as: 𝐹𝐷𝐸𝑃 = 2𝜋𝜀𝑚𝑅3𝐶𝑀(∇𝐸2) (1.8) where 𝜀𝑚 is the absolute permittivity of the medium, 𝑅 is the radius of the particle, 𝐶𝑀 is the Clausius Mossotti factor and 𝐸 is the electric field. The 𝐶𝑀 factor depends on the relative permittivity of the particle and medium and it is the factor that determines the sign of the DEP force. 𝐶𝑀 =𝜀𝑝 − 𝜀𝑚𝜀𝑝 + 2𝜀𝑚 (1.9) Based on the sign of the 𝐶𝑀 factor, there are two categories of DEP: negative dielectrophoresis (nDEP) and positive dielectrophoresis (pDEP). The pDEP happens when the particle is more polarizable than the medium and the DEP force on the particles is towards the highest intensity electric field regions. On the other hand, the higher polarizability of the medium in comparison to the particle, generates an nDEP effect, pushing the particles towards the lowest intensity electric field regions. Particle separation can be achieved using the DEP force according to the size of the particles or the 𝐶𝑀 factor. The latter depends on the permittivity of the particles relative to the permittivity of the medium. Permittivity also changes with the frequency of the applied voltage. The frequency at which the sign of the 𝐶𝑀 factor (and hence the sign of the DEP force) changes is known as the cross-over frequency. By applying the frequency in the range between the cross-over frequencies of two different particles, the DEP force applied to the particles is in opposite directions, and can separate the particles 135. 17 Although both direct (DC) and alternative current (AC) can generate DEP, their effect on particles is not the same. In the case of DC DEP, insulators are used to make the non-uniform electric field, and hence it is called insulator-based DEP or iDEP. In this case, the electric field can be applied by external electrodes. The external electrodes can be fabricated by injection molding or embossing. As a result, the sputtering process can be avoided 136. Hence, the fabrication process of iDEP devices is easier and more cost-effective as compared to the AC-based DEP 137. Also, the chance of electrolysis can be reduced significantly as the electrodes are not in contact with the sample in iDEP devices 136. On the other hand, contactless electrode means that the electrodes should be on different sides of the channel (i.e.. a larger distance between the electrodes) which results in a smaller magnitude of the electric field and consequently the DEP force. Thus, higher voltages (and also higher excessive heat) should be applied in the case of iDEP to compensate for this large gap between the electrodes. On the other hand, smaller voltages required for the AC DEP makes it possible to use batteries for particle separation 138. Such phenomena and their applications have been reviewed in multiple studies 139-144. Manipulation of particles by DEP has been performed in a continuous or batch form 140 or even on a digital microfluidic chip 145-147. As a simple example of batch processes (which is used mostly in the earlier DEP devices), one type of particle is trapped by the positive DEP on the electrodes (which is a high-intensity region of the electric field), while the other particles affected by nDEP are repelled from the electrodes. The particles influenced by nDEP can move freely. Hence, when flow passes through the chamber, these particles move with the flow and separate from the trapped ones. By turning off the applied voltage, the trapped particles are released and can be washed away by another flow. The low separation throughput of this type of method is the main reason that it is not widely used 135. Although the batch DEP is simpler to perform, the higher throughput of the continuous DEP devices has attracted the interests of the researchers 148-150. Such devices usually use angled electrodes to push cells or particles to a target outlet. For continuous separation, more parameters (such as the flow rate, voltage, frequency, and geometry) need to be adjusted in a way to assure that each type of particles will be guided to its 18 specific outlet. As a result, the continuous process can be more complex than the batch type explained above. While pDEP is usually used for a batch process, it is also used for continuous separation. For example, human mesenchymal stem cells and their differentiation progenies (osteoblasts) have been sorted using the design shown in Figure 1-8 151. In this study, a buffer pushes the cells to one of the channel’s wall. The frequency is set in a way that the osteoblasts experience a pDEP and they move along the oblique electrodes. By turning off the voltage, the cells are released and move along with the flow. Consequently, the osteoblasts follow a zig-zag trajectory towards their outlet. Figure 1-8 A design used for the continuous sorting of stem cells and osteoblasts. (Reproduced from 151 with permission from The Royal Society of Chemistry, copyright 2015.) Some researchers have used nDEP and pDEP simultaneously 152, 153. Figure 1-9 shows an example of one of the first continuous DEP devices which uses both pDEP and nDEP. This device is used to separate viable and non-viable yeast cells with a flow rate of up to 1 𝜇L/min. The nonviable yeast cells, which are affected by the nDEP, are focused on the central line, while those affected by the pDEP are pushed to the sidewalls 152. Later, Das et al. 148 used the same configuration to manipulate and trap polystyrene particles as well as Hela cells. They also simulated this design and verified the model by performing experiments. This model was used to find an optimized design with effective manipulation of the cells without electroporation. 19 Figure 1-9 A design used by Doh and Cho for a continuous DEP-based cell separation (Reproduced from 152 with permission of Elsevier). DEP has also been used in field-flow fractionation 154 and implemented for both batch and continuous separation 155-169. In these devices, the poiseuille flow (with the parabolic velocity profile) makes the particles at different distances from the wall to move with different velocities. Hence, particles can be separated based on the time they reach the end of the separation chamber. To make use of this phenomenon, nDEP can be used to repel the particles from the electrodes, applying the force in the opposite direction of gravity. Figure 1-10 shows the effects of the DEP and buoyancy forces on dissimilar particles. The DEP and buoyancy forces applied to the particles vary with the density, size, and polarizability of the particles. As a result, the particles end up in different vertical positions inside the channel, flowing with different velocities and consequently reaching the end of the channel at different times. Vykoukal et al. 163 used dielectrophoretic field-flow fractionation (depFFF) to enrich putative stem cells from adipose tissue in a batch process. For each run, 106 cells were entered the separation channel. After 10 minutes of settling in the separation channel, the mixture flew with a flow rate of 1500 𝜇L/min while the DEP force was applied to the cells. DepFFF is also used for other biological purposes such as separation of platelets from the rest of blood cells 168, differential analysis of human leukocytes 161, separation of cancer cells 170, and CTCs 165, 171. 20 Figure 1-10 The interaction of hydrodynamic and DEP forces on particles positioning them in the different flow-velocity lamina. (Reproduced from 163 with permission of The Royal Society of Chemistry, copyright 2008) DEP devices can also be classified according to other properties such as electrode configuration, operating strategy, and applications 143. The classification based on electrodes includes parallel or interdigitated 172-175, castellated 138, oblique 176, 177, curved 178, 179, quadrupole 180-182, microwell 183, 184, matrix 185, extruded 186, top-bottom patterned 187-189, sidewall patterned 190, insulator-based or electrodeless 191-197, contactless 198-201 and bipolar electrodes 197. In most cases, the electrodes are fabricated in a planar form reducing the throughput of the device. In a planar DEP, there is also a limited range of heights at which the electric field interaction is strong enough to manipulate particles. As a result, the channel’s height should be small, which leads to a low throughput. To overcome this issue, some researchers go through the hardships of fabricating 3D DEP devices, which allow higher channel heights and higher flow rates 140, 150. For example, Yunus et al. 202 separated colloidal particles in a continuous mode using an nDEP device (see Figure 1-11). Their device is formed of an array of electrodes on the bottom which is aligned with an array of electrodes on the top of the channel. Since the particles are repelled by the nDEP both from the top and bottom of the channel, they are focused in the middle of the channel. As a result, they move faster and the throughput increases. Recently, 3D carbon electrodes have attracted attention 136, 203-206. The low cost, ease of fabrication, electrochemical stability (compared to gold and platinum), the less chance of electrolyzing, along with the excellent biocompatibility are among the advantages of 3D carbon electrodes 136. In a recent study, Yildizhan et 21 al. 204 used 3D carbon electrodes to separate live and dead human U937 monocyte cells. They successfully removed the dead cells while preventing the lysis of live cells. Figure 1-11 Schematic of a 3D DEP based device used for the continuous separation of colloidal particles (Reproduced from 202 with permission from John Wiley and Sons, copyright 2013.) 1.1.7 Magnetophoresis Among the forces used in the active cell separation methods, the magnetic force is inherently similar to the electric force. However, it has several advantages over the electric force: 1) the magnet is not in contact with the flow; 2) usually the magnetic force does not affect the viability of the cells; and 3) the magnetic force is not affected by the surface charges, ionic strength or pH of the medium 207. This method is specific and fast 73. However, the challenge is that most cells and particles inherently cannot be manipulated by magnetic forces 207. As a result, for most of the cases, the first step for the separation involves labeling the cells (or particles) with magnetic beads either integrated with the separation process on the same chip 208 or as a preparation step 209. The magnetic force can be used to separate particles in both batch 70, 210-215, and continuous 216-221 modes. In a batch process, first, a magnet attached to the chamber that contains the sample particles is turned on. As a result, the labeled particles are trapped in the chamber due to the 22 magnetic field. Then, the flow passes through the chamber and washes all the other particles. Finally, by turning off the magnet, the target particles can be released and collected 214. Magnetically actuated cell sorting (MACS), which has been commercialized, is of this type. Although the cost of the batch mode is relatively low, it is time-consuming and it may lose some of the target cells 222. The magnetic separation of the particles can also be performed in a continuous flow mode. In the continuous mode, particles can be separated based on the magnitude of the force applied to them. The magnetic force that is applied to the particles can be calculated as 223: 𝐹 =𝛥𝜒. 𝑉𝑝𝜇0. (∇. 𝐵). 𝐵 (1.10) where 𝛥𝜒 is the difference in magnetic susceptibility of the particle and the medium, 𝑉𝑝 is the volume of the particle, B is the magnetic flux density and 𝜇0 is the permittivity of the air. Based on this equation, the size of the particles and their magnetic susceptibility are the two factors that can distinguish particles in a magnetic field 224. The susceptibility of the particles can be changed by labeling them with magnetic beads. Pamme and Manz 224 developed a new method, referred to as the on-chip free-flow magnetophoresis, to separate more than two particles simultaneously. Figure 1-12 shows the configuration and operation of their method. First, the sample enters the channel while being pushed to the wall by a sheath flow. A magnetic field, which is orthogonal to the flow direction, affects the magnetic particles. As a result, the nonmagnetic particles move straight to the outlet while the magnetic particles move laterally according to their size and magnetic susceptibility. 23 Figure 1-12 On-chip free flow magnetophoresis. This chip is capable of separating three types of cells based on their magnetic susceptibility and size. Nonmagnetic particles are not affected by the magnetic field and the larger magnetic particles experience a larger magnetic force than the smaller particles. This difference leads to a different lateral position of the particles and makes them separable. (Reprinted with permission from 224, Copyright 2004, American Chemical Society.) As discussed earlier, the magnetic susceptibility difference between the particles and the medium is one of the parameters that affect the magnetic force applied to the particle. In the cases that the medium is paramagnetic (𝜒𝑚 > 0), the diamagnetic particles (𝜒𝑝 < 0) can be separated by applying a magnetic field 218, 225. The nonmagnetic particles in a magnetic solution are pushed towards the weaker magnetic field which is referred to as negative magnetophoresis 226. Tarn et al. 225 separated 5 and 10 𝜇m diamagnetic particles in a paramagnetic solution. They discovered that the particles are repelled in different lateral velocities, depending on the size and the diamagnetic susceptibility. A combination of negative and positive magnetophoresis is used by Zhu et al. 227 to separate not only magnetic and non-magnetic particles, but also particles with different magnetization. The continuous separation of magnetic particles is not limited to on-chip free flow magnetophoresis. Several methods have been developed for the continuous separation of magnetic particles 220, 228-234. Adams et al. 228 developed a method referred to as multitarget magnetic-activated cell sorter (MT-MACS). In this method, ferromagnetic strips (microfabricated on the channel substrate) manipulate the particles. The combined effects of the magnetic and hydrodynamic forces must 24 accurately be adjusted such that each type of particle is directed to a specific outlet. This method is proven to be capable of separating multiple magnetic particles with more than 90% purity with a throughput of 109 cells per hour. To manipulate the cells using magnetic force, the magnetic susceptibility of most of the cells must be changed by labeling them with paramagnetic particles; however, several studies have shown the use of label-free methods to separate certain particles using magnetic forces 220, 232, 235-239. As an example, label-free separation of red and WBCs from whole blood has been demonstrated using magnetic forces in 232, 235, 237, 238, 240. For instance, Han and Fraizer 240 designed a magnetophoretic microseparator, which can be seen in Figure 1-13, for separating RBCs and WBCs from whole blood using their inherent magnetic properties. Figure 1-13 A magnetophoretic-based device for the separation of blood cells. By applying the magnetic field, RBCs are repelled from the ferromagnetic wire while the WBCs are drawn closer. (Reprinted from 240, with the permission of AIP Publishing, copyright 2004.) 1.1.8 Optical The manipulation of cells and particles using optical force has gained significant attention since 1986 when Ashkin et al. 241 used an optical tweezer for trapping dielectric particles. The main advantage of this method is that even though the applied force is strong enough to manipulate the cells, it does not have harmful effects on them 242. Particles in the range of nanometre to micrometer diameters have been manipulated using the optical force 243. This force 25 can separate particles according to their size, shape, refractive index, and polarizability 207, 222, 244. The changes in the light absorbance or beam direction due to the presence of an object illustrate the collision between the object and the photons. Some or all of the momentum of the photons can be transferred to the object depending on the type of collision 245. Hence, light can apply forces to the particles, which can be used as a method for particle manipulation. The most popular method that works based on optical forces is the single beam optical traps, which is known as optical tweezer 246. The optical tweezers use a normal Gaussian laser beam to manipulate particles. When a light beam is exposed to a particle, two types of forces affect the particle: (1) a scattering force along the direction of the beam, and (2) a gradient force which is perpendicular to the direction of the beam. The latter pushes the particles towards the high-intensity region of the beam 246-248. The manipulation of cells and particles using optical force has gained significant attention since 1986 when Ashkin et al. 241 used an optical tweezer for trapping dielectric particles. The main advantage of this method is that even though the applied force is strong enough to manipulate the cells, it does not have harmful effects on them 242. Particles in the range of nanometre to micrometer diameters have been manipulated using the optical force 243. This force can separate particles according to their size, shape, refractive index, and polarizability 207, 222, 244. The changes in the light absorbance or beam direction due to the presence of an object illustrate the collision between the object and the photons. Some or all of the momentum of the photons can be transferred to the object depending on the type of collision 245. Hence, light can apply forces to the particles, which can be used as a method for particle manipulation. The most popular method that works based on optical forces is the single beam optical traps, which is known as optical tweezer 246. The optical tweezers use a normal Gaussian laser beam to manipulate particles. When a light beam is exposed to a particle, two types of forces affect the particle: (1) a scattering force along the direction of the beam, and (2) a gradient force which is 26 perpendicular to the direction of the beam. The latter pushes the particles towards the high-intensity region of the beam 246-248. In some studies, the target particles are first recognized by image processing based on different properties such as size 249, 250, or fluorescence radiation 242, 249, 251. For this purpose, a sheath flow is first used to align all the cells or particles for analysis. After the target particle is recognized, the optical tweezer is used to manipulate it to the target outlet. Figure 1-14 represents an example of such devices working based on this concept. This device has been used for sorting mammalian cells 252. Enger et al. 253 also used this method to separate yeast cells from E. coli bacteria, which were marked with fluorescent markers. Figure 1-14 Sheath flow is used to focus the cell in the middle of the channel. As a result, the cells can be recognized and if needed pushed to the collection outlet using optical forces. (with the permission of AIP Publishing 252, copyright 2004.) Wang et al. 249 used image processing to recognize the target cells according to multiple features such as the size of the cells and their fluorescent label. After recognizing the target cells, the optical tweezers were used to move them to the target outlet and separate them from the rest of the solution. Using several optical tweezers, they eliminated the need for single-cell flow focusing, which resulted in a much higher throughput of the system. The schematic of their chip 27 is shown in Figure 1-15. Landenberger et al. 242 used the same concept to separate yeast cells based on their size and fluorescent label. Their device can separate yeast cells with a recovery rate of 95% with a throughput of 1.4 cells per second. Despite the high viability, recovery, and purity rate of the optical tweezer devices, this method is not suitable for the separation of large populations of particles. Figure 1-15 Schematic of an optical tweezer. First, the particles are recognized by image processing. Then, the target particles are moved toward the target outlet to be collected. (Reproduced from 249 with permission of The Royal Society of Chemistry, copyright 2011.) 1.1.9 Hybrid systems So far, different active and passive methods for cell separation on a microchip have been reviewed. Active methods have more control over the separation process, can be tuned in real-time 254, and are more reliable. On the other hand, passive methods can handle higher flow rates, easy to be parallelized, and do not depend on any external forces. However, their operational range is limited due to their fixed geometry and design and they are limited to size-based separation. Hence, there is a growing trend in the use of hybrid systems to take advantage of both methods, achieving high throughputs (without affecting cell viability), efficiency, sensitivity, and capability of multiparameter separation (e.g., based on both size and electrical properties of the cells). 28 Magnetic-based methods have been combined with different passive 228, 255-261, and active methods 262, 263. In the majority of magnetophoresis techniques, the particles need to be focused prior to the separation. While sheath flows are mostly used for focusing 264-266, some studies have focused the particles using a passive method 58, 258-260, 267, 268. Several groups (e.g., Del Giudice 258, Kim et al. 259, and Zhang et al. 267) have exploited elasto-inertial particle focusing in a straight channel to align the particles on the centreline of the channel. Besides the passive methods, some active methods such as DEP 263 and acoustic 262 have been combined with magnetic force. Kim and Soh 263 made an integrated dielectrophoretic–magnetically activated cell sorter to achieve an efficient multi-target separation for multiple bacterial targets and to avoid cross-contamination among different outputs. This device, shown in Figure 1-16, can enrich multiple bacterial target cells up to 900 fold with more than 95% purity. Figure 1-16 (A) Schematic of a DEP-Magnetic Activated Cell Sorter. At first, the target cells are tagged with DEP or magnetic beads. Then they enter the channel and separate from each other. Finally, they will be labelled with fluorescent beads and countered by a cytometer. (B) The cells that are labelled with DEP tags are affected by the DEP force and move to outlet A. The rest of the cells move straight to the magnetic separation module, where the magnetic tags are captured and separated from the non-target cells. (Reproduced from 263 with permission of The Royal Society of Chemistry, copyright 2009.) 29 The combination of DEP with magnetic 263, acoustic 269-271, and optical 272 methods has been studied by several groups. DEP can manipulate particles with relative ease and acoustophoresis can handle higher throughputs, is less affected by the medium 271, and is a long-range force (in contrast to DEP) 269. In addition, DEP has also been combined with passive methods. Among the passive methods, DLD can result in fast and very high-resolution separation; but similar to the other passive methods it is not tunable. Hence, some groups have combined DLD and DEP to achieve a tunable separation with high resolution 96, 273. Chang and Cho 273 even eliminated the clogging issue of DLD devices by replacing the mechanical pillars with DEP virtual pillars. Different parameters including the applied electrical voltage, flow rate, particles’ diameter, and dielectric properties can be adjusted to achieve the separation of particles with different sizes. Hydrophoresis is another technique that has been used in combination with DEP 96, 254, 274, 275. In this method, the steric interaction between particles and grooves is used for applications such as cell separation and focusing 276, 277. Even though this method is powerful and robust, it has some limitations. For instance, experimental studies have shown that this method cannot manipulate particles that are smaller than half of the channel’s height 84. On one hand, the channel’s height must be larger than the large particles to avoid clogging, on the other hand, it has to be smaller than twice the diameter of the small particles so they can be affected by the hydrophoretic force. Therefore, the range of the size of the particles that can be separated using this technique is limited. Also, the restriction made by this method on the channel’s height affects the throughput. To avoid these problems, this method has been combined with DEP 96, 254, 274, 275. Yan et al. 275 have developed a chip that works based on the combination of DEP and hydrophoresis. In this device, nDEP pushes the particles toward the top wall, where the particles’ interaction with the grooves is effective enough to sort the particles. This device, which is tunable and can change the lateral position of the particles by changing the applied voltage, has been used to separate the blood cells from a whole blood sample with a purity of 94.2% and a yield of 16.5% at a flow rate of 10 𝜇L/min. A combination of DEP and multi-orifice flow fractionation (MOFF) has been used for the separation of blood samples 278. MOFF offers high throughput; however, it is size-dependent and cannot 30 function well for separating different types of cells with a similar size (e.g., separation of CTCs from WBCs). However, the combination of MOFF and DEP solves this problem. For instance, Moon et al. 278 initially separated CTCs and other similar size blood cells from the rest of the blood cells using MOFF (see the device shown in Figure 1-17). This way they enriched the CTCs up to 162-fold at a 126 𝜇L/min flow rate. After this stage, DEP is used for precise separation of the CTCs from other cells with a similar size to increase the separation efficiency. For instance, the separation efficiency of this device for WBCs and RBCs are 94.23% and 99.24%, respectively. Zhang et al. 279 experimentally and numerically investigated a DEP-inertial combined microfluidic device. Their device includes a serpentine PDMS channel on the top of a glass slide, which is patterned with interdigitated electrodes. In the serpentine channel, the vertical position of the particles can be adjusted by nDEP force in real-time with a 3D focusing pattern. Figure 1-17 A combination of MOFF and DEP, which is used for the separation of breast cancer cells (MCF-7) from blood samples. The first separation region separates larger cells including MCF-7 from the rest. Then, the target cells enter the separation chamber, where the MCF-7 is separated by their dielectric properties. (Reproduced from 278 with permission of The Royal Society of Chemistry, copyright 2011.) The optical force has shown to be capable of increasing the separation resolution of particles when it is combined with pinched flow fractionation (PFF). For example, it is shown that the combination of PFF and optical forces can enhance the lateral distance of the separated particles by a factor of 15 280. In 31 essence, the optical force manipulates particles in the pinched segment, in such a way that the particles are pushed away from the wall of the channel. Since the center of mass of small particles are already close to the wall and hence affected less by the optical force, the lateral distance between the particles with different sizes will be larger in the pinched segment than the conventional PFF systems. This distance will be further amplified in the broadened segment, resulting in higher efficiency of separation. 1.2 Commercialization of LOC cell sorters Well-established technologies such as fluorescence-activated cell sorting (FACS) and magnetic-activated cell sorting (MACS) are still the routine clinical cell sorting methods. Despite the advantages of FACS and MACS, i.e., high throughput and high separation efficiency, these methods require expensive and large equipment. As a result, a handful of companies are working on the commercialization of microfluidics-based cell sorting devices to address this market gap. Shields et al. 281 have reviewed some of these products. In addition to those listed in 281, a number of commercialized products are reported in Table 1-1. These microfluidics-based products are widely used for CTC isolation. The ApoStream® operates based on positive dielectrophoresis to enrich CTCs, in a label free process with a high recovery and purity rate. The vortex technology has been implemented by Vortex BIOSCIENCES to isolate CTCs from the whole blood sample with 800 𝜇L/min throughput. Microfiltration is the other method, which is used for the isolation of CTCs by Cellsievo and VyCAP. Microfluidics offers advantages such as low sample and reagent volume, low fabrication cost, low energy consumption, excellent control over the cells, and high separation efficiency. However, there are still barriers on the road to commercialization of these systems. Shields et al. 281 have categorized these barriers into device-related and commercialization-related challenges. The device-related issues include the need for bulky components (such as syringe pumps or microscope stations), the complexity of operating such components for the user, difficulty in fabrication of some components in bulk (e.g. PDMS), and the short lifespan of these devices (compared to macro-scale devices) due to bubbles, reagent/ buffer residues, and cell debris aggregation. The commercialization barriers include saturation 32 of microfluidic-related patents, the slow transition of the market from benchtop to commercialization, and limited investment opportunities. Table 1-1 A list of a few commercialized microfluidics-based cell sorters with their mechanisms, applications, and throughput Product Mechanism Application Throughput ApoStream Technology Platform Dielectrophoretic field-flow fractionation Isolation rare cells including CTCs, stem cells, progenitor cells, and differentiated immune cells (such as CAR-T cells, activated T cells, and immune cell populations for immuno-oncology applications) 15 mL/hr Vortex Bioscience Vortex Isolation of CTCs More than 8mL/hr for 10x diluted blood and 800 μL/min for whole blood 282 Cellsievo Filtration (Microsieve) Capturing CTCs NA VyCAP Filtration Rare cells enumeration and isolation The recovery rate of up to 50% for single-cell isolation 1.3 Motivation Since the advent of microfabrication techniques, lab-on-a-chip devices have advanced the field of cell separation and rare cell isolation. The main challenges associated with the current conventional macroscale methods have been overcome in the microscale due to the laminar flow conditions, simplicity, and low fabrication cost. Such advantages have led the microfluidic devices to benefit from high controllability over the cells and particles, thereby a higher separation efficiency. These devices also enhance the viability of the separated cells due to the lower shear stresses applied to the cells during the separation process. Besides, the microfluidic approach consumes lower sample and reagent volumes, and hence there is a lower risk of contamination. Despite the promising advantages provided by the microfluidic devices, more efforts are required to improve further cell viability, recovery and purity rates, and throughput of such devices. Amongst the existing sorting and manipulation techniques, DEP stands out as a label-free method, making the manipulation process faster and more cost-effective (as the preliminary steps) compared to 33 most traditional methods (such as fluorescent-activated cell sorter (FACS)283 or magnetic-activated cell sorter (MACS)284). Another advantage of DEP over other label-free techniques, such as acoustophoresis, is its relative ease of fabrication of DEP-based devices. Besides, DEP functions based on the application of an electric field, which is part of many other functions in microfluidics chips (e.g., in digital microfluidics the electric field is used for droplet manipulation 145 or capacitive biosensors 285). DEP is capable of sorting particles based on not only their size but also their electrical properties which makes it the method of choice for many cell sorting applications 286. DEP can be applied in different forms such as batch mode, continuous mode, or droplet-based mode 145-147, 287. Among these forms, continuous manipulation leads to higher throughput (despite the complexity in the design and sample handling). Continuous manipulation has been operated in different forms such as electrode-based 288 or insulator-based 289. The pros and cons of each of these forms have been discussed in literature. Among the electrode-based, the slanted form (dating back to 1998) has attracted attention because of its effectiveness, relative ease of fabrication and operation. The working principle of these slanted electrodes involves the generation of a non-uniform electric field, which locally polarizes non-charged particles, thereby they experience a lateral DEP force. As a result, the particles are pushed laterally (depending on their size) along the width of the microchannel. Slanted electrodes can be in the form of bottom-top electrodes 290 or coplanar electrodes 288. Numerical studies have modeled and simulated the bottom-top electrodes and investigated the effects of different geometrical and operational factors 291-293. While it has been shown that bottom-top electrodes can create a strong DEP force, their fabrication process is rather difficult. For instance, aligning the top and bottom electrodes and perfectly sealing the channel present significant barriers in terms of mass fabrication. In contrast, the planar electrodes have attracted considerable attention due to their ease of fabrication 151, 263, 278, 288, 294-296 while having the potential for effective cell/particle manipulation if well designed and fabricated. There are a number of geometrical parameters of the device (such as the length, width, and height of the DEP channel, the width of the electrodes, the gap between the electrodes, and the position of the outlets) and operational parameters (such as the volumetric throughput, concentration of the 34 cells/particles, the media, the applied voltage and its frequency) that affect the DEP-based manipulation in these systems. While some of these aspects are already investigated by several researchers 297-299, there is a lack of holistic study of the multiparametric effects of potential input factors in planar electrodes type of lateral DEP microseparators. 1.4 Objectives This thesis aims at the development of a LOC microparticle manipulator, which works based on the DEP force caused by slanted planar electrodes, that can effectively and accurately focus and/or separate micron-sized particles. To achieve this goal, a series of studies are conducted to find the effect of different geometrical and operational parameters on the induced DEP force and the resulting displacement of particles and throughput of the device. First, the effect of several parameters on sheath-assisted focusing of microparticles in Y-junctions is numerically studied and validated with experimental results. The results show that the focusing width is greatly impacted by the sheath flow rate ratio. Further, the standard deviation of the position of the particles, which represents the concentration of the particles, is shown to be mostly dependent on the flow rate ratio, width ratio, and particle size. COMSOL Multiphysics modeling is used to investigate the effect of the electrode width and electrode spacing on the induced DEP force between the planar electrodes. The study is carried for channels with different heights. Based on the results of this study, the criteria for the selection of the electrodes to achieve the highest DEP force are reported. By implementing the suggested electrode width and the electrode spacing, channels at different heights with different electrode angles are fabricated. The effects of the channel height and electrode angle, as well as the voltage and particle size on the effective flow rate of the device, are studied by carrying out a series of experiments. At the effective flow rate, the particle size of the study is effectively guided to the desired target outlet. It is shown that for small channel heights, the DEP force affects the particles more strongly and the effective flow rate is higher (compared to the large heights). Besides, a 35 linear relationship between voltage squared and the effective flow rate is observed, which indicates that the 𝐶𝑀 factor is linearly related to the effective flow rate. To further characterize the effect of the parameters, another study is conducted on the channel length, channel width, electrode angle, volumetric throughput, and particle size. In order to quantify the displacement of the particles, a video processing technique is implemented and more than 18,000 particles are studied. The results of the ANOVA test show that the channel length, electrode angle, particle size, and volumetric throughput significantly affect the lateral displacement of the particles. The effect of each as well as the interactive effect between different parameters are thoroughly discussed. NIH 3T3 mouse fibroblast cells are tested to show the validity of the results for the manipulation of biological samples. Finally, the proposed device is used for focusing and separation of microparticles. Based on the findings of the previous sections three sheath-assisted, sheathless, and weak sheath-assisted DEP microseparators are fabricated and used for ternary separation of microparticles. The performance of each design in terms of their separation efficiency and throughput is investigated and compared with each other. 1.5 Thesis organization The thesis is comprised of the following 9 chapters: Chapter 1: Different active and passive methods that are implemented for on-chip manipulation of cells and particles are reviewed. The advantage and disadvantages are discussed, and the motivation and objective of the current thesis are explained. Chapter 2: The theory behind the proposed device, which includes the DEP force as well as the hydrodynamic forces on the particles are discussed. Chapter 3: The main fabrication methods and procedures implemented in this thesis are presented in this chapter. 36 Chapter 4: The nonuniform electric field that forms between planar electrodes is modeled by COMSOL Multiphysics. The effect of the electrode width and the gap between the electrodes on the induced DEP force is discussed in the chapter. A guideline is provided to maximize the DEP force without increasing the voltage. Chapter 5: The common Y-junction configuration for sheath-assisted focusing is parametrically studied in this chapter. First, a computational model is developed and validated by comparing it with our experimental data. Then, the numerical data is used to develop two generalized linear models to predict the focusing width of the particles and the standard deviation of the position of the particles. Chapter 6: With the consideration of experimental studies, this part studies the geometrical factors of a LOC platform consisting of tilted planar electrodes to achieve higher throughput in continuous manipulation of polystyrene particles. The chapter studies the effect of the channel height, voltage, and particle size on enhancing DEP manipulation. Additionally, the experimental results reveal the optimum value for the angle of the slanted electrodes with respect to the direction of the flow. Chapter 7: This chapter presents a parametric study on the lateral displacement of the cell-representative polystyrene particles in continuous dielectrophoresis (DEP) based microseparator. The effect of fabrication parameters (i.e., the channel length and width, and the deflection angle of the electrodes), as well as operational factors (i.e., the volumetric throughput and particle size) on the dielectrophoretic displacement, are studied. The effect of the microseparator on the displacement of NIH 3T3 mouse fibroblast cells is also investigated and compared with synthetic microparticles. Chapter 8: Based on the findings from the previous chapters, the device is fabricated for focusing and separation of microparticles in the range of 5 to 15 𝜇m. Several experiments are conducted to show and evaluate the performance of the device for two sizes and three size separation of microparticles. Chapter 9: Finally, the summary, contributions, and suggestions for future works are provided. 37 Chapter 2: Theoretical background2 2.1 Overview This chapter summarizes the theories behind DEP-based manipulation and sorting of microparticles in the micro-scale. These theories explain how non-charged particles can be manipulated by DEP force and how particle trajectories are related to the balance of hydrodynamic and DEP forces. 2.2 Theory of DEP Dielectrophoresis, also known as DEP, happens when a dielectric particle gets locally polarized as a result of a non-uniform electric field. This force can be derived by different approaches such as the Maxwell stress tensor or the dipole moment. The latter, although not necessarily the most accurate, is easier to implement. Considering the effective dipole moment and electric field, the DEP force can be written as: 𝐹𝐷𝐸𝑃 = (𝑝. ?⃑? )?⃗⃑? (2.1) where 𝑝 is the dipole moment vector and ?⃗⃑? is the electric field. This equation is based on the assumption that the electric field is moderately non-linear, which is true for common LOC devices. However, in cases that the electric field gradient is very strong (such as the edges of electrodes), the induced higher order multipolar moments should be included in the equation. For more information in this regard please refer to literature 300. Equation 2.1 can be simplified for dielectric spheres of radius 𝑅, assuming that the electric field is constant around their surface. In this case, the time average DEP force can be found by using Equation 2.2: ?⃗?𝐷𝐸𝑃 = 2𝜋𝜀𝑚𝑅3𝑅𝑒[𝑓(𝐶𝑀)](∇?⃗⃑?2), (2.2) 2 Parts of this chapter have been published in Scientific reports (A. Dalli, Scientific reports (2020), 10 (1), 1-11). 38 where 𝐶𝑀 =𝜀𝑝∗ − 𝜀𝑚∗𝜀𝑝∗ + 2𝜀𝑚∗ (2.3) in these equations, 𝑓(𝐶𝑀) is the abbreviation for the Clausius-Mossotti factor. 𝑅𝑒⁡[𝑓(𝐶𝑀)] is the real part of the 𝐶𝑀 factor from Equation 2.3. 𝜀 is the permittivity and is the complex permittivity and 𝑅 is the radius of the particles. The imaginary part of the CM factor leads to the traveling wave DEP (twDEP), which only happens with a phase shift between neighboring electrodes. Subscripts 𝑚 and 𝑝 represent medium and particles, respectively. The absolute and complex permittivities can be calculated by the following equations: 𝜀 = ⁡ 𝜀0𝜀𝑟 (2.4) 𝜀∗ = 𝜀 − 𝑖𝜎𝜔 (2.5) 𝜀0⁡ is the absolute permittivity of the free space, which is 8.854⁡ × ⁡10 − 12⁡𝐹/𝑚 , i is the imaginary component. 𝜀𝑟 and 𝜎 are the relative permittivity and conductivity of the substance respectively, and 𝜔 is the angular frequency of the AC field. According to Equation 2.2, DEP is a body force, which makes it a suitable method for the size-based sorting of microparticles and biological cells. Besides, permittivity and conductivity of the particle and media as well as the voltage and frequency of the applied electric field are the other factors affecting the DEP force. Besides, DEP is a non-linear force as it is related to ∇?⃗⃑?2. Due to the latter term, the geometry and configuration of the electrodes, which are generating the non-uniform electric field, play an important role in the DEP force. Based on the sign of the 𝐶𝑀 factor, there are two categories of DEP: negative dielectrophoresis (nDEP) and positive dielectrophoresis (pDEP). The pDEP happens when the particles are more polarizable than the medium, and hence the DEP force on the particle is towards the highest gradient of the electric field regions. On the other hand, the higher polarizability of the medium compared to the particle generates an nDEP effect, pushing the particles towards the lowest gradient of the electric field 39 regions. When 𝑓(𝐶𝑀) equals to zero, the particles are not affected anymore as the DEP force is zero. The frequency at which 𝑓(𝐶𝑀) = 0, where the complex permittivity of the particle and medium is the same, is called the cross-over frequency. The 𝐶𝑀 factor shows that the frequency of the electric field, as well as conductivity and permittivity of the particles and media, are the decisive factors to determine the sign of DEP force. Rewriting the 𝐶𝑀 factor by combining Equations 2.2 and 2.3 leads to: 𝐶𝑀 =(𝜀𝑝 − 𝜀𝑚) + 𝑖/𝜔(𝜎𝑝 − 𝜎𝑚)(𝜀𝑝 + 2𝜀𝑚) + 𝑖/𝜔(𝜎𝑝 + 2𝜎𝑚) (2.6) Considering Equation 2.6, at very low frequencies (𝜔 → 0) the conductivity of the particle and media is the decisive factor for the 𝐶𝑀 factor. On the other hand, at very high frequencies (𝜔 → ∞)⁡the permittivity of the particle and media defines the 𝐶𝑀 factor. Typically, the particle (or cell) and medium are based on the application. Hence, the frequency is a desirable parameter to tune the 𝐶𝑀 factor and consequently the DEP force. In the case of microparticles, an additional shell occurs at the interface of the particle and media. This interfacial layer changes the effective conductivity of the particle. Accordingly, the effective conductivity of microparticles can be found by the following equation301: 𝜎𝑒 = ⁡2⁡𝐾𝑠𝑅⁡+⁡𝜎𝑏 (2.7) The common approach to theoretically model the cell response to DEP is the use of the single spherical shell model 302. This model substitutes the two-layered cells (cytoplasm and membrane) with a homogenous sphere. According to this model, the complex permittivity for the particles, 𝜀𝑝∗ , is replaced by a homogenous complex permittivity value 𝜀𝑝′ which can be found from 303: 𝜀𝑝′ =𝜀𝑚𝑒𝑚∗ [(𝑅𝑅𝑐𝑦𝑡𝑜)3+2(𝜀𝑐𝑦𝑡𝑜∗ −𝜀𝑚𝑒𝑚∗𝜀𝑐𝑦𝑡𝑜∗ +2𝜀𝑚𝑒𝑚∗ )(𝑅𝑅𝑐𝑦𝑡𝑜)3−(𝜀𝑐𝑦𝑡𝑜∗ −𝜀𝑚𝑒𝑚∗𝜀𝑐𝑦𝑡𝑜∗ +2𝜀𝑚𝑒𝑚∗ )] (2.8) As an example, the CM factor of polystyrene particles (5, 10, 15, and 20 𝜇m), suspended in de-ionized water, is depicted in Figure 2-1. This figure shows how smaller particles are more affected by 40 the interfacial layer. The conductivity of the media and bulk conductivity of the polystyrene particles are 2.03 × 10−3⁡S/m⁡ and 10−15⁡S/m , respectively. The relative permittivity of the media and polystyrene particles is 76.4 and 2.4, respectively. The surface conductance of polystyrene particles is 10−9⁡S. The summary of these parameters is shown in Table 2.1. Table 2.1 Parameters used to find the 𝐶𝑀 factor The permittivity of the vacuum (C/Vm) The relative permittivity of polystyrene The relative permittivity of media Bulk conductivity of polystyrene (S/m) The conductivity of the media (S/m) 8.854×10-12 2.4 76.4 10-15 2.03 ×10-3 Figure 2-1 The CM factor for polystyrene particles as a function of the frequency of the applied electric field. The interface between the media and electrode surface may also experience the interfacial effects, as a result of the change of the charge carrier between the metals (electrons) and fluids (ions). 41 This phenomenon, which is called electrode polarization, causes an electric potential loss, local heating, and bubble formation around the electrodes leading to their dissolution. The electrode polarization can be avoided by keeping the frequency higher than 10 kHz and/or choosing a medium with conductivity higher than 100 mS/m 304. 2.3 Particle trajectories In order to study the trajectory of the particles in the lateral DEP separator, for the range of particle sizes examined in this research, drag, buoyant, and gravity forces should also be considered. Due to the low concentration of the particles, the interaction of the particles with each other is neglected. At low Reynolds numbers for a small sphere, which is the case in this study, Stokes law can be applied to find the drag force by using the following equation: ?⃗?𝑑𝑟𝑎𝑔 = 6 π⁡𝜇 R 𝑣𝑟 (2.9) where 𝜇 is the viscosity of the flow and ?⃗⃑?𝑟 is the flow velocity relative to the particles. The effect of gravity and buoyant forces, which are against each other, can also be found by the implementation of Equations 2.9 and 2.10, respectively: ?⃗?𝑔 = 𝑚𝑝?⃗? (2.10) ?⃗?𝑏𝑜𝑢𝑦𝑎𝑛𝑐𝑦 = −𝑚𝑝?⃗?𝜌𝑚𝜌𝑝 (2.11) where 𝑚𝑝 and 𝜌𝑝 are the mass and density of the particle, and 𝜌𝑚 is the density of the medium. Based on Newton’s second law of motion: ?⃗?𝑏𝑜𝑢𝑦𝑎𝑛𝑐𝑦 + ?⃗?𝑔 +⁡?⃗?𝑑𝑟𝑎𝑔 + ?⃗?𝐷𝐸𝑃 = 𝑚𝑝⁡?⃗? (2.12) where ?⃗? is the acceleration of the particle. In this study, the density of the solution is adjusted to the density of the polystyrene particles. Therefore, the buoyancy and gravity forces cancel each other out. Thus, the balance of DEP and drag forces determine the trajectory of the particles. 42 Chapter 3: Device fabrication 3.1 Overview The micro-fabrication techniques that have been used to fabricate the DEP-based separator are explained in this chapter. Also, the experimental setup, equipment, and experimental procedures are described. The DEP-based microseparator is made of gold electrodes patterned on a glass substrate, which is bonded to a polydimethylsiloxane (PDMS) channel. These parts were fabricated in a class 100 cleanroom, shown in Figure 3-1(a) by implementing photolithography techniques. Examples of fabricated chips are shown in Figure 3-1(b and c). Figure 3-1 (a) The cleanroom facility. (b)The SU8 mold is fabricated on a silicon wafer in the cleanroom. (c) A sample of the chips fabricated by photolithography. 43 3.2 Electrode deposition and patterning Gold was chosen as the conductive layer due to its excellent electrical conductivity and stability in the presence of ion charge carriers in liquid media. However, the adhesion between gold and glass is weak. Thus, typically a layer of chromium is deposited on the glass to increase the adhesion. For the fabrication of the DEP-based separator, a 25-nm layer of chromium followed by an 80-nm layer of gold was sputtered on 25 × 75 mm microscope slides (VWR VistaVision™ Microscope Slides) by a magnetron sputtering system (Angstrom Sciences Inc.). The electrodes were patterned following a standard photolithography technique: first, a spin coater was used to coat a layer of positive photoresist, S1805 (MicroChem Corp.) on the slide followed by one minute of baking at 115℃ on a hotplate. The substrate was then subjected to ultra-violet (UV) light for 8 seconds while the substrate was in direct contact with a photolithography mask. The mask was designed in AutoCAD and printed by CAD/Art Services, Inc. Examples of the photolithography masks are presented in Figure 3-2. Figure 3-2 Examples of photolithography masks for electrode patterning. 44 After this stage, the chip was immersed in a photoresist developer (MF-319 developer, MicroChem Corp.) to wash away the non-exposed photoresists. Next, gold and chromium etchants consequently removed the unwanted gold and chromium parts, and the electrodes were patterned. Then, the remaining photoresist was stripped off by a photoresist remover (MICROPOSIT® REMOVER1165). Finally, the chip was rinsed with DI water and blow-dried with clean air. 3.3 Channel fabrication SU8 structures were fabricated on silicon wafers in the cleanroom to act as the mold for PDMS-based microchannels. Based on the desired height of the microchannel, SU8 2025 (MicroChem) or SU8 2075 (MicroChem) was spin-coated on a 3-inch silicon wafer (WaferPro). For each height, the spin coating speed, baking times, and UV exposure times were chosen based on the guidelines provided by MicroChem. The wafer was soft-baked at 65℃ and 95℃ consequently after the spin coating of the photoresist. Next, the wafer was exposed to UV under a designed photolithography mask, where the light was selectively passed through the transparent region of the mask to crosslink SU8 with the intended pattern on the chip. Some examples of the photolithography masks for mold fabrication are shown in Figure 3-3. After this step, there was another round of baking at 65℃ and 95℃. Then, the wafer was immersed in a batch of SU8 developer, which was being agitated continuously. Finally, the wafer was washed with isopropanol and hard baked at 150 ℃ for 30 minutes to anneal any possible surface cracks. 45 Figure 3-3 Examples of the photolithography masks for the fabrication of SU8 molds. 3.4 Bonding PDMS to glass PDMS and its curing agent (SYLGARD™ 184 Silicone Elastomer, Dow Corning) were mixed well with a weight ratio of 10 to 1 and poured into the fabricated mold. After degassing the solution in a desiccator for around an hour, the PDMS was cured in an oven at 80℃ for about 3 hours. These processes are presented in Figure 3-4. Figure 3-4 (a) The fabricated mold was placed in a petri dish. (b) PDMS and its curing agent. (c) The PDMS and its curing agent were mixed well. (d) The mixed PDMS solution was poured into the mold. The petri dish was placed in a desiccator. (e) After an hour the solution was degassed in a desiccator. (f) The petri dish was placed in the oven for 3 hours. Once the PDMS was peeled off, the inlets and outlets were punched (Miltex® Biopsy Punch with Plunger, ID 1 mm) and the surface was cleaned with a 3M tape. The channel and electrode slide were 46 plasma treated (PE-50, PLASMA ETCH) for 30 seconds at 50 Watts power, and 5 cm3/min oxygen flow rate. The oxygen plasma-treated channel and substrate were bonded to each other using a handmade PDMS aligner 305. The chip was placed on a hotplate at 80℃ for 30 minutes to achieve a perfect bonding. See Figure 3-5 for a demonstration of the process. Figure 3-5 (a) The PDMS was cured. (b) The PDMS was peeled off by using a cutter. (c) The PDMS channel was cut and the inlets and outlets were punched. (d) The PDMS channel and glass substrate were plasma treated. (e) A close-up view of the chamber of the plasma machine. 47 Chapter 4: Sheath-assisted focusing of microparticles on lab-on-a-chip platforms3 4.1 Overview Advancements in microfluidic particle/cell manipulation and separation technology have enabled researchers to develop devices that interface with cells and microbeads at the microscale level for a wide range of applications in the pharmaceutical, environmental, health, and biotechnology industries. In the past two decades, various lab-on-a-chip (LOC) platforms have been developed based on microfluidic particle/cell separation, sorting, isolation, counting, and detection technologies 306. Focusing is a critical preliminary step for these technologies, in which randomly positioned particles entering the microchannel are focused by implementing different active or passive methods. The integration of the particle focusing with the downstream techniques such as sorting or sensing can be challenging as the key operational parameters such as the volumetric throughput of the device, concentration of cells/particles, focusing width, and the performance of the downstream application (e.g., the recovery rate in a cell sorting application) are all highly dependent on each other. Therefore, a better understanding of how the particle focusing is affected by the geometry of the device and operational parameters is an asset to enhance the performance of LOC devices. LOC-based particle focusing has been achieved by several active and passive methods 307. Active methods, requiring an external power source, include dielectrophoresis, acoustophoresis 308, and optical tweezers 309. Although these active methods provide high control over the cells and are tunable in real-time, their effectiveness and throughput are highly dependent on the size of the sample particles. Hence, the applications of active methods are limited to specific areas, where the operational requirements are met by them. On the other hand, passive methods do not rely on an external source and include methods 3 Parts of this chapter have been published in Electrophoresis (A. Dalli, Electrophoresis (2020)). 48 such as hydrophoretic-based 310, and inertia-based 311 methods. Hydrophoretic-based methods effectively focus particles by taking advantage of microstructure-induced pressure field to manipulate particles. The hydrophoretic methods are complex to design and fabricate, as well as susceptible to particle clogging. The inertia-based methods take advantage of the shear gradient lift force and wall lift force to manipulate the particles 312. Several parameters such as particle size, velocity, and channel geometry affect the position of the particles. By proper adjustment of these parameters, the particles can be focused in a narrow region 313. The application of inertia-based methods is limited to high throughput applications, as the inertia-based methods are effective only for high Reynolds (𝑅𝑒) numbers. More information on inertia-based focusing can be found in the literature 314, 315. Besides the aforementioned active and passive methods, sheath-assisted flow focusing is a common approach among the researchers. Adding sheath flow(s) to a device can adversely affect its portability. Besides, the dilution of the sample raises some difficulties for some applications, where the effectiveness of the device is restricted by its total volumetric throughput. On the other hand, compared to other focusing techniques, sheath flow focusing is simple, easy to operate, and effective for a wider range of 𝑅𝑒 numbers and particle seizes. Also, the throughput of the method and location of the particles is tunable by adjusting the flow rates. Based on these advantages, this technique has been widely used prior to sorting and sensing applications. Common sheath-assisted focusing techniques use single 316 or dual 317 sheath flow configurations to focus the particles in a limited region. The dual sheath flow configuration is commonly applied to focus the particles in the midchannel, while a single sheath flow is desirable for confining the particles near the channel wall. The single sheath flow configuration typically is used in the form of Y-junctions. The performance of these devices in terms of the mean position and focusing width of the particles depends on the geometry of the device and operational parameters. These geometrical parameters include the width of the channel, the ratio of the width of the sample inlet to that of the sheath flow, the height of the channel, and the junction angle. The operational parameters include the particle size, the total volumetric throughput of the device, and the sample over the sheath flow ratio. The focusing width 49 of the particles can greatly contribute to the performance of many cell sorting and separation devices in terms of their throughput, recovery, and purity rate at their outlets. For example, in flow cytometry applications, single-cell alignment is a necessity for accurate results 318. As a result, precise control over the effective parameters on the mean position and focusing width have been of interest in several studies. Theoretical 319 and numerical 320 models, as well as experimental studies, have been used to predict the average position and focusing width of the particles in flow-focusing devices. These devices include a middle inlet to infuse the sample into the main channel, and two side inlets, which serve as sheath flows. Knight et al. 321 developed a circuit model in which the fluid flow was mapped into a network of resistors. This model takes the pressure resistance of the channels into account to develop a symmetric dual sheath-assisted focusing junction. Based on this model they found a relationship for predicting the focusing width of the stream based on the pressure ratio of the side over the main inlet. Later, Stiles et al. 322 used circuit analysis for very thin channels where the Hele-Shaw limit (height/width → 0) applies. They reported a model for the prediction of the focusing width based on the ratio of the flow rates. Lee et al. 323 developed a model for channels with a high aspect ratio (height to width) to predict the focusing width based on the ratio of the side channel velocities to the inlet channel velocity. Their proposed model is valid for small ratios of the focusing width over the channel width. Later, another theoretical model was developed by Lee et al. 319, which had no limitation in terms of the channel’s height to width ratio. This model was capable of the prediction of the particle location as well as the focusing width for symmetric and asymmetric dual sheath-assisted focusing junctions. They studied the effects of the flow rates ratio, and the aspect ratio (height over width), which showed that the focusing width decreases by increasing the flow rate ratio and the aspect ratio. Contrary to the flow-focusing configurations, there are a limited number of studies on the modeling and parametric study of particle focusing in Y junction and T-junction configurations. Some studies have been conducted regarding the downstream effect of the focusing process but not on the focusing width specifically. For instance, the effect of the sheath flow ratio on the particle purity in a 50 spiral microfluidic channel for sorting applications was experimentally studied 324. This study demonstrated by properly adjusting the sheath flow ratio based on the size of the particles, microparticles can be sorted effectively due to the inertia forces with more than 99% purity in the outlets. Other studies have mainly focused on focusing application 325, 326. Nasir et al. 325 numerically and experimentally studied the effect of the junction angle and different 𝑅𝑒 numbers on symmetric and asymmetric Y junctions. They concluded that a smaller confluence angle and 𝑅𝑒 number will lead to a flatter interface between the focused stream and the sheath flow. In another study, Panwar et al. 326 used numerical simulations to investigate the effects of Re number and particle Reynolds number (Rep) on the sheath-assisted particle focusing for flow cytometry applications. They showed that sheath assisted focusing is not able to achieve single particle alignment for particles smaller than 10 𝜇m in Re numbers less than 1, where the viscous forces are dominant. For higher Re numbers, where both the inertial and viscous forces contribute, the single particle alignment is even possible for smaller particles. Despite the few mentioned studies, there has not been enough attention to the importance of the parametric study on Y-junctions for focusing purposes. In this chapter, we addressed this gap by examining the effect of several geometrical parameters, namely the junction angles, and width ratio (width of sheath inlet over the width of the channel), as well as hydrodynamic parameters, namely flow rate ratio (sheath flow over total flow rate) and particle size on the focusing width of the particles using COMSOL Multiphysics simulation. The simulation results were used to develop two generalized linear regression models to predict the focusing width and the standard deviation (STD) of the particles’ distribution, which was validated with our experimental results. It was shown that the focusing width mainly depends on the (i) flow rate ratio and (ii) the interactive effect of the particle size and volumetric throughput. Also, the flow rate ratio, width ratio, and particle size greatly affect the distribution of the particles within the focusing region. These results provide a guideline for the fabrication of effective and highly-throughput particle focusing LOC devices for downstream applications of sensing and sorting of both cells and particles. 51 4.2 Design The asymmetric Y junction design consists of two inlets, one for injecting particles and the other for the sheath flow, the main channel, and an outlet. A schematic of the device is shown in Figure 4-1. The effect of several factors, which are the width ratio [𝑊𝑟 = 𝑤𝑠/(𝑤𝑠 + 𝑤𝑝)], the junction angle of the particle inlet (𝜃𝑝), the junction angle of the sheath flow inlet (𝜃𝑠), the total flow rate of the device (Q), the flow rate ratio (𝑄𝑟) (i.e, sheath flow rate (𝑄𝑠) over the total flow rate), and particle size (D) on the focusing width of the particles, and their distribution within the channel width is investigated .In this study, the width and height of the microchannel are 500 𝜇m and 40 𝜇m, respectively. Figure 4-1 Schematic of the device. The simulation has been performed for the following range of parameters: Table 4-1 The range of the parameters that are studied in this research. Width ratio (𝑊𝑟) Sheath flow junction angle (𝜃𝑠 ) Particle flow junction angle (𝜃𝑝) Total flow rate (Q) (𝜇L/min) Flow rate ratio (𝑄𝑟 = 𝑄𝑟/𝑄) Particle diameter (𝜇m) 0.5 - 0.95 0° – 90° 0° – 90° 5 – 50 0.5 - 0.99 1 – 20 4.3 Numerical simulation 4.3.1 Governing equations The governing equations are those for the conservation of mass, Equation (4.1), and momentum, Equation (4.2): 52 𝜕𝜌𝜕𝑡+ ∇ ∙ (𝜌𝐔) = 0 (4.1) 𝜕𝜕𝑡(𝜌𝐔) + ∇. (𝜌𝐔𝐔) = −∇𝑃 + ∇ ∙ [𝜇(∇𝐔 + ∇𝐔𝑇] (4.2) where r, t, 𝐔, P, and m donate density, time, velocity vector, pressure, and dynamic viscosity, respectively. Due to the small length-scale of the simulations, the gravitational acceleration as a body force is neglected. The equations of laminar flow are coupled with the particle trajectory model. The model determines the trajectory of particles suspended in the Newtonian flow, as they move through the main channel, by solving the equation of motion for each set of particles. This equation is given by Newton’s second law as: 𝑑𝜕𝑡(𝑚𝑝𝐕) = 𝐅 (4.3) where mp and V are the particle mass and velocity, respectively, and F is the total force acted on the particles by the flow field. The force acting on particles suspended in a fluid consists of drag and lift forces. The drag force can be calculated employing Stokes’ law: FD = 6𝜋𝜇𝑟𝑝(𝐔 − 𝐕) (4.4) where rp is the particle radius. The lift force is given by Ho model 53: FL = 𝜌𝑟𝑝4𝑊2β(β𝐺1(𝑠) + 𝛾𝐺2(𝑠))𝐧 (4.5) β = |𝑊(𝐧 ∙ ∇)𝐮𝑝| (4.6) 𝛾 = |W22(𝐧 ∙ ∇)2𝐮𝑝| (4.7) 53 𝐮p = (𝐼 − (𝐧⁡⨂⁡𝐧))𝐮 (4.8) where 𝐼 (dimensionless) is the identity matrix, n (dimensionless) is the wall-normal at the nearest point on the reference wall, W (SI unit: m) is the distance between the channel walls, s is the normalized distance from the particle to the reference wall, divided by W so that 0 < 𝑠 < 1 for particles in the channel. Finally, G1 and G2 are dimensionless functions of the normalized wall distance, given by Ho and Leal 53, which are plotted in Figure 4-2. Figure 4-2 The G1(s) and G2(s) functions, which were used to define the wall-induced lift force. 4.3.2 Numerical procedure Two-dimensional, time-dependent, computational fluid dynamics simulations were developed to model the problem, utilizing COMSOL Multiphysics 5.5 software. The velocity field, u, is first computed using the Laminar Flow physics interface. The velocity field is then coupled to the Particle Tracing for Fluid Flow physics interface using the Drag Force node. The effect of the particle's motion on the surrounding flow is neglected. As a result, the model is limited to a low concentration of particles suspended in the fluid. Both sheath and particle flow inlets were specified using constant velocities. The 54 outlet boundary condition was determined as a pressure outlet (with ambient pressure) to suppress backflow. The channel length was considered 30 times larger than the channel width to eliminate the effects of the outlet boundary condition on the flow domain. All other boundaries were specified as walls with no-slip conditions and freeze behavior as the particles collide with them. A single particle was released through the particle inlet in constant time intervals of 0.01 seconds with a random initial position. The total runtime was 5 seconds, making the total number of particles within the domain 500. The average positions and focusing widths were computed by the analysis of particle trajectory data using an in-house. 4.3.3 Mesh study A mesh study of five different element sizes was conducted to examine the influence of lattice resolution on the parabolic velocity profile inside the main channel. Figure 4-3 demonstrates the results of the mesh study for the corresponding operation conditions of 𝜃𝑠 = 45°, 𝜃𝑝 = 45°, 𝑊𝑟 = 0.725, 𝑄𝑟 =0.745, and 𝐷 = 10 𝜇m. It can be noticed that element size does not have a remarkable influence on the velocity profile near the walls; however, a fine mesh is required for the central region. Therefore, considering computational cost and the minor difference between the two last meshes (36,873 versus 33,673 elements), a very fine mesh with total elements of 33,673 was considered for the subsequent numerical studies. 55 Figure 4-3 Velocity profiles in different numbers of elements. 4.4 Experimental procedure 4.4.1 Device fabrication A PDMS microchannel, 40 𝜇m deep, was fabricated to test the validity of the developed numerical simulation. First, a SU8 mold was fabricated in a cleanroom facility. An appropriate amount of SU8 2025 was spun coated on a silicon wafer at 2000 rpm. Following, the wafer was baked at 65℃ and 95℃ for 3 and 6 minutes, respectively. The SU8 was exposed to UV under a photolithography mask to pattern the microchannel. The wafer was once more baked at 65℃ and 95℃, for 1 and 6 minutes, respectively, before sitting in a batch of SU8 developer to remove the unwanted photoresist. Finally, the wafer was hard-baked at 150℃ to further cross-link the SU8 mold. An appropriate amount of PDMS and its curing agent were mixed (the weight ratio of 10 to 1) and degassed in a desiccator. The solution was poured on top of the mold and cured at around 80℃. Next, the PDMS channel and a glass substrate were plasma treated (30 seconds, 50 Watts power, 5 cm3/min O2 flow rate) and bonded to each other. Examples of the device can be found in the supplementary information, Figure 4-4. 56 Figure 4-4 The fabricated device. Food color is injected into the channel to enhance the visualization. 4.4.2 Experiments and data acquisition The sample flow consisted of synthetic microparticles, polystyrene-based mixed in a solution of DI water, and 0.1% (Volume/Volume) TWEEN 20. The surfactant (TWEEN 20) was added to prevent the particle from sticking to the surface of the channel. The sample and the sheath flow were injected into the channel by using syringe pumps. Particle trajectories were recorded using an inverted microscope, far enough from the inlets to make sure that the particles' positions stabilized (Figure 4-5(a)). A video processing software, developed by Basu et al. 327, was used to locate the position of the particles with respect to the channel wall. This software uses a video processing technique called Droplet Morphometry and Velocimetry (DMV). Polystyrene particles (5, 10, and 15 𝜇m) were tested separately using a variety of flow rate ratios. Close up images of the particles in a microchannel, for a flow rate ratio of 0.9, are exhibited in Figure 4-5(a). The particles were tracked for around 60 seconds for each case. After being analyzed by the video processing software, the distribution of the particles was obtained and is depicted in Figure 4-5(b). 57 Figure 4-5 (a) 5, 10, and 15𝜇m particles after being focused by a sheath flow (flow rate ratio=0.9). The 5 𝜇m particles are highlighted with red circles for better visualization. (b) The videos of the particles were recorded (each around 60 seconds) and analyzed by Droplet Morphometry and Velocimetry (DMV) software 327. The distribution of the particles is demonstrated in individual histograms. 4.4.3 Validation of the simulation The results of the simulation were validated by comparing them with the experimental results. The focusing width for three different sizes of particles (5, 10, and 15 𝜇m) and a range of flow rate ratios (𝑄𝑟 = 0.2, 0.4, 0.6, 0.8) for the corresponding operation conditions of 𝜃𝑠 = 45°, 𝜃𝑝 = 45°, 𝑊𝑟 = 0.725, and 𝑄 = 10⁡𝜇𝐿/𝑚𝑖𝑛 were obtained. Each experiment was repeated three times and the standard deviation of the results is shown using error bars. Figure 4-6 demonstrates that the results of the simulation are in agreement with the experimental results (with less than 4% difference). 58 Figure 4-6 The experimental and numerical results for the normalized focusing width (with respect to the channel width) of microparticles based on the size of the microparticles for several flow rate ratios. 4.5 Results and discussion The Re number in this study was in the range of 0.6 to 3, indicating that the flow regime was laminar, and the viscous forces were dominant over the inertial forces. The streamlines and particle trajectories around the junction region for a variety of conditions are presented in Figure 4-7. The results show that by increasing the junction angles, Figure 4-7 (a) and (b), there was a jump in the focusing width of the particles, which was quickly damped by the sheath flow. The graphs suggest that this effect was more significant by changing the angle of the sheath flow inlet compared to the particles inlet. The effect of the width ratio is presented in Figure 4-7 (c). Figure 4-7 (d) shows that by increasing the flow rate ratio, the particles were focused in a narrower region. Figure 4-7 (e) depicts the effect of the particle size on the streamlines and particle trajectories. While the streamlines were not affected by this factor, it can be observed that larger particles tended to keep a distance from the channel wall (which could be attributed to the induced shear wall stress). Comparing the streamlines and particle trajectories shows 59 that overall, they are more or less similar except for the cases in which the particles keep a distance from the channel wall (e.g Figure 4-7 (d), 𝑄𝑠=0.5 or Figure 4-7(e), D=20 𝜇m). Figure 4-7 Example results of the simulation of the fluid flow. Streamlines are presented for a variety of conditions. The numerical simulation results were used to run a sensitivity test on all the parameters that were involved in the simulation as well as their interactions. The Pareto chart, Figure 4-8, shows the standardized effects of these parameters on the focusing width of the particles. The graph suggests that the flow rate ratio and the interaction of the flow rate and particle size are the parameters that significantly contribute to the focusing width of the particles with a 95% confidence interval. The rest 60 of the parameters, including the junction angle for the sample flow and sheath flow, as well as the width ratio of the inlets, do not significantly affect the focusing width of the particles. Figure 4-8 The Pareto chart for the standardized effect of the studied parameters on the focusing width. The red line acts as a reference line for distinguishing significant from non-significant parameters. Using the flow rate ratio, total flow rate, and particle size, a generalized linear model was developed to predict the normalized (with respect to the channel width) focusing width of the particles in Y-junctions. This model, which can predict the focusing width with 𝑅2 = ⁡99.0%, is: 𝑤𝑓 =⁡ (0.75669⁡ − 0.70586 𝑄𝑟 ⁡+ 0.000059 𝐷𝑄)0.78942 (4.9) where D is in 𝜇m, and Q is in 𝜇L/min. The residual plots for 𝑤𝑓, Figure 4-9, shows the high accuracy of this model (𝑅2 = ⁡99.0%), the normal distribution of the residuals showing the validity of the model, and constant variance of the residuals. The random distribution of the residuals versus the fitted values and the residuals versus the order of the fits proves the constant variance of the residuals and verifies the independency of the residuals from each other. Further, the histogram of the residual demonstrates that the residuals are normally distributed and not affected by the outliers. 61 Figure 4-9 The residual plots for the regression model that is presented for the prediction of the focusing width. Figure 4-10 (a) and (b), present the effect of the flow rate ratio (𝑄𝑟) on the normalized focusing width of the particles for several particle sizes and the total volumetric throughput. As expected, by increasing 𝑄𝑟, the particles are more squeezed to the wall side of the channel and the focusing width decreases. For smaller particles, the results (see Figure 4-10 (a)) suggest that the volumetric throughput does not have a significant contribution to the focusing width. As an example, when 𝑄𝑟 = 0.8, for a 1 𝜇m particle, the predicted normalized focusing widths of the particles at flow rates of 5, 10, and 50 𝜇L/min, are 0.2721, 0.2724, and 0.2751, respectively, which demonstrate a negligible (1.1%) variation. The focusing width of the larger particles, for instance, particles with a 20 𝜇m diameter, is more sensitive to the total volumetric throughput of the device. For such particles by increasing the flow rate, the focusing width of the particles within the width of the microchannel increases. As an example, when 𝑄𝑟 = 0.8, for a 20 𝜇m particle, the predicted normalized focusing widths of the particles at flow rates of 5, 10, and 50 𝜇L/min, are 0.2784, 0.2849, and 0.3358, respectively. In other words, by changing the 62 flow rate from 5 𝜇L/min to 50 𝜇L/min, the focusing width of 20 𝜇m particles increased 20.6%. Figure 4-10 (b) exhibits that the particle size plays a more critical role in the focusing width at higher flow rates. While at low flow rates such as 5 𝜇L/min, the focusing width for all the particle sizes in the range of 5 𝜇m to 20 𝜇m, are focused in almost the same width, at higher flow rates as the particle size increases the focusing width also increases. Figure 4-10 (a) The normalized focusing width of the particles as a function of the flow rate ratio and flow rate. The results are demonstrated for four different sizes of particles (1, 5, 10, and 20 𝜇m). (b) The normalized focusing width of the particles as a function of the flow rate ratio and particle size. The results are demonstrated for four different flow rates (5, 10, 20, and 50 𝜇L/min). The distribution of the particles in the focusing width region is the other output that was investigated in this study. The standard deviation (STD) of the position of the particles is representative of the particles’ distribution along the channel width. The smaller STD values correspond to a more compact distribution of the particles along their mean position. The standardized effects for studied parameters are demonstrated in Figure 4-11, which shows that the flow rate ratio has the most 63 contribution to the STD. The width ratio, as well as the interaction effect between flow rate ratio and width ratio, the particle size, the interaction of the flow rate ratio with the angle of the particle inlet, the interaction of the flow rate ratio with particle size, and the interaction of the width ratio with the angle of the particle inlet, were the other parameters that have subsequent significant contributions to the lateral distribution of the particles. Figure 4-11 The Pareto chart for the standardized effect of the studied parameters on the standard deviation (STD) of lateral positions of the particles. The red line acts as a reference line for distinguishing significant from non-significant parameters. The interaction plot for the effective parameters is shown in Figure 4-12. On this graph, nonparallel lines are an indication of the mutual interaction of the factors to the response (e.g., the particle junction angle and width ratio) without showing the significance of the level of interaction (which is clearer in Figure 4-11). 64 Figure 4-12 The interaction plots of the factors that have significantly contributed to the STD of the position of the particles. By considering the effective parameters, a generalized linear model is developed for the prediction of the STD of the position of the focused particles. 𝑆𝑇𝐷 = 0.42 − 0.17𝑊𝑟 − 0.42𝑄𝑟 − 0.003𝐷 − 0.009𝜃𝑝⁡𝑊𝑟 + 0.006𝜃𝑝⁡𝑄𝑟+ 0.21𝑊𝑟⁡𝑄𝑟 + 0.002𝑄𝑟𝐷 (4.10) This model predicts STD with 𝑅2 = ⁡95.37%.⁡The residual plots, Figure 4-13, demonstrate the validity of this model by verifying the normal distribution of the residuals, their constant variance, and their independence from each other. 65 Figure 4-13 The residual plots for the generalized linear regression model that is developed for the prediction of the STD of the particle position. The model is used to demonstrate the effect of the factors by showing the contour plots in Figure 4-14. The general trend shows that as the flow rate ratio, width ratio, particle inlet angle, and size of the particles increase, the STD of the position of the particles decreases. The impact of the width ratio on the STD increases as the particle inlet angle increases. For instance, at 𝜃𝑃 = 0°, increasing the width ratio from 0.5 to 0.95 has a negligible effect on the STD. However, at 𝜃𝑃 = 90°, the same change in the width ratio leads to about a 50% reduction in the STD. Contrary, the effect of the flow rate ratio on the STD slightly decreases as the angle of the particle inlet increases. For instance, increasing the flow rate ratio from 0.5 to 0.99 at 𝜃𝑝 = 0° and 𝜃𝑝 = 90° leads to 85% and 81% increase in the STD, respectively. The results also show that the effect of the width ratio on the distribution of the particles is more for lower flow rate ratios. 66 Figure 4-14 The standard deviation of the lateral position of the particles as a function of the junction angle of the particle inlet, width ratio, flow rate ratio, and particle size are presented. Each graph investigates the effect of two factors while the other ones are held at constant values provided in the figure legend. Overall, among the studied parameters, the particle size cannot be controlled as it depends on the application. The flow rate ratio significantly affects the focusing width (and it should be as the tunable parameter for adjusting the focusing width), but not the distribution of the particles. The junction angle and width ratio, on the other hand, only affect the distribution of the particles within the focusing region. Hence, if a more concentrated distribution of the particles is desired, the inlet angle and the inlet width ratio should be adjusted accordingly. 4.6 Summary In this chapter, we used numerical simulation to predict the behavior of particles affected by a sheath flow in Y-junctions. After validating the simulation results with our experimental data, we developed generalized linear models to predict the focusing width of the particles as well as the standard deviation of their position along the width of microchannels. Our results demonstrated that the focusing width is mostly dependant on the flow rate ratio and, to some extent, on the total volumetric throughput 67 of the device and particle size. As the flow rate ratio increases and particle size decreases, the particles were focused in a narrower region. We also demonstrated that the particle concentration increases with the width ratio of the inlets and the junction angle of the particle’s inlet. These results provide an insight into how the device fabrication and the operational factors affect the position of the particles and can benefit future studies on the development of higher performance on-chip sensing and sorting of both cells and particles. 68 Chapter 5: Parametric study of electrode geometry4 5.1 Overview The geometry of the electrodes including the electrode width and the spacing between each one of the electrodes affects the resulting electric field. DEP is related to the gradient of the electric field squared. Thus, the geometry of the electrodes plays an important role in the induced DEP force, regardless of the other geometrical and operational parameters. Different configurations of electrodes such as interdigitated 328, castellated 138, top-bottom patterned 189, and sidewall patterned 190 have been used for DEP-based devices. Among these configurations, planar slanted electrodes are easy to fabricate and operate and are suitable for continuous cell separation. The slanted electrodes can continuously move the cells laterally toward the target outlet. To take advantage of the planar slanted electrode design in the DEP manipulation, the effect of the geometry of the electrodes on the DEP force has to be studied. Several simulation and numerical models have been developed to investigate the effect of the planar electrode’s width and spacing 329-332. The effect of electrode’s geometry on levitation of the particles 332, the magnitude of the electric field above the electrodes 329, and the variation of the electric field at the center and the edge of the electrode along the channel height 331 have been reported. In this chapter, slanted electrodes were modeled by COMSOL Multiphysics. The simulation model was used to find the optimum electrode width and spacing for achieving a maximum DEP force and hence effective cell separation. 5.2 Simulation of the electric field The numerical simulation was developed using a commercialized finite element analysis (FEA) software package (COMSOL Multiphysics, version 5.3a). Since the Z direction (along the 4 Parts of this chapter have been published in Scientific reports (A. Dalli, Scientific reports (2020), 10 (1), 1-11). 69 microchannel’s length) of DEP force was negligible compared to X and Y directions (along the width and height of the microchannel), only a cross-sectional view of the microchannel, shown in Figure 5-1, was simulated. Hence, the 2D computational domain was chosen which consisted of two rectangular electrodes, positioned at the bottom of the microchannel. The width of the microchannel and height of the electrodes were held constant, 750 𝜇m and 100 nm, respectively, while different values of height of the channel, the electrode width, and the electrode spacing were studied. Figure 5-1 Geometry of the simulation performed using COMSOL Multiphysics software. The electrical field inside the fluid domain was evaluated based on the Gauss’s law, Ohm’s law, and the continuity equation for the current. The governing equations in a frequency domain form are expressed as: 𝐸 = −∇𝑉 (5.1) 𝐷 = 𝜀0𝜀𝑟𝐸 (5.2) 𝐽 = 𝜎𝐸 + 𝑗𝜔𝐷 (5.3) ∇ ∙ 𝐽 = 0 (5.4) By combining these equations, Equation (5.5) can be obtained: 70 ∇ ∙ [(𝜎 + 𝑗𝜔𝜀0𝜀𝑟⁡)∇𝑉] = 0 (5.5) where 𝐽, 𝜎, 𝐸, 𝜔, 𝐷 𝑎𝑛𝑑 𝑉 are electric current density, electrical conductivity, electrical field, angular frequency, electric displacement field, and electrical voltage, respectively. The 𝜀0 is the relative permittivity of air and 𝜀𝑟 is the relative permittivity of the fluid respected to the air. When alternative current (AC) voltage is applied, both real and imaginary terms are created in 𝐽 which will be changed by the frequency. By solving these equations, the electric field (𝐸) is calculated in the fluid. In this study, only the variable 𝐸 is important as it has a direct influence on the amount of DEP force. Electrical potentials of 1 and -1 were used as a boundary condition for electrodes. All other boundaries were assumed to be insulated (glass and PDMS walls). The continuity condition was assumed for the electrodes and fluid interface. Triangular elements were chosen to mesh the geometry. The element size was finer near the electrodes’ boundaries, specifically the edges, and became coarser at regions further from the electrodes (See Figure 5-2). Figure 5-2 An example of the geometries used for the simulation of the electric field. Triangular elements are used to mesh the geometry using COMSOL Multiphysics. 71 5.3 Results In order to apply an effective DEP force for cell/particle separation, the geometry of the electrodes must be optimized. The optimized design leads to higher throughput and lower voltage (which means lower joule heating occurrence and higher cell viability). The geometrical parameters of this design, including the width of the electrodes and the electrode’s spacing, can affect ∇𝐸2 and the resulting DEP force. The input geometry for the simulation consists of two same-sized rectangular electrodes positioned at the bottom of the microfluidic channel. For several channel heights (25 µm, 40 µm, 60 µm, and 75 µm) the electric field was simulated for electrode’s spacing and width, ranging from 5 µm to 50 µm and 10 µm to 110 µm, respectively. An AC electric field of 2 Volts peak to peak was applied to the electrodes while the other boundaries of the channel were assumed to be isolated. In this study only the horizontal component of the gradient of the electric field square (∇𝐸2𝑥) is considered, as it is the dominant factor in the lateral displacement of the particles due to DEP force. Figure 5-3 presents the⁡∇𝐸2𝑥 counter for a sample with 100 µm electrode’s width and 15 µm electrode spacing. It shows that the highest gradient of the electric field is around the edges of the electrode. Increasing the distance from the electrodes leads to a decrease in the gradient of the electric field. As a result, if the channel is long enough, nDEP pushes the particles to the top of the channel, away from the electrodes, where the gradient of the electric field is minimum. As we study the nDEP effect in this research we have focused on the value of ∇𝐸2𝑥 at the top of the channel. In deeper channels, for example, 75 𝜇m, the cells do not necessarily reach the very top of the channel. However, since the optimum electrode width and spacing are not too sensitive to the height of the channel, we can neglect the actual height position of the cells and consider them within the top region of the channel. This misplacement, however, becomes greater as the height of the channel increases and should be considered as a factor in deeper channels. 72 Figure 5-3 The geometry of the channel (used for the COMSOL Multiphysics simulation) and⁡∇E2x contour (the width of the electrodes is 100 𝜇m and the electrode’s spacing is 15 𝜇m). The maximum values for ∇𝐸2𝑥 at the top of the channel are calculated and shown in Figure 5-4 (a-d). These results show that by increasing the width from 10 µm to 110 µm for any electrodes’ spacing and channel height values, the ∇𝐸2𝑥 increases until it reaches a maximum level. From that point on, any further increase in the width does not change the value of ∇𝐸2𝑥. This critical width is highly dependent on the height of the channel while only slightly dependent on the electrode’s spacing. Hence, for each channel height, designing the electrodes smaller than this corresponding critical width must be avoided. Based on these results, 40 µm, 40 µm, 60 µm, and 80 µm were chosen as the electrode width for 25 µm, 40 µm, 60 µm, and 75 µm channel heights, respectively. 73 Figure 5-4 The combined effect of the electrode spacing and electrode width on ∇E2x for different channel heights: (a) 25 𝜇m, (b) 40 𝜇m, (c) 60 𝜇m, (d) 75 𝜇m. As the next step in analyzing the simulation results, the effect of the electrode’s spacing was investigated. Considering the aforementioned electrode widths for each channel height, the ∇𝐸2𝑥 values were calculated for different channel’s heights and electrode’s spacing which is demonstrated in Figure 5-5. The results show that decreasing the electrode spacing leads to the higher values of ∇𝐸2𝑥 for all channel’s heights. This effect is more significant for lower channel heights. For instance, decreasing the electrode spacing from 50 µm to 5 µm for the 25 µm and 75 µm channel’s heights led to almost 205% and 22% enhancement of the ∇𝐸2𝑥, respectively. These results also reveal that decreasing the electrode spacing from 10 µm to 5 µm increases ∇𝐸2𝑥 by less than 4%. Although greater values of ∇𝐸2𝑥 are more 74 favorable, considering the fabrication limitations, 10 µm was chosen as the electrode spacing to allow for easier fabrication, lower costs, and higher reproducibility. Figure 5-5 ∇E2x as a function of the electrode spacing for each one of the studied channel heights. The results are presented for the chosen electrode width from the previous study. After finding the design criteria for the electrodes, the optimum condition for each channel height was chosen based on the data presented in Figure 5-4 and Figure 5-5. Using the chosen electrodes’ geometries, the effect of the channel’s height on ∇𝐸2𝑥 was investigated and presented in Figure 5-6. It can be observed that the value of ∇𝐸2𝑥 greatly increases by lowering the channel’s height. For instance, decreasing the height from 75 µm to 60 µm, 60 µm to 40 µm and 40 µm to 25 µm increased the ∇𝐸2𝑥 by approximately 93%, 229%, and 295%, respectively. However, to have a full understanding of the effect of channel height on DEP-based manipulation of particles, the effect of the hydrodynamic forces must also be considered, which is presented in the next section. 75 Figure 5-6 The effect of the channel height on ∇E2x for the chosen electrode configuration (electrode width and spacing). 5.4 Summary In this chapter, the role of the geometrical parameters of the planar slanted electrodes such as the electrode spacing and electrode width on the applied electric field and the resulting DEP force was studied. To achieve this goal, a simulation model was developed by using COMSOL Multiphysics. Based on the simulation results, we showed that increasing the electrode’s width to a certain amount, called critical width, causes greater DEP force. Further increase of the width above the critical value did not change the maximum DEP force. We also demonstrated that the critical width is dependent on the height of the channel. Furthermore, electrode spacing also affects the DEP force. Decreasing the electrode spacing, strengthen the induced DEP force. The effect of decreasing the electrode spacing on the induced DEP force is more significant for lower channels. Based on the simulation results, the 76 induced DEP force can increase up to 198% by proper selection of electrode width (110 μm) and spacing (10 μm) for a 75 μm channel height. 77 Chapter 6: Parametric study of effective throughput of the device5 6.1 Overview Generally speaking, DEP is a label-free method (with only a handful of studies using biomarkers to change the dielectric properties of cells 333) which makes it more cost-effective compared to other active methods such as magnetophoresis, fluorescent activated cell sorters (FACS), and magnetic-activated cell sorters (MACS). DEP-based devices are also easier to fabricate compared to other label-free active methods such as acoustophoresis. Besides, DEP has shown promising high-throughput capabilities with reported throughputs of 5000 cells per second 334, 10,000 cells per second 335, and more recently 170,000 cells per second 336. These advantages have attracted researchers towards the use of DEP for a range of applications such as cell patterning 147, trapping 337, focusing 338, enrichment 339, separation 340, and isolation 341. The first DEP devices were designed to separate cells and particles in a batch process. During this process, the sample fills a chamber that has electrodes on its surface that trap particles or cells affected by positive DEP (pDEP) within the chamber. As a result, by flowing the sample into the chamber, only the pDEP entrapped particles remain in the chamber, thereby separated from the rest of the particles. Later, by deactivating the electric field, the channel can be washed to collect the formerly trapped particles. Although this is a simple and effective technique, interest in pDEP has declined over the years due to its low throughput. Continuous DEP-based lateral displacement of particles is another technique that has garnered attention due to its higher throughput 342, 343. For this purpose, electrodes can be patterned into different 2D or 3D configurations (such as trapezoidal electrode arrays 344, spiral electrodes345, liquid-based 5 Parts of this chapter have been published in Scientific reports (A. Dalli, Scientific reports (2020), 10 (1), 1-11). 78 electrodes 346, slanted interdigitated 288, 297, 347 and interdigitated electrodes 153, 332, 348), as well as insulator-based DEP 289. Slanted electrodes in 3D or planar forms have been used for manipulation purposes such as focusing and sorting since 1998 349. Some researchers have taken advantage of pDEP to sort particles of different sizes, passing over slanted electrodes 151, 152. Despite these studies, the combination of nDEP and slanted electrode has been more commonly used. For instance, this method has been used for particle focusing 350, particle sorting 188, 298, 351, 352, separation of breast cancer cells from blood cells 278, enrichment of circulating nucleated cells from peripheral blood 353, separation of mouse P19 embryonic carcinoma and red blood cells 354 among others 177. Overall, 3D electrodes can produce more uniform and stronger DEP forces along the channel. Yet, the difficulty of perfect alignment and sealing has hindered their mass production 297. Planar electrodes, on the other hand, are more favorable as they can be fabricated by following a standard lithography process, which makes them simple, cost effect, and easy to operate in compared to 3D electrodes. Furthermore, the induced DEP force in such a 2D configuration could be strong enough to manipulate the particles or cells if the proper electrodes and microchannel sizes were employed. To take advantage of the planar electrode design in the DEP manipulation, the geometry parameters and operational conditions of the chip (including voltage, channel height, electrode’s angle) have to be optimized based on the size of the particles and the throughput of the device, accordingly. Although not specifically for the planar-shape electrodes, a few studies have reported the effect of the channel’s height on the levitation distance of particles 355 and the particle capture time 332. However, these effects depend on specific conditions and methods which are implemented in the previously reported studies, such as the geometry of the electrodes and the mode of the DEP force (nDEP versus pDEP). Kralj et.al 297 developed a model to study the effect of several parameters, including the channel’s length, particle size, voltage, and flow rate, on the transverse displacement of the particles. They validated their model using experimental data; however, the effect of the channel height and 79 electrode’s angle for DEP-based manipulation of microparticles using tilted interdigitated electrodes is not investigated yet. This chapter studies a continuous nDEP-based particle separator, which uses planar slanted electrodes. Multiple experiments were conducted to investigate the effect of the particle size, applied voltage, channel’s height, and electrode’s angle on the effectiveness of the DEP-based particle manipulation. In this study, the density of the buffer is adjusted with particles’ density. As the cells are in the same density range of their surrounding media, the density adjustment of the particles and media leads to better representing the biological cells. These results cannot necessarily cover all the aspects of biological cells. For instance, the cells are not as round nor rigid as the synthetic particles. Yet, the particles can be used as a proof of concept to show the application of the DEP force. The results showed that the DEP force is more dominant for larger particles, and higher voltages (as expected). Additionally, it was shown that decreasing the channel’s height increases the hydrodynamic forces against the DEP force; yet the increase in the induced DEP force is more significant. Based on our results, it was shown that the 25 𝜇m channel height and tilt angle of 8° provides the highest manipulation of particles in the slanted electrodes configuration. 6.2 Methods 6.2.1 Fabrication of the device The proposed DEP device includes a glass substrate, which is patterned with gold electrodes, bonded to a PDMS channel. A layer of 25 nm chromium followed by an 80 nm layer of gold is sputtered using a magnetron sputtering system (Angstrom Sciences Inc.). To pattern the electrodes, a standard photolithography technique is used: first, a layer of photoresist S1805 (MicroChem Corp.) was spin-coated at 3000 rpm for 30 seconds on the glass and baked at 115℃ for one minute. Next, the glass was exposed to ultraviolet (UV) light for 8 seconds, under the designed mask. The fabrication was then followed by washing away the exposed photoresist by rinsing the glass with the MF-319 developer (MicroChem Corp.). Next, gold and chromium etchants are used to pattern the electrodes. Finally, the 80 remaining photoresist is stripped away in the photoresist remover 1165. To fabricate the PDMS channel, an SU8 mold is fabricated in the cleanroom. Based on the desired height SU8 2025 or SU8 2075 is spin-coated on a silicon wafer. Following the guideline provided by MicroChem, the wafer is soft-baked at 65℃ and 95℃. Afterward, it is exposed to the UV light and baked again. Subsequently, it is soaked in the SU8 developer and patterned. Finally, after washing with isopropanol and hard baking for 30 minutes at 150 ℃, the mold is ready. A proper amount of PDMS and its curing agent are mixed with a proportion of 10 to 1 (w/w) and poured into the mold. After removing the bubbles in the desiccator and curing in the oven at 80℃, the PDMS channel is peeled off, and the inlets and outlets are punched. Finally, the PDMS and glass are surface treated with oxygen plasma and bonded together (see Figure 6-1). Figure 6-1 (a) The SU8 mold fabricated on a silicon wafer in the cleanroom. (b) A sample of the chips fabricated by photolithography. (c) The final chip under the microscope. 6.3 Sample preparation and experimental procedure The channel is washed with a solution of deionized (DI) water and 0.1% TWEEN 20 for about 30 minutes. The same solution is used as the sheath flow. The sample consists of the DI water, 0.1% (v/v) TWEEN 20, the 2% (v/v) polystyrene particles, and 20% (v/v) glycerol for density adjustment of the particles with the solution. The conductivity of the solution is measured as 20.3 𝜇S/cm. The permittivity of the polystyrene particles and DI water is 2.4 and 76.4, respectively 287. The permittivity of the solution is assumed to be the same as the DI water. 81 A syringe pump (KD-scientific) is used to inject the particles and sheath flow through 1-mL syringes (Hamilton gastight) into the micro-channel. The proper voltage and frequency are applied to the electrodes by a signal function generator. Depending on the geometry of the channel and injected particles, the appropriate flow rate is applied through the syringe pump. A microscope (Leica) is used to observe the trajectory of the particles. The experimental setup and an example of the polystyrene particles, effected by the DEP, moving toward the Target Outlet are shown in Figure 6-2. Figure 6-2 (a) The experimental setup including (b) an APO microscope, (c) 1 mL Hamilton glass syringes, which are connected to a KD Scientific syringe pump. (d) a close-up view of one of the microfluidics chips. The alligator clips and the inlet and outlet tubes are connected to the chip. The outlet solutions are collected for further analysis. 6.4 Design of the device As it is shown in Figure 6-3, the proposed DEP device consists of two inlets and two outlets. The particles enter the channel from one of the inlets and then they are pushed to the sidewall due to the hydrodynamic force caused by a sheath flow. As the particles approach the electrodes’ region, depending on their electrical properties and the resulting CM factor, they experience different amplitude and 82 direction of DEP force. The DEP force, as shown in Figure 6-3, consists of two components in the 𝑋𝑌 plane. The lateral component pushes the particles toward the opposite side of the channel. If the DEP force is dominant against the hydrodynamic forces, the particles are deflected to the target outlet. Otherwise, they remain in the same lateral position and follow the laminar streamline and move to the other outlet. The inertia can affect how quickly the particles respond to the applied forces. The inertia effects can be evaluated based on the Stokes number (𝑆𝑡𝑘 =2𝜌𝑝𝑅2𝑢09𝜇𝑙0). In a microfluidics device of 𝑙0 =50⁡𝜇𝑚 (characteristic length), 𝜌𝑝 = 1060⁡𝑘𝑔/𝑚3 (density of the polystyrene particles, 𝑅 ≃ 5𝜇𝑚 (radius of the particles), 𝑢0 ≃ 1⁡𝑚𝑚/𝑠 (average velocity of the flow, 𝜇 ≃ 10−3⁡𝑃𝑎. 𝑠 (viscosity of the fluid, the 𝑆𝑡𝑘 ≃ 10−4 is too low, and the inertial effects are negligible. As a result, the particles can be separated based on their response to the DEP force. The parameters affecting this process include the size of the particles (𝑅), electrodes’ width (𝑤) and spacing (𝑔), the height of the channel (ℎ), voltage (𝑉) and frequency (𝑓) of the applied electric field, and the flow rate (?̇?). 83 Figure 6-3 A schematic of the proposed DEP-based micro-separator with all its elements. 6.5 Results Based on the simulation results (chapter 5), the geometry of the electrodes inducing the strongest DEP force was determined. However, the simulation only considered the DEP force and not the hydrodynamic forces caused by the flow of the particles in the device. In order to investigate the effect of the channel’s height, electrode angle, particle size, and applied voltage on the effectiveness of the continuous DEP manipulation, a series of experiments were conducted, which are presented in this section. As demonstrated in the previous section, the DEP force decreases as a particle moves away from the electrodes, meaning that lowering the channel height leads to a greater DEP force. Another force on the particles that varies with the channel height is the drag force, which is proportional with the fluid velocity relative to the particles. When the DEP force dominates over the drag force, the particles are moved laterally towards the target outlet, shown in Figure 6-3. 84 As the height of the channel decreases, the particles are expected to be more influenced by the DEP force. On the other hand, decreasing the channel height reduces the cross-sectional area of the channel. Hence, in order to keep the volumetric throughput of the system constant, the velocity should increase, which leads to a larger drag force on the particles. Also, higher velocities lead to larger particle Reynolds number, which means that the inertial lift and lateral forces become more effective. Other forces such as the interaction of the particles with each other as well as the channel’s surfaces, can also affect the displacement of the particles. For instance, Brownian motion plays a role in the trajectory of the particles; however, it is not a dominant force for the sizes of the particles that are studied in this research. In order to investigate the effect of the channel’s height on the combination of all the aforementioned forces, and as a result the DEP-based manipulation of the particles, several experiments were conducted. To eliminate the effect of the size of the particles on the CM factor, the frequency was set to 10 MHz (at this frequency, the CM factor was almost -0.47 for all the studied particles). While the length (10 mm) and width (700 𝜇m) of the channel, and the deflection angle of the electrodes (5° deg.) were kept constant, the experiments were run for different channel heights (25 𝜇m, 40 𝜇m, 60 𝜇m, and 75 𝜇m). For each height, the goal was to find the maximum flow rate (volumetric throughput) as a function of the applied voltage that results in the particle collection efficiency of more than 99%. In other words, for each voltage, the flow rate was increased up to the point at which any further increase in the flow rate resulted in missing the particles in the desired outlet. Four different diameter sizes of the particles (5 𝜇m, 10 𝜇m, 15 𝜇m, and 20 𝜇m particles) and a voltage range of 2 to 10 Volts (peak to peak) were tested. In order to make sure that the experiments were repeatable, we repeated each of the tests three times. The DEP force is proportional to the gradient field squared, resulting in a linear relation with the DEP force and voltage squared. Consequently, the effect of the voltage squared is representative of all the affective parameters in the DEP force, specifically the permittivity of the medium and the CM factor. As the size of the particles contributes to both the DEP and hydrodynamic forces, its effect is separately investigated. The average volumetric 85 throughputs have been presented with their standard deviation (shown as the error bars) in Figure 6-4 to show the effect of the voltage squared on the maximum effective volumetric throughput for four different particle sizes and a 25 𝜇m micro-channel height. As expected, the larger the particles, the easier it is to deflect them with DEP force, and hence, the higher the flow rate. The same trend was observed for deeper channels. Figure 6-5 compares the maximum flow rates obtained from the largest voltage for different particle sizes as a function of the channel height. The results posit that as the height increases from 25 𝜇m to 75 𝜇m, the volumetric throughput decreases (for all particle sizes) since the DEP force becomes less dominant. Figure 6-4 The effect of the applied voltage squared (V2) on the effective throughput for a 25 𝜇m channel height. 86 Figure 6-5 The effect of the channel height on the effective throughput for an applied voltage of 10 Vpp. Another important parameter to consider is the angle of the electrodes. As mentioned earlier, the lateral component of the DEP force moves the particles laterally. This component is represented in Figure 6-3. In essence, the lower the angle, the larger the lateral component of the DEP force. On the other hand, for a fixed length of the channel, increasing the electrode’s angle increases the number of the electrodes that can fit in the channel, which may cause greater overall DEP-based manipulation of the particles. To investigate this, we conducted experiments on the effect of the electrode angle on the DEP manipulation of polystyrene particles with various diameters (5 𝜇m, 6 𝜇m, 8 𝜇m, and 10 𝜇m). A range of electrode angles from 5° to 45° were fabricated and tested. The 5° design comprised only one pair of electrodes; while the 45° consisted of 24 pairs. Similar to the previous results obtained for the effect of the channel height, the highest flow rate affecting the particles effectively was determined for each one of the electrode angles, and particle size. The results, presented in Figure 6-6, showed that decreasing 87 the angle from 45° to 8° increases the strength of DEP-based manipulation. However, any further decrease to the angle below 8° weakened the DEP manipulation of the particles. Figure 6-6 The effect of the electrode angle on the effective flow rate. 6.6 Summary Experimental results showed that the DEP manipulation is stronger for larger particles at higher voltages. A linear relationship was exhibited between the voltage squared and the volumetric throughput of the system. Since the strength of the DEP force is linearly related to the CM factor and voltage squared, the same linear relationship between the CM factor and the volumetric throughput is expected. Decreasing the absolute value of the CM factor, e.g. through manipulation of the frequency of the AC voltage, is expected to have the same effect as decreasing the voltage squared, which will lead to lower volumetric throughput. Additionally, the tests revealed that the channel height inversely affects the effectiveness of the DEP-based manipulation; meaning that lowering the height can lead to higher volumetric throughput of the system. For instance, decreasing the channel height from 75 𝜇m to 25 𝜇m, increases the throughput 88 up to 241% for 5 𝜇m particles. The last parameter that we optimized was the electrode angle. The trend of the effective throughputs of the system showed that generally lower angles are more effective, and the optimum angle is around 8°. Taken together, this work provides a guideline for designing the optimum electrode width and electrode spacing to achieve the highest induced DEP force for the proposed electrode configuration. In addition, the size-based response of the proposed device to changes of the electrodes deflection angle as well as the channel height was experimentally tested and discussed. These findings can help to achieve the optimal geometrical and operational conditions for designing effective DEP-based devices. 89 Chapter 7: Dielectrophoretic manipulation of particles6 7.1 Overview The performance of the slanted DEP-based systems (regardless of their electrodes form) heavily relies on several geometrical properties as well as the working conditions of the system. The geometrical properties involve the size of the electrodes, gap size, the deflection angle of the electrodes, the channel's height, width, and length. The working conditions include frequency and voltage of the applied electric field, permittivity and conductivity of the particles and media, flow rate, and size of the particles. Studying the effect of each of these parameters on DEP manipulation is a crucial step for the optimal design of DEP-based microdevices. There are several studies on the modeling and analysis of these devices. For example, the geometry of the electrodes and different aspects of its effects on the generated DEP force has been studied analytically 356, 357, and numerically 332, 358-361 by several groups. There are other studies in which the focus has been on the trajectory of the particles and how it relates to the DEP and hydrodynamic forces 148, 288, 362-364. A few commercial software such as COMSOL Multiphysics, CFD, and ANSYS Fluent are widely used to simulate the flow behavior in such devices 365. Several groups have also implemented experimental approaches to study the effective parameters on the performance of DEP-based devices with slanted planar electrodes. For instance, Kralj et al. 297 studied the continuous DEP-based separation of polystyrene particles. They worked on the force balance between the DEP and drag forces. They developed an analytical model to predict the lateral displacement of the particles and showed agreements between their model and their experimental results using 4 𝜇m and 6 𝜇m polystyrene particles under a range of voltages and throughputs. According to their model, the displacement of the particles is related to the particle size and voltage with a fourth-order, and it is linearly proportional to the length of the microchannel. Besides, the displacement is reversely 6 Parts of this chapter have been published in Sensors and Actuators B: Chemical (A. Dalli, Sensors and Actuators B: Chemical, 2020). 90 proportional to the flow rate squared. This model did not take into account the effect of the deflection angle of the electrodes, height, and width of the microchannel. Han et al. 298 conducted experimental studies to find the lateral displacement of particles per one set of electrodes at three different angles. They concluded that the particle size plays a major role in the displacement of the particles, and they managed to separate three sizes of polystyrene beads (3 𝜇m, 5 𝜇m, and 10 𝜇m) in their proposed microseparator with more than 98% purity (for each particle size). Later, they assumed that all the charges are at the edges of the electrodes, and based on that they developed a simplified line charge model for the numerical estimation of lateral displacement of the cells 299. Their proposed model suggested the ability of lateral DEP using interdigitated electrodes to separate the blood cells based on their size. This model does not include the effect of the flow velocity field and the resulting drag forces on the displacement of the particle. Significant steps have been taken in recent years to establish a standardized design protocol for DEP-based LOC separations devices. However, the application of these analytical approaches has been limited due to simplifications of such models, causing deviations from and poor predictability of the models. Experimental studies thus far have also simplified their analysis to a limited number of design parameters involved in the DEP design process. As a result, there is a lack of experimental studies on the multifactorial effects of input parameters in the planar electrode type of lateral DEP microseparators. Using statistical approaches, we provide here a general experimental model to characterize the separation efficacy of a DEP-based separator including slanted planar interdigitated electrodes. This study aims at identifying the effects of five different factors (i.e., channel length, width, the angle of the electrodes with respect to the flow direction, the volumetric flow rate, and the particle size) and their interactions on the lateral displacement of particles in a DEP-based LOC platform with planar interdigitated electrodes. A large library of data on the position and displacement of the particles (lateral displacements of more than 18,000 particles) is obtained and used to develop and validate a generalized linear regression model to aid the understanding of the effect of device inputs on the DEP-based 91 manipulation of cells and particles for focusing and separating applications. As a proof-of-concept, the capability of the presented approach for laterally displacing fibroblast cells is demonstrated. 7.2 Design, material, and method 7.2.1 Design The proposed device consists of three inlets (one for the buffer containing the sample particles and two for the sheath flows), planar interdigitated electrodes, and outlets (see Figure 7-1). PDMS microchannels were fabricated with 7 mm and 14 mm length and 400 𝜇m and 800 𝜇m width. Previous studies have shown the effectiveness of the lateral DEP microseparator: in essence, the DEP force increases when the height of the microchannel is decreased 297. Moreover, it is recommended to keep the channel height to width ratio above 0.05 (for PDMS channels) 366. Therefore, in this study, the heights for the 400 𝜇m and 800 𝜇m wide microchannels were set to 20 𝜇m and 40 𝜇m, respectively. Figure 7-1 A schematic of the device presenting the geometrical parameters of the device. Another parameter that we studied was the deflection angle of the electrodes with respect to the direction of flow (10° and 45°). The effects of the width and spacing of the electrodes have numerically been studied by our group in the past. It was shown that by increasing the width to a certain level, the DEP force increases. For the selected heights of the microchannels, 60 𝜇m wide electrodes were chosen, as a further increase in the width had a negligible effect on the DEP force. Also, based on our simulation 92 results, decreasing the electrode spacing results in higher DEP forces. Considering microfabrication limitations, we have selected 20 𝜇m as the electrode spacing. Based on the geometrical properties, the number of electrodes (n) can be calculated by the following equation: 𝑛 =𝐿𝑠𝑖𝑛𝜃 − 𝑊𝑐𝑜𝑠𝜃 + 𝑔(𝑔 + 𝑤) (7.1) where, L, W, w, g, and 𝜃 are the length of the DEP region, the width of the channel, the width of the electrodes, electrode spacing, and deflection angle of the electrodes, respectively. The first step of the DEP microseparator is to focus all the particles in a limited width of a microchannel. We applied a weak and strong sheath flow to take advantage of the hydrodynamic forces to focus the particles. After focusing, the particles enter the DEP region. The non-uniform electric field, generated by the slanted planar electrodes, applies nDEP force on the particles. Consequently, the particles levitate and move to the top of the channel. Since the electrodes are slanted, a lateral force is applied to the particles, deflecting them toward the opposite side of the channel. As the DEP force depends on the volume of the particles, the lateral displacement of the particles varies based on their size, allowing them to be separated. 7.2.2 Device fabrication, sample preparation, and experimental setup The electrodes were patterned on 25 × 75 mm microscope slides (VWR VistaVision™ Microscope Slides) following the photolithography processes, see Figure 7-2 (a). The PDMS channel was also fabricated by using an SU8 mold that was fabricated in our cleanroom facility, see Figure 7-2 (b). For details of the fabrication please refer to chapter 3. The channel and glass slide were plasma treated (PE-50, PLASMA ETCH) for 30 seconds at 50 Watts power, and 5 cm3/min oxygen flow rate. The oxygen plasma-treated channel and substrate were bonded to each other using a handmade PDMS aligner 305. The chip was placed on a hotplate at 80℃ for 30 minutes to achieve a perfect bonding. Figure 7-3 (c) depicts one of the chips under a microscope. 93 Figure 7-2 (a) an example of the patterned electrodes on a glass substrate. (b) An SU8 mold that is fabricated in the cleanroom. (c) An example of one of the microfluidics chips. Polystyrene microparticles (Sigma-Aldrich) with three different diameter sizes (5 μm, 10 μm, and 15 μm) were suspended in separate batches of de-ionized (DI) water. TWEEN® 20 (Sigma-Aldrich) with 0.1% (Volume/Volume) was added to the samples to avoid particle adhesion to the surfaces. In order to avoid the sedimentation of the particles in the syringe, the density of the solution was adjusted with the particles, from 0.998 g/cm3 to 1.060 g/cm3, by adding Glycerol (Sigma-Aldrich) to the solution. The conductivity of the solution at 25℃ was measured as 20.3 μS/cm. NIH 3T3 mouse fibroblast cells were cultured at 37ºC with 5% CO2. The cell culture media consists of Dulbecco's Modified Eagle Medium (DMEM) with 10% v/v fetal bovine serum and 1% v/v penicillin-streptomycin. The cell medium was changed every three days. The cells grown in the cell culture flasks were treated with trypsin to detach the cells. The cell culture media was added after a few minutes to neutralize the added trypsin. The cells suspended within the trypsin-media mixture was centrifuged at 1500 rpm for 3 minutes to obtain a cell pellet. The trypsin-media supernatant mixture was then aspirated, and a 10-mL solution of the DEP buffer (8.5% w/v sucrose, 0.3% w/v glucose, 0.725% v/v RPMI) was supplemented with 0.1% w/v Bovine Serum Albumin (BSA) and 0.1 mM Ethylenediaminetetraacetic acid (EDTA) and added to the cell pellet. After thorough mixing of the cell 94 pellet with the prepared buffer by pipetting multiple times, the mixture was centrifuged at 1500 rpm for 3 minutes to obtain the cell pellet. This washing process was carried out two more times to treat the cells with BSA and EDTA (BSA helps in preventing the aggregation of cells whereas EDTA helps in preventing the cell adhesion to the surface of the electrodes). Finally, the cells were suspended in the DEP buffer. The sample, as well as a weak and a strong sheath flow, were infused into the microchannel using syringe pumps (Masterflex®) from the inlets 1, 2, and 3 with a ratio of 1: 1: 8, respectively (see Figure 7-1). The weak sheath flow was used to avoid even the weak DEP force region near the wall. A function generator was used to apply 10 MHz, 10 Vp-p AC voltage to the electrodes. The frequency was maintained at 10 MHz throughout the experiments, keeping the CM factor at around -0.47 for each polystyrene particle size. A digital microscope (REVOLVE 4, DISCOVER ECHO INC.) was used to track and record the trajectories of the particles. The microscope was equipped with 10x and 20x optical lenses to provide magnified images of the particles. The experimental setup is shown in Figure 7-3 (a-d). Each experiment was repeated three times to make sure that the results are reproducible. Figure 7-3 (a) Representation of the experimental setup including the microscope, syringe pumps, and function generator. (b) The function generator that is used to apply electric potential to the electrodes. (c) The 95 close-up view of the chip including its inlet and outlet connections. (d) The screen of the microscope showing the microparticles reaching the outlets. 7.2.3 Experimental factors and level Two levels have been chosen for each one of the geometrical parameters of the device: 7 mm and 14 mm for the length; 400 𝜇m and 800 𝜇m for the width; and 10° and 45° for the deflection angle of the electrodes. The experiments were conducted for 5 𝜇m, 10 𝜇m, and 15 𝜇m polystyrene particles and a range of volumetric flow rates from 5 𝜇L/min to 20 𝜇L/min. All the factors are shown in Table 7-1. For the separation experiments, a hemocytometer was used to find the purity of the particles in each outlet. Table 7-1 The summary of the studied factors and their levels. Factor Length (mm) Width (𝜇m) Angle Particle diameter (𝜇m) Volumetric throughput (𝜇L/min) Levels 7, 14 400, 800 10°, 45° 5, 10, 15 5, 7.5, 10, 15, 20 7.3 Data acquisition In order to quantify the effect of DEP on the manipulation of polystyrene particles, a video processing algorithm, known as Droplet Morphometry and Velocimetry (DMV) developed by Amar S. Basu 327, was used (this software breaks the videos into separate frames. Each frame goes through a series of image processing steps to finally determine the position of the particle.) For data acquisition, the microscope recorded a fixed area near the outlets of the device where the lateral positions of the particles were at a steady state. For each set of experiments, the initial and final position of the particles after being affected by DEP were recorded. In total, 80 videos were gathered. Each video was analyzed by DMV and information on the position of the particles was obtained. A summary of how the software recognizes the particles is shown in Figure 7-4. Also, a video generated by the software is provided in the Supplementary Information. The video shows that the particles that flow in the microchannel are detected and identified by the software. 96 Figure 7-4 Sample images of how the DMV works. (a) The original frame that is analyzed by the software. The particles are on the right side of the channel. (b) The background image showing the same area without any particle. (c) By subtracting image (b) from image (a), the software identifies the particles. The particles are shown with colorful circles. 7.4 Results and discussion The position of polystyrene microparticles before and after being manipulated by the DEP force was recorded and studied for a variety of conditions, and the effects of the width and length of the microchannel, the deflection angle of the electrodes, the flow rate, and the particle size on the position of the particles (near the outlets) after being affected by DEP were examined. Using a flow-focusing technique, the particles were initially focused near the sidewall of the microchannel (see Figure 7-5(a), 𝑡 = 0). After applying the voltage, particle velocity suddenly decreased as they were levitated to the top of the microchannel due to the DEP force which partially acted against the drag force. As a result, the number of particles flowing over a fixed area after passing over the electrodes (i.e., the region over which the DEP acts on the particles) dropped as represented in Figure 7-5, 𝑡 = 1, 2 s. Due to the DEP force, the particles deflected towards the other side of the channel (Figure 7-5, 𝑡 = 3-7 s). After the position of the particles was stabilized, the final positions in each experiment were recorded. The distributions of the particles before and after the DEP manipulation are shown in Figure 7-5(b). In addition to the particles shift to the right, their distribution along the width (shown by the standard deviation of the distribution) increased meaning that the particles were distributed in a wider region after being manipulated by DEP. 97 Figure 7-5 (a) The trajectory of 15-𝜇m polystyrene particles in a 400-𝜇m wide channel. The pictures were taken from a fixed region near the outlet of the chip. At t = 0 s, 10 V is applied to the electrodes. The particles have migrated to the right until they reach a fixed position. (b) Using the video processing software, the distribution of the particles before and after applying the electric field was found. We ran the experiments for a variety of factors at different levels, as presented in Table 7-1. Our statistical analysis showed that the studied parameters (including the length and width of the microchannel, the electrode angle, the particle size, and the sample flow rate) did not have a significant effect on the distribution of the particles when the effect of the DEP diminished (i.e., the applied voltage was turned off). As a result, studying the lateral position of the particles not only provides information for the particle separation purposes but also reflects a measure of the effect of each parameter on the displacement of the particles and the effectiveness of the DEP-based manipulation. An ANOVA test was performed on the position readouts from the captured videos. The ANOVA test showed that the contribution of all the studied factors, except for the channel width (p>0.05), were statistically significant (p<0.05). The results suggest that the flow rate has the most contribution to the variation of the particle position (48.51%), followed by the particle size, which contributed to 9.22% of the variation. Among the interactions between the parameters, the deflection angle of the electrodes and the particle size have the most contributions to the variation of the particle’s position. 98 To ensure the statistical analysis correctly reflects the particle position behavior, the test quality plots were evaluated (represented in Figure 7-6). The residual plot shows the data points lying on a straight line which means that the analysis is not affected by the outliers (Figure 7-6(a)). The symmetricity of the residuals versus the fitted values (Figure 7-6(b)), the randomized trends in the residual-observation order (Figure 7-6(c)), as well as the symmetrical normal distribution of the residual values (Figure 7-6(d)), are all indicative of the test independence and constant variance, supporting the assumptions of ANOVA. We repeated the ANOVA test by removing the insignificant factors and observed no change which confirms the reliability of the model. Figure 7-6 The residual plots showing the validity of the ANOVA test. (a) Normal probability plot representing the data points lies well on the red line. (b) Residual versus fitted value demonstrating a well symmetric pattern. (c) Random trends in the behavior of residual-observation order and (d) histogram of residual showing a symmetric normal distribution with respect to the zero points. 99 As explained before, the results of the ANOVA test showed the significant factors affecting particle displacement observed near the outlets. To study the trend in the effect of these factors and their interactions, the main effect and the interaction plots were obtained, see Figure 7-7(a, b). From the main effect plots (representing the overall trends of the particle displacement with changes in each factor) it is concluded that the displacement due to DEP increases with the length of the electrodes region and particle size, and decreases with the electrode angle and flow rate. We discussed the underlying mechanisms pertinent to each factor separately in the following sections. Besides, the non-parallel trends in the interaction plots, Figure 7-7(b), represent an ordinal interaction where the effect of one independent variable may depend to some extent on the level of the other independent variable (such as the interaction between the deflection angle of the electrodes and particle size). The underlying interactions are also discussed in the following sections. Figure 7-7 (a) The main effect plot showing the effect of each parameter on the nondimensional lateral position of the particles, and (b) the interaction plots. 100 7.4.1 Effect of channel width In previous sections, we explained that the aspect ratio (i.e., the height to the width ratio) of the PDMS-based microchannel for the proposed microseparator is 0.05. Hence, for a constant volumetric flow rate of the sample, the fluid velocity, as well as the particle velocity, are inversely proportional to the channel width squared (1/𝑤2). The particle’s velocity and the lateral displacement of the particles are expected to be inversely correlated because when the particles move faster, they have less opportunity to be laterally pushed by the DEP force. We have previously found that the channel height and particle displacement are inversely related. Overall, the effect of the width includes both the fluid velocity and the channel height. These two terms interact against each other for the manipulation of the particles. Our results showed that the width of the channel had the most significant effect on the lateral position of the particles compared to other geometrical properties of the proposed device. However, the standardized position of the particles, which was calculated by dividing the position by the width, is the decisive factor for separation and focusing purposes. The ANOVA test showed that the width does not have a significant effect on the standardized position of the particles (p>0.05). Therefore, we did not include the effect of the microchannel width in the model. 7.4.2 Effect of flow rate Based on the statistically significant effect of the flow rate on the final particle position (p<0.05) and its ease of tunability, the flow rate is a highly desirable factor for positioning the particles at the desired location. Depending on the application (whether it is separation or focusing) and the sample particle sizes, adjusting the flow rate can achieve the intended manipulation process. The effects of flow rate on particle positions are shown in Figure 7-8 and Figure 7-9. In essence, increasing the flow rate consistently decreases the lateral displacement of the particles. 101 Figure 7-8 The distribution of the particles affected by nDEP for (length = 14 mm, width = 800 𝜇m, deflection angle = 10°) at different flow rates and particle sizes. In general, at low flow rates, the particles were highly affected by the DEP force and were deflected to the opposite side of the channel. Depending on the geometrical properties and operating conditions (specifically the particle size and the flow rate), the particles were trapped by the nDEP force along the wall of the channel, where the fluid velocity was low due to the no-slip boundary condition and the resulting boundary layer velocity profile. Increasing the flow rate released all the particles from the traps and aligned them moving along the channel wall. A further increase of the flow rate resulted in less displacement of the smaller particles, while the larger ones reached the other side of the channel (e.g. Figure 7-8 at the flow rate of 7.5 𝜇L/min). Based on the balance of hydrodynamic and DEP forces, the particles were laterally positioned as a function of their sizes (for instance, Figure 7-8, when 102 the flow rate is 10 and 15 𝜇L/min). At even higher flow rates, it was observed that the effect of the flow rate on the change of the lateral displacement of the particles is less important. For instance, Figure 7-9 shows that increasing the flow rate from 5 to 7.5 𝜇L/min led to 43% and 54% reduction in the displacement of the particles in the channels with 7 mm and 14 mm DEP region length, respectively. However, increasing the flow rate from 15 to 20 𝜇L/min resulted in 12% and 14% reduction in the displacement of the particles in channels with 7 mm and 14 mm DEP region length, respectively. Eventually, at even higher flow rates, the effect of the DEP on the particles was negligible. The main effect plot, Figure 7-7 (a), supports the above observation, showing that the effect of the flow rate became more noticeable by decreasing the flow rate from 20 𝜇L/min to 7.5 𝜇L/min. At lower flow rates, some of the particles already reached to the other side of the channel and could not be further displaced. Thus, the effect of the flow rate on the variation of position decreased. Figure 7-9 The lateral displacements of particles in 7 mm and 14 mm channels at different flow rates (length = 14 mm, width = 800 𝜇m, deflection angle = 10°). 7.4.3 Effect of channel length As the length of the DEP region increases, more electrodes can fit into the microchannel. As a result, the particles are technically more exposed to the nDEP force pushing them laterally to the other side of the channel. The results of our statistical analysis showed that the contribution of the length of the DEP region to the lateral displacement of the particles was the highest among all the geometrical 103 parameters of the device. As an example, the lateral “positions” and “displacement” of the microparticles after the DEP region for 7 mm and 14 mm long microchannels are depicted in Figure 7-10 and Figure 7-11, respectively. In this example, the width of the channel was 800 𝜇m and the electrodes are at an angle of 45°. Figure 7-10 The effect of the particle size and flow rate on the lateral position of the particles. For all the three flow rates and particle sizes, as the length increased from 7 mm to 14 mm, the particles shifted to the right, which means that the lateral displacement increased. The examples presented in Figure 7-10 and Figure 7-11 confirm the main effect plot for the length (i.e., Figure 7-7 (a)): for all the particle sizes and flow rates, increasing the length of the DEP region from 7 mm to 14 mm led to larger lateral positions and displacements of the particles. Besides, Figure 7-10 suggests that when the particles passed a larger DEP region, they ended up being distributed in a wider region (and as a result a larger standard deviation), which is a drawback for separation applications. 104 Figure 7-11 The effect of the particle size and flow rate on the displacement along the width of 7-mm and 14-mm channels (width = 800 𝜇m and angle = 45°) for different particle sizes and flow rates. Unlike the separation applications, longer channels are useful for focusing purposes. For instance, for channels with a 7-mm length of the DEP region, 400-𝜇m width, and deflection angle of 10°, the mean lateral position of the 5 𝜇m, 10 𝜇m, 15 𝜇m particles were 251.7 𝜇m, 378.2 𝜇m, and 388.4 𝜇m, respectively, at 4𝜇L/min flow rate. Increasing the length to 14 mm focused all the aforementioned particles in a narrow region near the wall side of the channel (approximately at a 400-𝜇m lateral position). A practical example to render the importance of the length adjustment is a two-stage DEP-manipulator: the length can be adjusted so that in the first DEP region, the particles are focused in a limited area. As they pass over the second DEP region (with a shorter length), the particles are separated. 7.4.4 Effect of the deflection angle of the electrodes The lateral component of the DEP force, which pushes the particles along the width of the microchannel, is proportional to cos𝜃 (see Figure 7-1). As a result, it is expected that at lower angles, the particles to be laterally pushed even further for each pair of electrodes. On the other hand, as the angle decreases, the number of electrodes that can be fitted into a specific length of the DEP region also decreases (see Equation (7.1)), which decreases the exposure of the particles to the DEP force. The 105 results in Figure 7-7(a) confirms that the particles passing over the electrodes with the 10° deflection angle were displaced more than the ones passing over the electrodes fabricated at 45°. Figure 7-7(b) shows that the effects of the deflection angle of the electrodes were interactive with the particle size and the sample flow rate. As an example, the relative changes in the final position of the particles in 14 mm long, 400 𝜇m wide channels for the deflection angle of 𝜃 =10°⁡compared to 𝜃 =45° are depicted in Figure 7-12. The results in this figure confirm that the smaller the deflection angle the larger displacement of the particles. Besides, this figure presents the interaction effect of the flow rate and electrode deflection angle: regardless of the particle size, decreasing the flow rate from 20 𝜇L/min initially showed an increase in the effect of the electrode angle on the variation of the particle position up to a maximum point. For instance, for the particle size of 10 𝜇m, the mean of the particle position is 13% and 106% greater for the designs with 𝜃 =10°⁡compared to 𝜃 =45° at 20 𝜇L/min and 7.5 𝜇L/min of flow rate, respectively. At lower flow rates, particles moved closer and closer to the other side of the channel. Eventually, the impact of the electrode deflection angle diminishes as the particles moved to the sidewall (e.g., at 5 𝜇L/min, 2.5 𝜇L/min, and 1.5 𝜇L/min flow rates the deflection angle does not affect the particle positions for 15 𝜇m, 10 𝜇m, and 5 𝜇m particles, respectively). 106 Figure 7-12 The relative change in the position of 5 𝜇m, 10 𝜇m, and 15 𝜇m particles passing over electrodes deflected at 10° compared to electrodes deflected at 45°. The length of the DEP region and width of the microchannel are 14 mm and 400 𝜇m, respectively. Figure 7-12 also shows that the effect of the electrode angle on the variation of the particle position was greater for the larger particles. For example, changing the electrode deflection angle from 45° to 10°, resulted in up to 45% and 120% increase in the position of 5 𝜇m and 15 𝜇m particles, respectively. In addition, the average standard deviation of the distribution of the particles was 16% higher for the designs with 45° electrodes, which is not suitable for separation purposes as it can decrease the yield purity in the outlets. 7.4.5 Effect of particle size The DEP force is a function of the volume of the particles. Therefore, it is expected that the displacement of the particles due to the continuous DEP manipulation depends on the particle size, which our statistical analysis, supports with a 95% confidence interval. For separation purposes, a set of conditions where the effect of the particle size provides a higher contribution to the variation of the position is favorable. The interaction plot, Figure 7-7(b), shows that the effect of the particle size has 107 significantly increased by changing the angle from 45° to 10°. Also, the effect of the particle size and the flow rate interact with each other. As an example, Figure 7-8 shows how changing the flow rate from 7.5 𝜇L/min to 20 𝜇L/min has affected the lateral position of the particles. At flow rates lower than 4 𝜇L/min, all the particles were moved to the side of the channel, leading to zero contribution of the particle size to their position. At a flow rate of 7.5 𝜇L/min, the 10 𝜇m and 15 𝜇m particles were at approximately at the same position (near the wall of the channel region) and distinct from 5 𝜇m particles which are positioned in the middle of the channel. At a flow rate of 10 𝜇L/min, the size of the particles had greatly contributed to their lateral position as all the three sizes were separated from each other along the width of the channel. By further increasing the flow rate to 12.5 𝜇L/min and even further to 20 𝜇L/min, the contribution of the particle size to the position of the particles decreased as the mean position of the particles became closer and closer. 7.5 Applications in particle focusing and separation The device was capable of focusing all three sizes of the particles within a narrow region (2% of the channel width) beside the sidewall of the microchannel, with a volumetric throughput of 4 𝜇L/min. Also, a more portable and user-friendly LOC device with a higher sample throughput of this design can be achieved by integrating DEP-based focusing and separation sections into a single chip, which eliminates the need for sheath flows. Our experimental results confirmed that with the electrodes deflected at 10° and at a total volumetric throughput of 4 𝜇L/min, all the particles (which were passed over 14 mm of the DEP region) were shifted to the sidewall of the channel. For the same conditions, except for a DEP length of 7 mm, the particles were positioned in totally separate regions of the channel. The total volumetric throughput, in the current device, consists of 10% sample and 90% sheath flow. By integrating the focusing and separation regions, the whole 4 𝜇L/min throughput will be for the sample, which is a great increase in the throughput of the system. 108 To fully take advantage of this study, an accurate nonlinear model can be developed in the future to predict the behavior of different sizes of particles in different channels. Based on the application (focusing or sorting) and the sample particle sizes, the geometrical properties of the device can be selected from the model to maximize the volumetric throughput. Besides the channel length and the deflection angle of the electrodes, the position, and width of the outlets can be designed, which can be of great assistance in achieving high purity and throughput of size-based separation of cells and particles. 7.6 Applications in cell manipulation To test the applicability of the findings of this study in biological applications, manipulation of fibroblast (3T3) cells was performed on the developed device. The frequency response of the cells was studied and is presented in the supplementary document. In this study, the frequency is set to 50 kHz to keep the CM factor close to -0.5 (see Figure 7-13), which resembles the previous experiments with the polystyrene particles. The cells were suspended in the prepared DEP buffer and infused into the channel. Figure 7-13 The CM factor response of the fibroblasts (3T3) cells based on the single-shell model for different conductivities of the media (𝝈𝒄𝒚𝒕𝒐 = 𝟎. 𝟔⁡𝑺/𝑴, 𝜺𝒄𝒚𝒕𝒐 = 𝟓𝟎, 𝝈𝒎𝒆𝒎 = 𝟏𝟎−𝟔⁡𝑺/𝑴, 𝜺𝒎𝒆𝒎 = 𝟏𝟐, 𝑹 = 𝟖⁡𝝁𝒎). 109 Similar to the response of the particles, increasing the flow rate from 5 to 15 𝜇L/min led to a smaller displacement of the cells, as shown in Figure 7-14(a). Increasing the length of the DEP region from 7 mm to 14 mm, resulted in a larger displacement of the cell, Figure 7-14(b). Moreover, the cells that were passed over the electrodes deflected at 10° experienced greater displacement than the cells that were passed over electrodes at 45°, Figure 7-14(c). Overall, the results show that the 3T3 cells follow the same general trends as the particles. Figure 7-14 (a) The normalized position of the cells in a 14 mm long channel (DEP region), and electrodes deflected at 10°. The distribution of the position of the cells at different flow rates (5, 10, and 15 𝜇L/min) are presented. (b) The position of the cells in 7 mm and 14 mm long channels (flow rate of 5 𝜇L/min and electrodes deflected at 10°). (c) The position of the cells passing over electrodes deflected at 10° versus 45° (flow rate of 5 𝜇L/min and 14 mm long channel). A few differences were observed between the behavior of particles and cells on the device. The 3T3 cells were positioned in a wider region of the channel compared to the synthetic particles. This can be attributed to the heterogeneity of the cell sizes. The wide range of cell sizes (10 to 20 𝜇m) can be problematic for size-based cell separation, however, if the device is well designed, the device can still be effective for cell focusing applications or the cases where the cell sizes are largely different (such as the separation of red blood cells from cancer cells). 110 7.7 Summary In this chapter, we experimentally examined the effect of the length and width of the microchannel, the deflection angle of the electrodes, the volumetric throughput, and the particle size on the lateral displacement of polystyrene particles. We developed a generalized linear model and used statistical analysis to demonstrate the significance of each one of the parameters and how the interaction of these parameters affects the particles. The findings of this study identified that the effect of the microchannel width on the nondimensional displacement of the particles is negligible, while the rest of the parameters are responsible for the variation of the positions. Importantly, we demonstrated that the volumetric throughput and length of the DEP region have the most contribution to the position of the particles. Increasing the length of the microchannel while decreasing the angle of the electrodes as well as the volumetric throughput leads to a higher displacement of larger particles. While the length of the DEP region independently affects the position of the particles, the volumetric flow rate, particle size, and deflection angle of the electrodes are highly interactive. For electrodes slanted at a lower angle, the particle size contributes more effectively to the position of the particles, which enhances the sorting capability of the device. Besides, depending on the flow rate, the contribution of the particle size to the position of the particles varies greatly. The same trends were observed for the manipulation of NIH 3T3 mouse fibroblast cells. The results of this study can advance the design of DEP-based microchip platforms for focusing or sized based separation of particles or cells. For focusing purposes, the greatest displacement of all the particle sizes is of interest. Consequently, longer channels with a lower angle of the electrodes are suitable. However, for separation purposes, the design is more complex as the largest contribution of the particle size to the variation in the positions is desired. As the size of the particles and the angle of the electrodes highly interact with each other, we suggest fixing the angle at 10°. Then by adjusting the length and the flow rate, efficient separation of the particles can be achieved. For instance, our results showed that for a volumetric throughput of 10 𝜇L/min, 5, 10, and 15 𝜇m particles were positioned in distinct regions in a 14 mm long channel. Our proposed design can, therefore, be applied towards the 111 development of portable and user-friendly LOC devices with a higher sample throughput by integrating DEP-based focusing and separation regions into a single chip. 112 Chapter 8: Particle focusing and separation 8.1 Overview Cell, bacteria, and particle separation technologies are rapidly improving due to their important role in sample preparation, rapid clinical detection, and environmental applications such as water purification. In the previous chapters, mostly in chapter 1, the different on-chip manipulation methods were introduced. Besides, the advantages and disadvantages of each method were discussed in detail. The linear relationship between the DEP force and volume of the particles makes DEP a popular method for size-based separation of biological cells and microparticles 297, 299, 367-369. The majority of the studies have used sheath flows to focus the particles before entering the DEP-based separation region 296-299, 367-369. The main reason that sheath-assisted focusing is so popular is the tunability of this method and the availability of the required equipment (such as syringe pumps) in research labs. However, the elimination of the sheath flows can enhance the portability of the device and make the operation of the device cheaper and simpler. Besides, the elimination of the sheath flows can improve the throughput of the device, significantly. Several active and passive methods have been implemented to achieve particle focusing (similar to separation discussed earlier) in microfluidics devices, one of which is DEP 307. In particular, DEP focusing is suitable prior to the DEP separation. Since the focusing and separation regions use the same method, the fabrication is easy and cost-effective. Also, the effective throughputs for both regions are in the same order and can be combined. So far there are a few studies, which have focused and separated cells or particles by the DEP force on the same chip 278, 370. These studies have a combination of nDEP and positive DEP (pDEP) for focusing and separation of the particles. One of the limitations in using pDEP is the potential negative effect of the high gradient of the electric field on the cells and their viability. To the best of our knowledge, there is no article in the literature that has used nDEP to focus and separate triple sizes of particles so far. 113 In this chapter, we implemented two nDEP regions for focusing and separation of particles on a lab-on-a-chip (LOC) device. Of course, such a device should be well designed to achieve high throughput and effective DEP focusing and DEP separation on the same platform. Several studies have investigated the effect of different geometrical as well as operational parameters on the DEP-induced displacement of the particles. In previous chapters (5 and 6), we investigated the effect of the geometry of the electrodes, applied voltage, volumetric throughput, particle size, as well as the height, width, and length of the microchannel on the displacement of the particles has been previously studied. COMSOL Multiphysics simulation results have shown that by decreasing the electrode gap and increasing the electrode width to a critical value, which depends on the channel height, the DEP force enhances significantly. In chapter 7, we have shown that the DEP-induced displacement of the particles increases with the length of the DEP region and decreases with the volumetric throughput. Also, we reported that the electrodes slanted at 10 degrees (with respect to the flow direction) lead to a larger displacement of the particles compared to the electrodes deflected at 45 degrees. In none of these studies, we have investigated the effect of the sheath flow on the separation of the particles. In this chapter, we have investigated the performance of sheath-assisted and sheathless nDEP microseparators. Three DEP microseparators are proposed and fabricated: sheath-assisted, sheathless, and weak sheath-assisted. All of these designs are based on almost the same design criteria (electrode angle, spacing, and width, as well as the length of the DEP region) for the DEP regions. The only difference is the length of the DEP focusing region and DEP separation region, which is 18 mm and 7 mm (these figures were obtained from our previous study), respectively. Using each one of the proposed devices, 5, 10, and 15 𝜇m particles are separated from each other. The volumetric throughput and separation efficiency of the three designs are evaluated and compared with each other. The results suggest that the highest throughput can be achieved by the sheathless design (4.7 𝜇L/min) while the highest yield and purity can be achieved by the weak sheath-assisted design. Also, adding a DEP focusing region to the sheath assisted design (i.e., the weak sheath assisted) improves the separation efficiency and tremendously increases the throughput of the device. 114 8.2 Generalized linear model In chapter 7, the lateral displacement of more than 18,000 particles was found by using a video processing technique, called DMV. The output was statistically analyzed (using ANOVA test) to find the significantly effective factors on the DEP-induced displacement. It was shown that the length of the DEP region, the deflection angle of the electrodes, particle size, and volumetric throughput of the device significantly contribute to the lateral position of the particles affected by the DEP force. Taking into account the effect of the significant factors (i.e., the sample flow rate, length, electrode's angle, and particle size), a generalized linear regression model was developed in the form of the following equation with 𝑅2 = 0.8978: X = -0.1246 + 0.04212 L + 0.002760 𝜃 + 0.07479 D - 0.01628 ?̇?- 0.000273 L×𝜃 - 0.000499 L×D - 0.001005 L× ?̇? - 0.000937 𝜃 ×D + 0.000474 𝜃 × ?̇? - 0.001394 D× ?̇? (8.1) In the above equation, 𝑋 is the nondimensional position,⁡𝐿 is the length in mm, 𝐷 is the particle size in 𝜇m, ?̇? is the volumetric flow rate, and 𝜃 is the deflection angle of the electrodes in degree. The above equation was used to plot the lateral displacement response (X) as a function of different studied factors, Figure 8-1. Each plot represents how the position of the particles is affected by two factors. The developed model can be used to predict the conditions at which focusing or separation is more likely to be accomplished to optimize the design for the desired throughput. For instance, if particle focusing is the desired outcome, the results suggest a device of a length of 𝐿 > 14 mm, deflection angle of 𝜃 = 10°, and flow rates lower of ?̇? < 5 𝜇L/min to focus particles in the range of 10 𝜇m to 15 𝜇m in one side of the channel (i.e., 𝑋> 0.85) . As another example, if the separation of different particle sizes (e.g., 10, and 15 𝜇m) is of interest, the results suggest a device with 𝐿 = 11 mm, 𝜃 = 20°, and ?̇? = 5 𝜇L/min to sort 10, and 15 𝜇m particles at 𝑋=0.68 and 𝑋=0.9, respectively. In order to achieve a sheathless micro-particle separator, the suggested DEP focusing and separation configurations can be integrated into one platform with a flow rate of 5 𝜇L/min. Besides, the outlets of the device can be positioned (in the design process) based on the predicted locations of the particles. 115 Figure 8-1 The lateral positions of the particles as a function of the length of the DEP region, the deflection angle of the electrodes, volumetric flow rate, and particle size are presented. Each graph investigates the effect of two factors while the other ones are held at constant values (Length =10 mm, Angle = 10°, Particle size = 10 𝜇m, and flow rate = 7.5 𝜇L/min). 8.3 Design Our proposed DEP-separator is made of gold electrodes sputtered on a glass substrate, which is bonded to a polydimethylsiloxane (PDMS) microchannel. Three devices for sheath-assisted, sheathless, and weak sheath-assisted DEP separator are proposed and depicted in Figure 8-2. The sheath flows were implemented to focus the particles near one side of the channel. When the particles entered the DEP separation region, they were displaced by this force according to their size and sorted in the outlets. The design of the DEP separation region is based on the findings of our previous studies (chapters 5, 6, and 7). Although these studies have not shown explicitly the DEP-based separation of a mixture of particles (which is the subject of this paper), their results revealed the criteria required in designing high throughput LOC devices for separation of 5, 10, and 15 μm particles from each other. The sheath-assisted design, shown in Figure 8-2(a), consists of a sample inlet in the middle with one strong (high flow rate) sheath flow and one weak (low flow rate) sheath flow inlet on the sides. A series of slanted interdigitated electrodes are included in the design to serve as the DEP separation 116 region. The gap between the electrodes and the width of the electrodes is 12 μm and 60 μm, respectively, which can apply a strong DEP force on microparticles in the proposed 25-μm tall channel. The electrodes are slanted at 10 degrees (𝜃 = 10°) with respect to the flow direction since it is reported that the size-based separation of microparticles is enhanced at this angle. The length of the DEP region was 7 mm as our experiments with single-size particles suggested that 5, 10, and 15 μm particles can be positioned at separate regions along the width of the channel. The sheathless design consists of only one inlet to infuse the sample to the channel. In the absence of sheath flows, a DEP focusing region is added to focus all the three particle sizes close to the side of the channel before entering the separation region, see Figure 8-2(b). The electrode gap, width, and angle are the same as the electrodes for the sheath-assisted design. The length of the DEP focusing region is 18 mm to focus all the particles effectively to a narrow region close to the wall. Using a syringe pump, the particles are infused into the channel. Close to the inlet, the particles are randomly distributed along the width of the channel. When the particles enter the focusing region, they get affected by the DEP force and are pushed toward the bottom wall of the channel. Then, the particles enter the separation region where they are separated from each other based on their sizes. Each size of the particles is collected in one of the outlets. The weak sheath-assisted device, shown in Figure 8-2(c), is a combination of the two aforementioned devices. Like the sheathless DEP separator, it consisted of the DEP focusing and separation regions. The difference is that the two sheath flows are added to the design to make a distance between the randomly-entered particles to the channel and the channel wall. In summary, the length of the DEP separation and focusing (if any) regions were 7 mm and 18 mm in all the devices, and the electrodes were deflected at 10 degrees. 117 Figure 8-2 (a) The schematics of the proposed sheath-assisted DEP separator. The 5 𝜇m (blue), 10 𝜇m (red), and 15 𝜇m (green) particles are separated. (b) The sheathless DEP separator and (c) the weak sheath-assisted DEP separator. 8.4 Experiments 8.4.1 Device fabrication Following the standard lithography procedures which are explained in chapter 3 in detail, the electrodes were patterned on a glass substrate, as shown in Figure 8-3(a). 118 Figure 8-3 (a) Examples of the fabricated electrodes. (b) An example of the SU 8 mold for the sheathless design. (c) The PDMS channel for the sheathless device. Su8 molds for each one of the designs were fabricated following the procedures explained in chapter 3. One of the fabricated molds and PDMS channels are presented in Figure 8-3(b) and (c). The PDMS channel and glass substrate were plasma treated by using an oxygen plasma machine. After careful alignment, the PDMS and glass were bonded to each other. Examples of the DEP microseparators are depicted in Figure 8-4 and Figure 8-5. Food color was injected into the channel for better visualization of the channel. Figure 8-4 (a) The sheathless DEP microseparator and (b) the close-up view. 119 Figure 8-5 The sheath-assisted DEP microseparator. 8.4.2 Sample preparation The channel is washed with a solution of deionized (DI) water and 0.1% TWEEN 20 for about 30 minutes. The same solution is used as the sheath flow. The sample consists of the DI water, 0.1% (v/v) TWEEN 20, the 2% (v/v) polystyrene particles, and 20% (v/v) glycerol for density adjustment of the particles with the solution. The conductivity of the solution is measured as 20.3 𝜇S/cm. The permittivity of the polystyrene particles and DI water is 2.4 and 76.4, respectively 287. The permittivity of the solution is assumed to be the same as the DI water. 8.5 Results 8.5.1 Binary particle separation The capability of the DEP microseparator for binary separation of the particles was investigated. For this purpose, a sheath flow focuses on all the particles close to one side of the channel. The sheath flow was adjusted to be three-time stronger (flow rate of the sheath flow = 3 × flow rate of the sample). 10 Volts (peak to peak) was applied to the electrodes and the frequency was set to 10 MHz. Examples of the particles entering the channel and being separated at the outlet are depicted in Figure 8-6 (a-d). 120 Figure 8-6 Two sizes of polystyrene microparticles were infused into the device and separated from each other. Examples of 5 and 8 𝜇m particles (a), 8 and 10 𝜇m particles (b), 8 and 15 𝜇m particles (c), and 10 and 15 𝜇m particles (d) being separated near the outlets of the device is presented. 8.5.2 Ternary particle separation The proposed design in Figure 1 (a) was used to separate a mixture of particles of three sizes. The particles and the sheath flows were infused into the separation chamber at a flow rate ratio of 1, 1, 8 for the weak sheath flow, sample, and strong sheath flow, respectively. Before applying the electric potential to the electrodes, all the particles were focused in a narrow region due to the sheath flows. By applying the electric potential, particles were affected by the DEP force. Therefore, they levitated to the top of the channel and were laterally displaced toward the other side of the microchannel. At low flow rates, all the particles were moved laterally all the way toward the left outlet. By increasing the flow rate, the 5 𝜇m particles were separated from 10 and 15 𝜇m particles and collected in the middle outlet. Further increase in the flow rate to about 4.7 𝜇L/min led to the successful separation of the particles based on their size (see Figure 8-7). At this flow rate, 96.6% of the 5 𝜇m particles, 93.4 % of the 10 𝜇m particles, and 98% of the 15 𝜇m particles were collected in the rights, middle, and left outlets, respectively. The percentage of each particle size in each of the outlets is shown in Figure 8-9 (a). At higher flow rates (higher than 4.7 𝜇L/min), the lateral deflection of the particles decreased, and eventually, all the particles were collected in the right outlet. 121 Figure 8-7 Sheath-assisted design successfully separated polystyrene microparticles. The 5, 10, and 15 𝜇m particles were collected in the “Right outlet”, “Middle outlet”, and “Left outlet”, respectively. To enhance the throughput of the device, the sheath flow was removed from the design, and a DEP focusing region was added, see Figure 8-2 (c). The particles were injected into the chamber and were randomly distributed along the width of the channel (Figure 8-8 (a)). The DEP force was used to focus all the particles close to the side of the channel (Figure 8-8 (b)). Then, the particles entered the separation region, Figure 8-8 (c). After passing over the separation region, the particles were separated based on their sizes. An example of the particles reaching the outlets is shown in Figure 8-8 (d). The proper adjustment of the geometrical parameters of the device is crucial for this design. In essence, the particles can be separated only at specific volumetric throughputs. At that flow rate, the focusing region should be long enough to focus all the sizes of the particles. Otherwise, effective separation is not possible. From the previous design, we found out the flow rate of 4.7 𝜇L/min is appropriate for separation, and hence, the same flow rate was used for the sheathless separation design. Of course, the length of the focusing region was selected as 18 mm to use the same flow rate of 4.7 𝜇L/min for the DEP focusing part. 122 Figure 8-8 (a) 5, 10, and 15 𝜇m particles were injected into the channel from the inlet. (b) After passing over the focusing electrodes, the particles were focused. (c) The particle passing over the separation region. (d) After the separation region, the 5, 10, and 15 𝜇m particles were separated based on their size, and collected in the “Right outlet”, “Middle outlet”, and “Left outlet”, respectively. Besides the elimination of the sheath flows which simplifies the operation of the device, another advantage of the DEP focusing is the narrower focusing width of the particles. The narrower the width of focusing the greater the role of DEP in the final position of the particles. On the other hand, the drawback of the sheath-less microseparator is the lower yield in the outlets (see Figure 8-9 (b)) although the role of the DEP separation is more in the case of the sheathless design. The reason for the lower yield is due to those particles which entered the channel close to the PDMS channel wall (almost around 5-7% observed in different iterations). The DEP force at those regions is not strong enough to manipulate 123 the particles and laterally displace them. Therefore, these particles were collected on the side outlets, resulting in lower separation yields of the collected samples. In order to solve this issue, the weak sheath-assisted design was proposed (see Figure 8-2 (d)). This design is a combination of sheath-assisted focusing and DEP-based focusing designs. Weak sheath flows were used to keep a distance between the particles and the PDMS channel. Our experiments showed that sheath flows that were as strong as 10% of the total flow rate, can keep a 40 𝜇m distance between the particles and the channel. Therefore, the DEP force can effectively manipulate the particles to focus them all and then separate them from each other based on their sizes. Like the previous cases, a total of 4.7 𝜇L/min, which included 90% of the sample and 10% of the sheath flows were infused into the channel. The results showed that the 10% sheath flow was strong enough to keep all the particles in the DEP effective region of the channel. The separation yield of the 15, 10, and 5𝜇m particles were 98.7%, 97.1%, and 96.7%, respectively. The distribution of the particles in each one of the outlets is presented in Figure 8-9 (c). In order to solve this issue, the weak sheath-assisted design was proposed. This design is a combination of sheath-assisted focusing and DEP-based focusing designs. The weak sheath flows were used to keep a distance between the particles and the PDMS channel. There the DEP force can effectively manipulate the particles to focus them all and then separate them from each other based on size. Like the previous cases, a total of 4.7 𝜇L/min, which included 90% of the sample and 10% of the sheath flows were infused into the channel. The results showed that the 10% sheath flow was strong enough to keep all the particles in the DEP effective region of the channel. The purity of the left, middle, and right outlets were 98.7%, 87.1%, and 96.7%, respectively. The distribution of the particles in each one of the outlets is presented in Figure 8-9 (c). 124 Figure 8-9 The percentage of the 5, 10, and 15 𝜇m microparticles collected in each one of the outlets for the (a) sheath-assisted, (b) sheath-less, and (c) weak sheath-assisted microseparators. 8.6 Discussions The results showed the capability of the DEP-based microseparator for size-based separation of cell representative particles. The results showed that the sheath-assisted design separated 5, 10, and 15 𝜇m particles with a higher separation efficiency compared to the sheathless DEP separator. This enhancement is less than 7% for each one of the particle sizes, which was achieved with the cost of losing 90% of the flow rate of the device. Therefore, the sample volumetric throughput of the sheathless DEP separator was 10 times larger than the sheath-assisted DEP separator. Besides, the elimination of the sheath flow is an advantage due to the simpler and cheaper operation of the device. The lower purity rate of the sheathless DEP separator was attributed to the particles that were not affected by the DEP force. These particles were too close to the PDMS channel where the DEP force is not strong enough to displace the particles along the width of the channel. To address this issue, another DEP separator was designed that used the DEP for focusing and two weak sheath flows (10% of the total flow rate) to keep the particles within the DEP effective region of the channel. The latter device showed the highest purity rate of the collected particles. In other words, compared to the sheath-assisted DEP separator, the sample volumetric throughput of this device was 9 125 times larger and it also separated the particles more efficiently. The only downside may be the extra 14 mm length of the device for the DEP-based focusing region. The weak sheath-assisted DEP separator improved the separation efficiency of the sheathless DEP separator by up to 11.2%. However, the sample volumetric throughput was 10% decreased and more importantly, the simplicity of the operation of the device was affected by the addition of the sheath flows. 8.7 Summary In this chapter, three different devices were fabricated for sheath-assisted and sheathless separation of the polystyrene microspheres. The capability of the developed device for size-based separation of microparticles was presented. By implementing sheath flows, two different sizes of the particles, with a minimum of 20% size difference (8 and 10 𝜇m) were successfully separated from each other with high purities in the outlets. The same technique was used to separate 5, 10, and 15 𝜇m particles from each other. The tests with the sheath-assisted design showed the capability of the proposed device for triple size particle separation. This design separated the particle with purities in the range of 93.4% to 98%. However, this device suffers from a low sample throughput. The sheathless device processed a higher volumetric throughput (4.7 𝜇L/min), 9 times greater than the sheath assisted design. The only downside was the loss of those particles that were too close to the channel wall to be affected by the DEP force. Therefore, two sheath flows were added to the design, and with a minor loss in the volumetric throughput, the highest sample purity in the outlets were achieved. 126 Chapter 9: Conclusions 9.1 Summary This thesis has focused on the manipulation and separation of microparticles on the LOC platform based on the DEP force. In recent years, advances in LOC devices have progressed them toward micro total analysis systems (𝜇TAS). One of the steps towards this goal is the development of an effective and efficient method for manipulating particles, biological cells, bacteria, and other micro and nano-size particles through active or passive methods. As mentioned before, the active methods provide better control on the cells, can separate the cells based on more specific properties (rather than just the size), and can be tuned in real-time. Among the active methods, DEP is label-free, cost-effective, and functions based on the application of an electric field (which is part of many functions and operations in LOC devices). A specific type of DEP-based microseparator functions based on slanted planar electrodes. These devices are suitable for mass production from both technological and economical points of view. Despite the promising advantages provided by the microfluidic devices, more efforts are still required to characterize the performance of such devices. In this thesis, several studies were conducted to improve the performance of DEP-based LOCs to manipulate and separate microparticles. A conclusive summary of the studies is provided here. 9.1.1 Sheath-assisted focusing of particles Focusing the particles before sorting and separating the particles from each other is a necessary process in microfluidics chips. Sheath flows are the most common approach to reach this goal. Therefore, exploring the effective parameters for cells/particles focusing enables us to improve the desired output of LOC devices. Using COMSOL Multiphysics software, Y junctions were modeled and validated by comparing them with our experimental data. Using the model, the effects of the sheath flow ratio (the sheath flow over the total flow rate), width ratio (the width of the sheath inlet over the total width), junction angles, and particles sizes on the focusing width and the distribution of the particles within the focusing region were studied. Then, two generalized linear models predicting the focusing 127 width of the particles and the standard deviation of the position of the particles were developed. According to the results, the focusing width is mostly affected by the sheath flow rate ratio and to some extent by the flow rate and the particle size. Further, the flow rate ratio, width ratio, and particle size are the decisive factors on the standard deviation of the position of the particles. Our results provided a better understanding of how the device geometrical and operational factors affect the position of the particles in the development of high-performance on-chip sensing and sorting of both cells and particles. 9.1.2 Effect of interdigitated electrodes Planar interdigitated electrodes create a non-uniform electric field, which exerts the force on the microparticles. The width of the electrodes and the gap between them can affect the electric field and the resulting DEP force. Besides, the angle at which they are deflected with respect to the direction of the flow affects the DEP force that is applied to the particles. To study the effect of the electrode width and spacing, the electric field was simulated by COMSOL Multiphysics. It was shown that decreasing the electrode spacing leads to a stronger DEP force. Also, depending on the channel height, there was an optimized electrode width, which created the strongest DEP force. As an example (showing the importance of the electrode geometry), the DEP force can increase up to 198% by the proper selection of the electrode width (110 μm) and spacing (10 μm) for a 75-μm channel height. 9.1.3 Effect of electric parameters The amplitude of the voltage affects the electric field and the induced DEP force. Also, the frequency of the sinusoidal voltage affects the CM factor. To show how the voltage affects the DEP-based manipulation of cells and particles, a series of experiments were conducted for a variety of particle sizes and channel heights. It was shown that the voltage squared and the flow rate at which particles are effectively moved to the other side of the channel are related. Also, the DEP force is linearly related to both voltage squared and CM factor. Therefore, it can be concluded that the effective flow rate is related to the CM factor. For instance, decreasing the absolute value of the CM factor, e.g. through manipulation 128 of the frequency of the AC voltage, is expected to have the same effect as decreasing the voltage squared, which will lead to lower volumetric throughput. 9.1.4 Effect of geometrical and operational parameters Understanding the effects of fabrication and performance parameters on the separation efficiency can further advance applications of LOC devices in life sciences. A parametric study on the lateral displacement of the cell-representative polystyrene particles in continuous dielectrophoresis (DEP) based microseparator was conducted. The effects of the fabrication parameters (i.e., the channel length and width, and the deflection angle of the electrodes), as well as the operational factors (i.e., the volumetric throughput and particle size) on the DEP-induced displacement, were studied. The results showed that the volumetric throughput has the most contribution to the position of the particles. Among the geometrical parameters, the channel length was found to be the most effective factor in the particle position in the outlet, while the effect of the channel width was shown to be negligible. The results of this research rendered the desired separation performance to be attained via the design of low-angle slanted electrodes alongside with a balance between the volumetric throughput and the length of the DEP region. These findings provide valuable insights into the design of microfluidic platforms to selectively separate different cell types in biofluids. 9.1.5 Particle separation The LOC platform, for which the parametric study was conducted, was used for the separation of binary and triple particle sizes. Sheath flows were implemented to separate 5 and 8 𝜇m, 5 and 10 𝜇m, 8 and 10 𝜇m, 8 and 15 𝜇m, as well as 10 and 15 𝜇m polystyrene particles. Also, three sizes of the particles (5, 10, and 15 𝜇m) were separated successfully. To increase the throughput of the device, a sheath-less approach was implemented for particle separation. In such a device, the particles were randomly entered into a focusing region, where they were affected by the DEP force. The length of this focusing region was designed long enough to be able to focus all the particles close to the side of the channel. Then, the particles passed over the separation region, which was shorter than the focusing region, were separated 129 based on their size. The length of the focusing and separation regions were carefully designed to achieve a high throughput device, which was capable of separating the particles with high purity in each outlet. 9.2 Contributions to the field The findings of this research can widely benefit the field of microfluidics and in particular on-chip separation of cells and particles to achieve better performance. A summary of the contributions of this research is listed below: • The importance of the geometry of the interdigitated electrodes on the manipulation of cells and particles were demonstrated. Design criteria were presented for enhancing the DEP strength with no additional cost or fabrication difficulties (a more powerful DEP force means that the cells or particles can be effectively manipulated without applying a stronger electric potential). This will decrease Joule heating in the system as well as the chance of electrolysis and will eventually lead to higher cell viability and more durable and portable LOC devices. • The parametric study conducted on the geometrical and operational parameters of sheath-assisted focusing has a great impact on on-chip cell separation as well as cell sorting technologies. In particular, the findings of this research can contribute to higher throughput and enhanced performance of these technologies. • It is shown that decreasing the channel height has a great impact on the effectiveness of the DEP force for cell and particle manipulation. As the height of the channel decreases the DEP force can more effectively displace cells or particles. However, it should be noted that the height to the width ratio of the PDMS channel is recommended to be greater than 0.05. Having this limitation in mind, the aspect ratio of 0.05 must be set to achieve the most effective DEP manipulation of cells or particles when interdigitated electrodes are used. • The study on the DEP-induced displacement of the particles uncovered the great potential of the developed device for the size-based sorting of cells and particles. The role of length of the DEP 130 region, the deflection angle of the electrodes, and volumetric throughput on the manipulation of different sizes of the particles are thoroughly investigated and design guidelines are provided. 9.3 Future works • The next step towards a point-of-care particle separator and detector is to integrate separation and sensing into a single chip. For the cell sensing purpose, different methods are used. Among these methods, impedance-based flow cytometry has shown a great potential for cell and particle sensing, and just like DEP, it works based on the electric field. A comprehensive theoretical study of the effective parameters on cell sensing is needed. COMSOL Multiphysics can be used to gain a better understanding of the sensing methods and the expected responses for different configurations. The impedance response is highly dependent on the applied frequency. Therefore, the sensor response should be investigated in different frequencies to find a range in which the sensor is sensitive. The combination of sorting and sensing can lead to a cost-effective, portable point-of-care device, which is most suitable in remote areas. • The aforementioned method for separating particles based on their sizes can be applied to the cells if their sizes are different enough. If not, DEP-based separation can be achieved based on the dielectric properties of the cells. The basic idea here is to separate the cells based on their dielectric properties. This method can be useful for the separation of two types of cells. Another effective factor in the DEP force is the dielectric properties of the media. Changing the media can be a solution for the single-stage separation of three cells. For this purpose, the channels should be modified so that we have three inlets and outlets as shown in Figure 9-1. Two types of cells (e.g., the blue and green cells) are affected by the nDEP force and move laterally upward. As a result, the red cells are separated. The other cells enter new media with different dielectric properties. Since the media changes, the resultant DEP force on the cells changes. The dielectric properties of the new media should be accurately adjusted to make the DEP force equal to zero 131 on one type of cell. In the meantime, the other cell (e.g., the green cells) should be affected by nDEP so that they are transported laterally and separated from the blue cell. Figure 9-1 A schematic of the proposed design for multi-cell separation. 132 References 1. Y. Cheng, X. Ye, Z. Ma, S. Xie and W. Wang, Biomicrofluidics, 2016, 10, 014118. 2. E. L. Jackson and H. Lu, Current opinion in chemical engineering, 2013, 2, 398-404. 3. P. Li, Z. Mao, Z. Peng, L. Zhou, Y. Chen, P.-H. Huang, C. I. Truica, J. J. Drabick, W. S. El-Deiry and M. Dao, Proceedings of the National Academy of Sciences, 2015, 112, 4970-4975. 4. L. Jeanbart and M. A. Swartz, Proceedings of the National Academy of Sciences, 2015, 112, 14467-14472. 5. M. A. Swartz, S. Hirosue and J. A. Hubbell, Science translational medicine, 2012, 4, 148rv149-148rv149. 6. V. Plaks, C. D. Koopman and Z. Werb, Science, 2013, 341, 1186-1188. 7. M. Cristofanilli, G. T. Budd, M. J. Ellis, A. Stopeck, J. Matera, M. C. Miller, J. M. Reuben, G. V. Doyle, W. J. Allard and L. W. Terstappen, New England Journal of Medicine, 2004, 351, 781-791. 8. C. Sun, Y.-P. Hsieh, S. Ma, S. Geng, Z. Cao, L. Li and C. Lu, Scientific reports, 2017, 7, 40632. 9. W. Huang, C.-L. Chang, N. D. Brault, O. Gur, Z. Wang, S. I. Jalal, P. S. Low, T. L. Ratliff, R. Pili and C. A. Savran, Lab on a Chip, 2017, 17, 415-428. 10. W. Lee, P. Tseng and D. Di Carlo, in Microtechnology for Cell Manipulation and Sorting, Springer, 2017, pp. 1-14. 11. E. Samiei, M. Tabrizian and M. Hoorfar, Lab on a Chip, 2016, 16, 2376-2396. 12. J. P. Lafleur, A. Jönsson, S. Senkbeil and J. P. Kutter, Biosensors and Bioelectronics, 2016, 76, 213-233. 13. Y. Temiz, R. D. Lovchik, G. V. Kaigala and E. Delamarche, Microelectronic Engineering, 2015, 132, 156-175. 14. E. Samiei, G. S. Luka, H. Najjaran and M. Hoorfar, Biosensors and Bioelectronics, 2016, 81, 480-486. 15. E. Samiei and M. Hoorfar, in Miniature Fluidic Devices for Rapid Biological Detection, Springer, 2018, pp. 171-205. 16. W. Hu, Z. Lu, Y. Liu, T. Chen, X. Zhou and C. M. Li, Lab on a Chip, 2013, 13, 1797-1802. 17. E. W. Esch, A. Bahinski and D. Huh, Nature reviews Drug discovery, 2015, 14, 248-260. 18. K.-H. Lee, J. Lee and S.-H. Lee, Lab on a Chip, 2015, 15, 3822-3837. 19. K. Choi, A. H. Ng, R. Fobel and A. R. Wheeler, Annual review of analytical chemistry, 2012, 5, 413-440. 20. E. Samiei and M. Hoorfar, Journal of Micromechanics and Microengineering, 2015, 25, 055008. 21. E. Samiei, M. D. de Leon Derby, A. Van den Berg and M. Hoorfar, Lab on a Chip, 2017, 17, 227-234. 22. H. R. Nejad, E. Samiei, A. Ahmadi and M. Hoorfar, RSC Advances, 2015, 5, 35966-35975. 23. C.-J. Liao, C.-H. Hsieh, H.-M. Wang, W.-P. Chou, T.-K. Chiu, J.-H. Chang, A. C. Chao and M.-H. Wu, RSC Advances, 2017, 7, 29339-29349. 24. Q. D. Tran, T. F. Kong, D. Hu, Marcos and R. H. W. Lam, Lab on a Chip, 2016, 16, 2813-2819. 25. Z. H. Fan and D. J. Beebe, Lab on a Chip, 2014, 14, 12-13. 26. M. E. Warkiani, G. Guan, K. B. Luan, W. C. Lee, A. A. S. Bhagat, P. Kant Chaudhuri, D. S.-W. Tan, W. T. Lim, S. C. Lee, P. C. Y. Chen, C. T. Lim and J. Han, Lab on a Chip, 2014, 14, 128-137. 27. S. Zheng, H. Lin, J.-Q. Liu, M. Balic, R. Datar, R. J. Cote and Y.-C. Tai, Journal of chromatography A, 2007, 1162, 154-161. 28. H. K. Lin, S. Zheng, A. J. Williams, M. Balic, S. Groshen, H. I. Scher, M. Fleisher, W. Stadler, R. H. Datar and Y.-C. Tai, Clinical Cancer Research, 2010, 16, 5011-5018. 133 29. X. Li, W. Chen, G. Liu, W. Lu and J. Fu, Lab on a Chip, 2014, 14, 2565-2575. 30. X. Fan, C. Jia, J. Yang, G. Li, H. Mao, Q. Jin and J. Zhao, Biosensors and Bioelectronics, 2015, 71, 380-386. 31. Y.-T. Kang, I. Doh and Y.-H. Cho, Biomedical microdevices, 2015, 17, 45. 32. J. A. Hernández-Castro, K. Li, A. Meunier, D. Juncker and T. Veres, Lab on a Chip, 2017, 17, 1960-1969. 33. S. Rawal, Z. Ao, R. H. Datar and A. Agarwal, in Circulating Tumor Cells, Springer, 2017, pp. 93-105. 34. Y. J. Kim, Y.-T. Kang and Y.-H. Cho, Analytical chemistry, 2016, 88, 7938-7945. 35. T. F. Didar, K. Li, T. Veres and M. Tabrizian, Biomaterials, 2013, 34, 5588-5593. 36. J. Alvankarian, A. Bahadorimehr and B. Y. Majlis, Biomicrofluidics, 2013, 7, 014102. 37. X. Chen, C. C. Liu and H. Li, Sensors and Actuators B: Chemical, 2008, 130, 216-221. 38. N. J. Panaro, X. J. Lou, P. Fortina, L. J. Kricka and P. Wilding, Biomolecular engineering, 2005, 21, 157-162. 39. I. Doh, H.-i. Yoo, Y.-H. Cho, J. Lee, H. Kwan Kim and J. Kim, Applied Physics Letters, 2012, 101, 043701. 40. Y.-T. Kang, I. Doh, J. Byun, H. J. Chang and Y.-H. Cho, Theranostics, 2017, 7, 3179. 41. C.-C. Wu, L.-Z. Hong and C.-T. Ou, Journal of Medical and Biological Engineering, 2012, 32, 163-168. 42. P. Wilding, L. J. Kricka, J. Cheng, G. Hvichia, M. A. Shoffner and P. Fortina, Analytical biochemistry, 1998, 257, 95-100. 43. T. A. Crowley and V. Pizziconi, Lab on a Chip, 2005, 5, 922-929. 44. G. Stemme and G. Kittilsland, Applied physics letters, 1988, 53, 1566-1568. 45. G. Kittilsland, G. Stemme and B. Nordén, Sensors and Actuators A: Physical, 1990, 23, 904-907. 46. K. Aran, A. Fok, L. A. Sasso, N. Kamdar, Y. Guan, Q. Sun, A. Ündar and J. D. Zahn, Lab on a Chip, 2011, 11, 2858-2868. 47. P. Sajeesh and A. K. Sen, Microfluidics and nanofluidics, 2014, 17, 1-52. 48. H. M. Ji, V. Samper, Y. Chen, C. K. Heng, T. M. Lim and L. Yobas, Biomedical microdevices, 2008, 10, 251-257. 49. Y.-Y. Chiu, C.-K. Huang and Y.-W. Lu, Biomicrofluidics, 2016, 10, 011906. 50. Y. Yoon, S. Kim, J. Lee, J. Choi, R.-K. Kim, S.-J. Lee, O. Sul and S.-B. Lee, Scientific reports, 2016, 6. 51. G. Segre, Nature, 1961, 189, 209-210. 52. P. Saffman, Journal of fluid mechanics, 1965, 22, 385-400. 53. B. Ho and L. Leal, Journal of fluid mechanics, 1974, 65, 365-400. 54. D. Di Carlo, D. Irimia, R. G. Tompkins and M. Toner, Proceedings of the National Academy of Sciences, 2007, 104, 18892-18897. 55. E. S. Asmolov, Journal of Fluid Mechanics, 1999, 381, 63-87. 56. S. C. Hur, N. K. Henderson-MacLennan, E. R. McCabe and D. Di Carlo, Lab on a Chip, 2011, 11, 912-920. 57. J.-S. Park, S.-H. Song and H.-I. Jung, Lab on a Chip, 2009, 9, 939-948. 58. E. Ozkumur, A. M. Shah, J. C. Ciciliano, B. L. Emmink, D. T. Miyamoto, E. Brachtel, M. Yu, P.-i. Chen, B. Morgan and J. Trautwein, Science translational medicine, 2013, 5, 179ra147-179ra147. 59. J. Oakey, R. W. Applegate Jr, E. Arellano, D. D. Carlo, S. W. Graves and M. Toner, Analytical chemistry, 2010, 82, 3862-3867. 60. E. W. Kemna, R. M. Schoeman, F. Wolbers, I. Vermes, D. A. Weitz and A. van den Berg, Lab on a Chip, 2012, 12, 2881-2887. 61. H. W. Hou, M. E. Warkiani, B. L. Khoo, Z. R. Li, R. A. Soo, D. S.-W. Tan, W.-T. Lim, J. Han, A. A. S. Bhagat and C. T. Lim, Scientific reports, 2013, 3, 1259. 134 62. T. H. Kim, H. J. Yoon, P. Stella and S. Nagrath, Biomicrofluidics, 2014, 8, 064117. 63. J. M. Burke, R. E. Zubajlo, E. Smela and I. M. White, Biomicrofluidics, 2014, 8, 024105. 64. S. S. Kuntaegowdanahalli, A. A. S. Bhagat, G. Kumar and I. Papautsky, Lab on a Chip, 2009, 9, 2973-2980. 65. M. Robinson, H. Marks and G. L. Coté, 2016. 66. A. Kulasinghe, T. H. P. Tran, T. Blick, K. O’Byrne, E. W. Thompson, M. E. Warkiani, C. Nelson, L. Kenny and C. Punyadeera, Scientific reports, 2017, 7, 42517. 67. S. Shen, C. Tian, T. Li, J. Xu, S.-W. Chen, Q. Tu, M.-S. Yuan, W. Liu and J. Wang, Lab on a Chip, 2017, 17, 3578-3591. 68. L. M. Lee, J. M. Rosano, Y. Wang, G. Klarmann, C. J. Garson, B. Prabhakarpandian, K. Pant, L. M. Alvarez and E. Lai, Analytical Methods, 2018. 69. B. C. Syverud, E. Lin, S. Nagrath and L. M. Larkin, Tissue Engineering Part C: Methods, 2018, 24, 32-41. 70. B. Kwak, J. Lee, J. Lee, H. S. Kim, S. Kang and Y. Lee, Biosensors and Bioelectronics, 2018, 101, 311-316. 71. H. Song, J. M. Rosano, Y. Wang, C. J. Garson, B. Prabhakarpandian, K. Pant, G. J. Klarmann, L. M. Alvarez and E. Lai, Microfluidics and Nanofluidics, 2017, 21, 64. 72. M. Rafeie, J. Zhang, M. Asadnia, W. Li and M. E. Warkiani, Lab on a Chip, 2016, 16, 2791-2802. 73. S. Shen, C. Ma, L. Zhao, Y. Wang, J.-C. Wang, J. Xu, T. Li, L. Pang and J. Wang, Lab on a Chip, 2014, 14, 2525-2538. 74. J.-S. Park and H.-I. Jung, Analytical chemistry, 2009, 81, 8280-8288. 75. Z. Wu, Y. Chen, M. Wang and A. J. Chung, Lab Chip, 2016, 16, 532-542. 76. M. G. Lee, S. Choi and J.-K. Park, Journal of Chromatography A, 2011, 1218, 4138-4143. 77. Q. Zhao, D. Yuan, S. Yan, J. Zhang, H. Du, G. Alici and W. Li, Microfluidics and Nanofluidics, 2017, 21, 55. 78. Q. Zhao, J. Zhang, S. Yan, D. Yuan, H. Du, G. Alici and W. Li, Scientific reports, 2017, 7, 41153. 79. A. J. Chung, D. R. Gossett and D. Di Carlo, Small, 2013, 9, 685-690. 80. J. Zhang, S. Yan, W. Li, G. Alici and N.-T. Nguyen, RSC Advances, 2014, 4, 33149-33159. 81. J. Zhang, S. Yan, R. Sluyter, W. Li, G. Alici and N.-T. Nguyen, Scientific reports, 2014, 4. 82. D. Di Carlo, J. F. Edd, D. Irimia, R. G. Tompkins and M. Toner, Analytical chemistry, 2008, 80, 2204-2211. 83. T. Jin, S. Yan, J. Zhang, D. Yuan, X.-F. Huang and W. Li, Biomicrofluidics, 2016, 10, 034104. 84. S. Choi, S. Song, C. Choi and J.-K. Park, Analytical chemistry, 2008, 81, 50-55. 85. J. He, M. Huang, D. Wang, Z. Zhang and G. Li, Journal of Pharmaceutical and Biomedical Analysis, 2014, 101, 84-101. 86. L. R. Huang, E. C. Cox, R. H. Austin and J. C. Sturm, Science, 2004, 304, 987-990. 87. Z. Liu, F. Huang, J. Du, W. Shu, H. Feng, X. Xu and Y. Chen, Biomicrofluidics, 2013, 7, 011801. 88. K. Loutherback, J. D'Silva, L. Liu, A. Wu, R. H. Austin and J. C. Sturm, AIP advances, 2012, 2, 042107. 89. H. Okano, T. Konishi, T. Suzuki, T. Suzuki, S. Ariyasu, S. Aoki, R. Abe and M. Hayase, Biomedical Microdevices, 2015, 17, 59. 90. N. Tottori, T. Nisisako, J. Park, Y. Yanagida and T. Hatsuzawa, Biomicrofluidics, 2016, 10, 014125. 91. C. I. Civin, T. Ward, A. M. Skelley, K. Gandhi, Z. Peilun Lee, C. R. Dosier, J. L. D'Silva, Y. Chen, M. Kim and J. Moynihan, Cytometry Part A, 2016, 89, 1073-1083. 92. K. K. Zeming, T. Salafi, C.-H. Chen and Y. Zhang, Scientific reports, 2016, 6, 22934. 93. T. Krüger, D. Holmes and P. V. Coveney, Biomicrofluidics, 2014, 8, 054114. 135 94. J. McGrath, M. Jimenez and H. Bridle, Lab on a Chip, 2014, 14, 4139-4158. 95. D. W. Inglis, J. A. Davis, R. H. Austin and J. C. Sturm, Lab on a Chip, 2006, 6, 655-658. 96. J. P. Beech, P. Jönsson and J. O. Tegenfeldt, Lab on a Chip, 2009, 9, 2698-2706. 97. D. W. Inglis, N. Herman and G. Vesey, Biomicrofluidics, 2010, 4, 024109. 98. J. P. Beech, S. H. Holm, K. Adolfsson and J. O. Tegenfeldt, Lab on a Chip, 2012, 12, 1048-1051. 99. Z. Zhang, E. Henry, G. Gompper and D. A. Fedosov, The Journal of chemical physics, 2015, 143, 243145. 100. J.-c. Hyun, J. Hyun, S. Wang and S. Yang, Separation and Purification Technology, 2017, 172, 258-267. 101. Y. S. Lubbersen, M. A. I. Schutyser and R. M. Boom, Chemical Engineering Science, 2012, 73, 314-320. 102. J. D'Silva, R. H. Austin and J. C. Sturm, Lab on a Chip, 2015, 15, 2240-2247. 103. K. Loutherback, K. S. Chou, J. Newman, J. Puchalla, R. H. Austin and J. C. Sturm, Microfluidics and Nanofluidics, 2010, 9, 1143-1149. 104. J. Beech, MSc, Lund University, 2005. 105. D. W. Inglis, K. J. Morton, J. A. Davis, T. J. Zieziulewicz, D. A. Lawrence, R. H. Austin and J. C. Sturm, Lab on a Chip, 2008, 8, 925-931. 106. M. Al-Fandi, M. Al-Rousan, M. A. Jaradat and L. Al-Ebbini, Robotics and computer-integrated manufacturing, 2011, 27, 237-244. 107. M. Yamada, M. Nakashima and M. Seki, Analytical chemistry, 2004, 76, 5465-5471. 108. J. Takagi, M. Yamada, M. Yasuda and M. Seki, Lab on a Chip, 2005, 5, 778-784. 109. X. Lu and X. Xuan, Analytical chemistry, 2015, 87, 4560-4565. 110. X. Lu and X. Xuan, Analytical chemistry, 2015, 87, 6389-6396. 111. T. Laurell, F. Petersson and A. Nilsson, Chemical Society Reviews, 2007, 36, 492-506. 112. J. Shi, H. Huang, Z. Stratton, Y. Huang and T. J. Huang, Lab on a Chip, 2009, 9, 3354-3359. 113. Y. Chen, M. Wu, L. Ren, J. Liu, P. H. Whitley, L. Wang and T. J. Huang, Lab on a Chip, 2016, 16, 3466-3472. 114. J. Nam, H. Lim, C. Kim, J. Y. Kang and S. Shin, Biomicrofluidics, 2012, 6, 024120. 115. S. Gupta, D. L. Feke and I. Manas-Zloczower, Chemical Engineering Science, 1995, 50, 3275-3284. 116. F. Petersson, L. Åberg, A.-M. Swärd-Nilsson and T. Laurell, Analytical chemistry, 2007, 79, 5117-5123. 117. D. A. Johnson and D. L. Feke, Separations Technology, 1995, 5, 251-258. 118. J. Shi, X. Mao, D. Ahmed, A. Colletti and T. J. Huang, Lab on a Chip, 2008, 8, 221-223. 119. X. Ding, P. Li, S.-C. S. Lin, Z. S. Stratton, N. Nama, F. Guo, D. Slotcavage, X. Mao, J. Shi and F. Costanzo, Lab on a Chip, 2013, 13, 3626-3649. 120. X. Ding, Z. Peng, S.-C. S. Lin, M. Geri, S. Li, P. Li, Y. Chen, M. Dao, S. Suresh and T. J. Huang, Proceedings of the National Academy of Sciences, 2014, 111, 12992-12997. 121. J. Nam, H. Lim, D. Kim and S. Shin, Lab on a Chip, 2011, 11, 3361-3364. 122. J. Nam, H. Lim, C. Kim, J. Yoon Kang and S. Shin, Biomicrofluidics, 2012, 6, 024120. 123. G. Destgeer, A. Alazzam and H. J. Sung, Journal of Mechanical Science and Technology, 2016, 30, 3945-3952. 124. G. Destgeer, B. H. Ha, J. H. Jung and H. J. Sung, Lab on a Chip, 2014, 14, 4665-4672. 125. J. Behrens, S. Langelier, A. R. Rezk, G. Lindner, L. Y. Yeo and J. R. Friend, Lab on a Chip, 2015, 15, 43-46. 126. Z. Ma, D. J. Collins, J. Guo and Y. Ai, Analytical chemistry, 2016, 88, 11844-11851. 127. W. Ung, K. Mutafopulos, P. Spink, R. W. Rambach, T. Franke and D. A. Weitz, Lab on a Chip, 2017, 17, 4059-4069. 128. K. Park, J. Park, J. H. Jung, G. Destgeer, H. Ahmed and H. J. Sung, Biomicrofluidics, 2017, 11, 064112. 136 129. J. Nam, Y. Lee and S. Shin, Microfluidics and Nanofluidics, 2011, 11, 317-326. 130. Z. Ma, D. J. Collins, J. Guo and Y. Ai, Analytical Chemistry, 2016. 131. G. Destgeer, K. H. Lee, J. H. Jung, A. Alazzam and H. J. Sung, Lab on a Chip, 2013, 13, 4210-4216. 132. K. Wang, W. Zhou, Z. Lin, F. Cai, F. Li, J. Wu, L. Meng, L. Niu and H. Zheng, Sensors and Actuators B: Chemical, 2018, 258, 1174-1183. 133. L. Y. Yeo and J. R. Friend, Annual Review of Fluid Mechanics, 2014, 46, 379-406. 134. Z. Wang and J. Zhe, Lab on a Chip, 2011, 11, 1280-1285. 135. M. P. Hughes, Electrophoresis, 2002, 23, 2569-2582. 136. R. Martinez‐Duarte, P. Renaud and M. J. Madou, Electrophoresis, 2011, 32, 2385-2392. 137. D. Chen, H. Du and C. Tay, Nanoscale research letters, 2010, 5, 55. 138. H. Zhu, X. Lin, Y. Su, H. Dong and J. Wu, Biosensors and Bioelectronics, 2015, 63, 371-378. 139. S. Dash and S. Mohanty, ELECTROPHORESIS, 2014, 35, 2656-2672. 140. M. P. Hughes, Biomicrofluidics, 2016, 10, 032801. 141. L. Chen, X.-L. Zheng, N. Hu, J. Yang, H.-Y. Luo, F. Jiang and Y.-J. Liao, Chinese Journal of Analytical Chemistry, 2015, 43, 300-309. 142. R. Pethig, Biomicrofluidics, 2010, 4, 022811. 143. K. Khoshmanesh, S. Nahavandi, S. Baratchi, A. Mitchell and K. Kalantar-zadeh, Biosensors and Bioelectronics, 2011, 26, 1800-1814. 144. P. Modarres and M. Tabrizian, Sensors and Actuators B: Chemical, 2017, 252, 391-408. 145. H. R. Nejad, O. Z. Chowdhury, M. D. Buat and M. Hoorfar, Lab on a Chip, 2013, 13, 1823-1830. 146. E. Samiei, H. Rezaei Nejad and M. Hoorfar, Applied Physics Letters, 2015, 106, 204101. 147. B. Nestor, E. Samiei, R. Samanipour, A. Gupta, A. Van den Berg, M. D. de Leon Derby, Z. Wang, H. R. Nejad, K. Kim and M. Hoorfar, RSC Advances, 2016, 6, 57409-57416. 148. D. Das, K. Biswas and S. Das, Medical engineering & physics, 2014, 36, 726-731. 149. M. Alshareef, N. Metrakos, E. Juarez Perez, F. Azer, F. Yang, X. Yang and G. Wang, Biomicrofluidics, 2013, 7, 011803. 150. I.-F. Cheng, W.-L. Huang, T.-Y. Chen, C.-W. Liu, Y.-D. Lin and W.-C. Su, Lab on a Chip, 2015, 15, 2950-2959. 151. H. Song, J. M. Rosano, Y. Wang, C. J. Garson, B. Prabhakarpandian, K. Pant, G. J. Klarmann, A. Perantoni, L. M. Alvarez and E. Lai, Lab on a Chip, 2015, 15, 1320-1328. 152. I. Doh and Y.-H. Cho, Sensors and Actuators A: Physical, 2005, 121, 59-65. 153. A. Alazzam, B. Mathew and F. Alhammadi, Journal of separation science, 2017, 40, 1193-1200. 154. Y. Huang, X. B. Wang, F. F. Becker and P. R. Gascoyne, Biophysical Journal, 1997, 73, 1118-1129. 155. M. Camarda, S. Scalese and A. La Magna, Electrophoresis, 2015, 36, 1396-1404. 156. X.-B. Wang, J. Yang, Y. Huang, J. Vykoukal, F. F. Becker and P. R. Gascoyne, Analytical Chemistry, 2000, 72, 832-839. 157. J. Yang, Y. Huang, X.-B. Wang, F. F. Becker and P. R. Gascoyne, Analytical Chemistry, 1999, 71, 911-918. 158. G. H. Markx, J. Rousselet and R. Pethig, Journal of liquid chromatography & related technologies, 1997, 20, 2857-2872. 159. H. Peng, N. T. Alvarez, C. Kittrell, R. H. Hauge and H. K. Schmidt, Journal of the American Chemical Society, 2006, 128, 8396-8397. 160. J. Čemažar and T. Kotnik, Electrophoresis, 2012, 33, 2867-2874. 161. J. Yang, Y. Huang, X.-B. Wang, F. F. Becker and P. R. Gascoyne, Biophysical Journal, 2000, 78, 2680-2689. 162. S. Shim, P. Gascoyne, J. Noshari and K. S. Hale, Integrative Biology, 2011, 3, 850-862. 137 163. J. Vykoukal, D. M. Vykoukal, S. Freyberg, E. U. Alt and P. R. Gascoyne, Lab on a Chip, 2008, 8, 1386-1393. 164. Y. Huang, X.-B. Wang, F. F. Becker and P. Gascoyne, Biophysical journal, 1997, 73, 1118-1129. 165. P. R. Gascoyne, J. Noshari, T. J. Anderson and F. F. Becker, Electrophoresis, 2009, 30, 1388-1398. 166. B. Mathew, A. Alazzam, M. Abutayeh and I. Stiharu, Journal of Separation Science, 2016, 39, 3028-3036. 167. B. Mathew, A. Alazzam, M. Abutayeh, A. Gawanmeh and S. Khashan, Chemical Engineering Science, 2015, 138, 266-280. 168. N. Piacentini, G. Mernier, R. Tornay and P. Renaud, Biomicrofluidics, 2011, 5, 034122. 169. X.-B. Wang, J. Vykoukal, F. F. Becker and P. R. Gascoyne, Biophysical journal, 1998, 74, 2689-2701. 170. Y. Huang, J. Yang, X.-B. Wang, F. F. Becker and P. R. Gascoyne, Journal of hematotherapy & stem cell research, 1999, 8, 481-490. 171. S. Shim, K. Stemke-Hale, A. M. Tsimberidou, J. Noshari, T. E. Anderson and P. R. Gascoyne, Biomicrofluidics, 2013, 7, 011807. 172. M. Nikolic-Jaric, S. F. Romanuik, G. A. Ferrier, T. Cabel, E. Salimi, D. B. Levin, G. E. Bridges and D. J. Thomson, Biomicrofluidics, 2012, 6, 024117. 173. M. Şen, K. Ino, J. Ramón-Azcón, H. Shiku and T. Matsue, Lab on a Chip, 2013, 13, 3650-3652. 174. V. V. Swaminathan, M. A. Shannon and R. Bashir, Biomedical microdevices, 2015, 17, 1-9. 175. L. Yang, P. P. Banada, M. R. Chatni, K. S. Lim, A. K. Bhunia, M. Ladisch and R. Bashir, Lab on a Chip, 2006, 6, 896-905. 176. A. Hemmatifar, M. S. Saidi, A. Sadeghi and M. Sani, Microfluidics and nanofluidics, 2013, 14, 265-276. 177. M. S. Pommer, Y. Zhang, N. Keerthi, D. Chen, J. A. Thomson, C. D. Meinhart and H. T. Soh, Electrophoresis, 2008, 29, 1213-1218. 178. K. Khoshmanesh, C. Zhang, F. J. Tovar‐Lopez, S. Nahavandi, S. Baratchi, K. Kalantar‐zadeh and A. Mitchell, Electrophoresis, 2009, 30, 3707-3717. 179. K. Khoshmanesh, S. Baratchi, F. J. Tovar-Lopez, S. Nahavandi, D. Wlodkowic, A. Mitchell and K. Kalantar-Zadeh, Microfluidics and nanofluidics, 2012, 12, 597-606. 180. A. Menachery, J. Burt, S. Chappell, R. Errington, D. Morris, P. Smith, M. Wiltshire, E. Furon and R. Pethig, UNE, 2016, 13, 15. 181. S.-I. Han, Y.-D. Joo and K.-H. Han, Analyst, 2013, 138, 1529-1537. 182. S. Menad, A. El-Gaddar, N. Haddour, S. Toru, M. Brun, F. o. Buret and M. Frenea-Robin, Langmuir, 2014, 30, 5686-5693. 183. Y. Yoshimura, M. Tomita, F. Mizutani and T. Yasukawa, Analytical chemistry, 2014, 86, 6818-6822. 184. Y. Yoshimura, C. Fujii, M. Tomita, F. Mizutani and T. Yasukawa, Chemistry Letters, 2014, 43, 980-981. 185. R. Krishnan, B. D. Sullivan, R. L. Mifflin, S. C. Esener and M. J. Heller, Electrophoresis, 2008, 29, 1765-1774. 186. F. E. Tay, L. Yu, A. J. Pang and C. Iliescu, Electrochimica acta, 2007, 52, 2862-2868. 187. D. Chen, H. Du and W. Li, Sensors and Actuators A: Physical, 2007, 133, 329-334. 188. D. Chen, H. Du and W. Li, Journal of micromechanics and microengineering, 2006, 16, 1162. 189. S. H. Ling, Y. C. Lam and K. S. Chian, Analytical chemistry, 2012, 84, 6463-6470. 190. S. Li, M. Li, Y. S. Hui, W. Cao, W. Li and W. Wen, Microfluidics and nanofluidics, 2013, 14, 499-508. 191. V. Chaurey, A. Rohani, Y. H. Su, K. T. Liao, C. F. Chou and N. S. Swami, Electrophoresis, 2013, 34, 1097-1104. 138 192. C.-H. Chiou, L.-J. Chien and J.-N. Kuo, Applied Physics Express, 2015, 8, 085201. 193. C.-H. Chiou, J.-C. Pan, L.-J. Chien, Y.-Y. Lin and J.-L. Lin, Sensors, 2013, 13, 2763-2776. 194. G. R. Pesch, L. Kiewidt, F. Du and M. Baune, Electrophoresis, 2016, 37, 291-301. 195. L. Shi, A. Rana and L. Esfandiari, Biophysical Journal, 2018, 114, 691a-692a. 196. J. Ding, C. Woolley and M. A. Hayes, Analytical and Bioanalytical Chemistry, 2017, 409, 6405-6414. 197. S. Soltanian‐Zadeh, K. Kikkeri, A. N. Shajahan‐Haq, J. Strobl, R. Clarke and M. Agah, Electrophoresis, 2017. 198. H. R. Gwon, S. T. Chang, C. K. Choi, J. Y. Jung, J. M. Kim and S. H. Lee, Electrophoresis, 2014, 35. 199. H. Shafiee, M. B. Sano, E. A. Henslee, J. L. Caldwell and R. V. Davalos, Lab on a Chip, 2010, 10, 438-445. 200. H. Shafiee, J. L. Caldwell, M. B. Sano and R. V. Davalos, Biomedical microdevices, 2009, 11, 997-1006. 201. A. Rahmani, A. Mohammadi and H. R. Kalhor, Electrophoresis, 2018, 39, 445-455. 202. N. A. M. Yunus, H. Nili and N. G. Green, Electrophoresis, 2013, 34, 969-978. 203. R. Martinez‐Duarte, F. Camacho‐Alanis, P. Renaud and A. Ros, Electrophoresis, 2013, 34, 1113-1122. 204. Y. Yildizhan, N. Erdem, M. Islam, R. Martinez-Duarte and M. Elitas, Sensors, 2017, 17, 2691. 205. R. Martinez-Duarte, R. A. Gorkin III, K. Abi-Samra and M. J. Madou, Lab on a Chip, 2010, 10, 1030-1043. 206. M. Islam, R. Natu, M. F. Larraga-Martinez and R. Martinez-Duarte, Biomicrofluidics, 2016, 10, 033107. 207. N. Pamme, Lab on a Chip, 2007, 7, 1644-1659. 208. T. Y. Lee, K.-A. Hyun, S.-I. Kim and H.-I. Jung, Sensors and Actuators B: Chemical, 2017, 238, 1144-1150. 209. G. Kokkinis, S. Cardoso, F. Keplinger and I. Giouroudi, Sensors and Actuators B: Chemical, 2017, 241, 438-445. 210. T. Deng, G. M. Whitesides, M. Radhakrishnan, G. Zabow and M. Prentiss, Applied physics letters, 2001, 78, 1775-1777. 211. P. Grodzinski, J. Yang, R. H. Liu and M. D. Ward, Biomedical Microdevices, 2003, 5, 303-310. 212. V. I. Furdui and D. J. Harrison, Lab on a Chip, 2004, 4, 614-618. 213. H. Lee, A. Purdon and R. Westervelt, Applied physics letters, 2004, 85, 1063. 214. S. Miltenyi, W. Müller, W. Weichel and A. Radbruch, Cytometry, 1990, 11, 231-238. 215. J.-C. Yu, C.-C. Hu, W.-H. Chang, P.-C. Chen, M. S. Lee, K.-T. Peng and G.-B. Lee, Microfluidics and Nanofluidics, 2018, 22, 13. 216. N. Pamme, J. C. Eijkel and A. Manz, Journal of Magnetism and Magnetic Materials, 2006, 307, 237-244. 217. B. Ngamsom, M. M. N. Esfahani, C. Phurimsak, M. J. Lopez-Martinez, J.-C. Raymond, P. Broyer, P. Patel and N. Pamme, Analytica Chimica Acta, 2016, 918, 69-76. 218. M. Vojtíšek, M. D. Tarn, N. Hirota and N. Pamme, Microfluidics and nanofluidics, 2012, 13, 625-635. 219. N. Pamme and C. Wilhelm, Lab on a Chip, 2006, 6, 974-980. 220. D. W. Inglis, R. Riehn, R. H. Austin and J. C. Sturm, Applied Physics Letters, 2004, 85, 5093-5095. 221. M. D. Tarn and N. Pamme, in Microchip Diagnostics, Springer, 2017, pp. 69-83. 222. A. A. S. Bhagat, H. Bow, H. W. Hou, S. J. Tan, J. Han and C. T. Lim, Medical & Biological Engineering & Computing, 2010, 48, 999-1014. 223. M. Hejazian, W. Li and N.-T. Nguyen, Lab on a Chip, 2015, 15, 959-970. 224. N. Pamme and A. Manz, Analytical chemistry, 2004, 76, 7250-7256. 139 225. M. D. Tarn, N. Hirota, A. Iles and N. Pamme, Science and Technology of Advanced Materials, 2016. 226. R. Cheng, T. Zhu and L. Mao, Microfluidics and Nanofluidics, 2014, 16, 1143-1154. 227. T. Zhu, R. Cheng, Y. Liu, J. He and L. Mao, Microfluidics and Nanofluidics, 2014, 17, 973-982. 228. J. D. Adams, U. Kim and H. T. Soh, Proceedings of the National Academy of Sciences, 2008, 105, 18165-18170. 229. W.-T. Wu, A. B. Martin, A. Gandini, N. Aubry, M. Massoudi and J. F. Antaki, Microfluidics and Nanofluidics, 2016, 20, 41. 230. X. Han, Y. Feng, Q. Cao and L. Li, Microfluidics and Nanofluidics, 2015, 18, 1209-1220. 231. E. Brouzes, T. Kruse, R. Kimmerling and H. H. Strey, Lab on a Chip, 2015, 15, 908-919. 232. B.-Y. Qu, Z.-Y. Wu, F. Fang, Z.-M. Bai, D.-Z. Yang and S.-K. Xu, Analytical and Bioanalytical Chemistry, 2008, 392, 1317. 233. C. B. Fuh, H. Y. Tsai and J. Z. Lai, Analytica Chimica Acta, 2003, 497, 115-122. 234. B. Kwak, J. Lee, D. Lee, K. Lee, O. Kwon, S. Kang and Y. Kim, Biosensors and Bioelectronics, 2017, 88, 153-158. 235. K.-H. Han and A. B. Frazier, Proc. Nanotech, 2005, 2005, 187-190. 236. E. Furlani, Journal of Physics D: Applied Physics, 2007, 40, 1313. 237. J. Jung and K.-H. Han, Applied Physics Letters, 2008, 93, 223902. 238. Y. Jung, Y. Choi, K.-H. Han and A. B. Frazier, Biomedical Microdevices, 2010, 12, 637-645. 239. F. Shen, H. Hwang, Y. K. Hahn and J.-K. Park, Analytical chemistry, 2012, 84, 3075-3081. 240. K.-H. Han and A. B. Frazier, Journal of Applied Physics, 2004, 96, 5797-5802. 241. A. Ashkin, J. Dziedzic, J. Bjorkholm and S. Chu, Optics letters, 1986, 11, 288-290. 242. B. Landenberger, H. Höfemann, S. Wadle and A. Rohrbach, Lab on a Chip, 2012, 12, 3177-3183. 243. D. Baresch, J.-L. Thomas and R. Marchiano, Physical review letters, 2016, 116, 024301. 244. K. Heon Lee, K. Soo Lee, J. Ho Jung, C. Bong Chang and H. Jin Sung, Applied Physics Letters, 2013, 102, 141911. 245. A. H. Yang, S. D. Moore, B. S. Schmidt, M. Klug, M. Lipson and D. Erickson, Nature, 2009, 457, 71-75. 246. M. Dienerowitz, M. Mazilu and K. Dholakia, Journal of Nanophotonics, 2008, 2, 021875-021875-021832. 247. A. Ashkin, Proceedings of the National Academy of Sciences, 1997, 94, 4853-4860. 248. K. Dholakia, P. Reece and M. Gu, Chemical Society Reviews, 2008, 37, 42-55. 249. X. Wang, S. Chen, M. Kong, Z. Wang, K. D. Costa, R. A. Li and D. Sun, Lab on a Chip, 2011, 11, 3656-3662. 250. C.-C. Lin, A. Chen and C.-H. Lin, Biomedical microdevices, 2008, 10, 55-63. 251. M. M. Wang, E. Tu, D. E. Raymond, J. M. Yang, H. Zhang, N. Hagen, B. Dees, E. M. Mercer, A. H. Forster and I. Kariv, Nature biotechnology, 2005, 23, 83-87. 252. M. M. Wang, E. Tu, D. E. Raymond, J. M. Yang, H. Zhang, N. Hagen, B. Dees, E. M. Mercer, A. H. Forster, I. Kariv, P. J. Marchand and W. F. Butler, Nat Biotech, 2005, 23, 83-87. 253. J. Enger, M. Goksör, K. Ramser, P. Hagberg and D. Hanstorp, Lab on a Chip, 2004, 4, 196-200. 254. S. Yan, J. Zhang, C. Pan, D. Yuan, G. Alici, H. Du, Y. Zhu and W. Li, Journal of Micromechanics and Microengineering, 2015, 25, 084010. 255. H.-K. Seo, Y.-H. Kim, H.-O. Kim and Y.-J. Kim, Journal of Micromechanics and Microengineering, 2010, 20, 095019. 256. M. Mizuno, M. Yamada, R. Mitamura, K. Ike, K. Toyama and M. Seki, Analytical chemistry, 2013, 85, 7666-7673. 257. D. Kirby, J. Siegrist, G. Kijanka, L. Zavattoni, O. Sheils, J. O’Leary, R. Burger and J. Ducrée, Microfluidics and nanofluidics, 2012, 13, 899-908. 140 258. F. Del Giudice, H. Madadi, M. M. Villone, G. D'Avino, A. M. Cusano, R. Vecchione, M. Ventre, P. L. Maffettone and P. A. Netti, Lab on a Chip, 2015, 15, 1912-1922. 259. M. J. Kim, D. J. Lee, J. R. Youn and Y. S. Song, RSC Advances, 2016, 6, 32090-32097. 260. Y. Zhou, L. Song, L. Yu and X. Xuan, Microfluidics and Nanofluidics, 2017, 21, 14. 261. V. Kumar and P. Rezai, Biomedical Microdevices, 2017, 19, 39. 262. J. D. Adams, P. Thévoz, H. Bruus and H. T. Soh, Applied physics letters, 2009, 95, 254103. 263. U. Kim and H. T. Soh, Lab on a Chip, 2009, 9, 2313-2318. 264. J. Wu, Q. Yan, S. Xuan and X. Gong, Microfluidics and Nanofluidics, 2017, 21, 47. 265. T. P. Forbes and S. P. Forry, Lab on a Chip, 2012, 12, 1471-1479. 266. W. Zhao, R. Cheng, B. D. Jenkins, T. Zhu, N. E. Okonkwo, C. E. Jones, M. B. Davis, S. K. Kavuri, Z. Hao and C. Schroeder, Lab on a Chip, 2017, 17, 3097-3111. 267. J. Zhang, S. Yan, D. Yuan, Q. Zhao, S. H. Tan, N.-T. Nguyen and W. Li, Lab on a Chip, 2016, 16, 3947-3956. 268. S. Yan, J. Zhang, D. Yuan, Q. Zhao, J. Ma and W. Li, Applied Physics Letters, 2016, 109, 214101. 269. M. Wiklund, C. Günther, R. Lemor, M. Jäger, G. Fuhr and H. M. Hertz, Lab on a Chip, 2006, 6, 1537-1544. 270. S. K. Ravula, D. W. Branch, C. D. James, R. J. Townsend, M. Hill, G. Kaduchak, M. Ward and I. Brener, Sensors and Actuators B: Chemical, 2008, 130, 645-652. 271. A. Abdallah, S. van den Driesche, L. Brandhoff, F. Bunge, M. Akhtar, R. Ebrahimifard, S. Clara, B. Jakoby and M. J. Vellekoop, Procedia Engineering, 2015, 120, 691-694. 272. X. Zhu, Y.-C. Kung, T.-H. Wu, M. A. Teitell and P.-Y. Chiou, 2017. 273. S. Chang and Y.-H. Cho, Lab on a Chip, 2008, 8, 1930-1936. 274. S. Yan, J. Zhang, Y. Yuan, G. Lovrecz, G. Alici, H. Du, Y. Zhu and W. Li, Electrophoresis, 2015, 36, 284-291. 275. S. Yan, J. Zhang, G. Alici, H. Du, Y. Zhu and W. Li, Lab on a Chip, 2014, 14, 2993-3003. 276. S. Choi, T. Ku, S. Song, C. Choi and J.-K. Park, Lab on a Chip, 2011, 11, 413-418. 277. S. Choi, S. Song, C. Choi and J. K. Park, Small, 2008, 4, 634-641. 278. H.-S. Moon, K. Kwon, S.-I. Kim, H. Han, J. Sohn, S. Lee and H.-I. Jung, Lab on a Chip, 2011, 11, 1118-1125. 279. J. Zhang, S. Yan, G. Alici, N.-T. Nguyen, D. Di Carlo and W. Li, Rsc Advances, 2014, 4, 62076-62085. 280. A. Y. Lau, L. P. Lee and J. W. Chan, Lab on a Chip, 2008, 8, 1116-1120. 281. C. W. Shields IV, K. A. Ohiri, L. M. Szott and G. P. López, Cytometry Part B: Clinical Cytometry, 2017, 92, 115-125. 282. J. Che, V. Yu, M. Dhar, C. Renier, M. Matsumoto, K. Heirich, E. B. Garon, J. Goldman, J. Rao and G. W. Sledge, Oncotarget, 2016, 7, 12748. 283. J.-C. Baret, O. J. Miller, V. Taly, M. Ryckelynck, A. El-Harrak, L. Frenz, C. Rick, M. L. Samuels, J. B. Hutchison and J. J. Agresti, Lab on a Chip, 2009, 9, 1850-1858. 284. S. Miltenyi, W. Müller, W. Weichel and A. Radbruch, Cytometry: The Journal of the International Society for Analytical Cytology, 1990, 11, 231-238. 285. G. Luka, E. Samiei, S. Dehghani, T. Johnson, H. Najjaran and M. Hoorfar, Sensors, 2019, 19, 258. 286. J. Čemažar, T. A. Douglas, E. M. Schmelz and R. V. Davalos, Biomicrofluidics, 2016, 10, 014109. 287. H. R. Nejad and M. Hoorfar, Microfluidics and Nanofluidics, 2015, 18, 483-492. 288. T. Luo, L. Fan, Y. Zeng, Y. Liu, S. Chen, Q. Tan, R. H. Lam and D. Sun, Lab on a Chip, 2018, 18, 1521-1532. 289. P. V. Jones, G. L. Salmon and A. Ros, Analytical chemistry, 2017, 89, 1531-1539. 290. D. Chen and H. Du, Microfluidics and nanofluidics, 2007, 3, 603-610. 291. B. Kazemi and J. Darabi, Physics of Fluids, 2018, 30, 102003. 141 292. J. H. Nieuwenhuis and M. J. Vellekoop, Sensors and actuators B: Chemical, 2004, 103, 331-338. 293. E. Altinagac, Y. Genc, H. Kizil, L. Trabzon and A. Beskok, 2014. 294. M. Kim, T. Jung, Y. Kim, C. Lee, K. Woo, J. H. Seol and S. Yang, Biosensors and Bioelectronics, 2015, 74, 1011-1015. 295. H. Hadady, D. Redelman, S. R. Hiibel and E. J. Geiger, AIMS Biophys, 2016, 3, 398-414. 296. S.-I. Han, H. S. Kim, K.-H. Han and A. Han, Lab on a Chip, 2019, 19, 4128-4138. 297. J. G. Kralj, M. T. Lis, M. A. Schmidt and K. F. Jensen, Analytical chemistry, 2006, 78, 5019-5025. 298. K.-H. Han, S.-I. Han and A. B. Frazier, Lab on a Chip, 2009, 9, 2958-2964. 299. S.-I. Han, S.-M. Lee, Y.-D. Joo and K.-H. Han, Lab on a Chip, 2011, 11, 3864-3872. 300. X. Wang, X.-B. Wang and P. R. Gascoyne, Journal of electrostatics, 1997, 39, 277-295. 301. N. G. Green and H. Morgan, The Journal of Physical Chemistry B, 1999, 103, 41-50. 302. I. Turcu and C. Lucaciu, Journal of Physics A: Mathematical and General, 1989, 22, 985. 303. H. Morgan and N. G. Green, AC electrokinetics, Research Studies Press, 2003. 304. P. Gascoyne, C. Mahidol, M. Ruchirawat, J. Satayavivad, P. Watcharasit and F. F. Becker, Lab on a Chip, 2002, 2, 70-75. 305. X. Li, Z. T. F. Yu, D. Geraldo, S. Weng, N. Alve, W. Dun, A. Kini, K. Patel, R. Shu and F. Zhang, Review of Scientific Instruments, 2015, 86, 075008. 306. X. Li, A. L. Corbett, E. Taatizadeh, N. Tasnim, J. P. Little, C. Garnis, M. Daugaard, E. Guns, M. Hoorfar and I. T. Li, APL bioengineering, 2019, 3, 011503. 307. X. Xuan, J. Zhu and C. Church, Microfluidics and nanofluidics, 2010, 9, 1-16. 308. D. J. Collins, Z. Ma and Y. Ai, Analytical chemistry, 2016, 88, 5513-5522. 309. Y. Zhao, B. S. Fujimoto, G. D. Jeffries, P. G. Schiro and D. T. Chiu, Optics express, 2007, 15, 6167-6176. 310. P. B. Howell Jr, J. P. Golden, L. R. Hilliard, J. S. Erickson, D. R. Mott and F. S. Ligler, Lab on a Chip, 2008, 8, 1097-1103. 311. B. R. Mutlu, J. F. Edd and M. Toner, Proceedings of the National Academy of Sciences, 2018, 115, 7682-7687. 312. D. Lee, S. M. Nam, J.-a. Kim, D. Di Carlo and W. Lee, Analytical chemistry, 2018, 90, 2902-2911. 313. J. Zhang, S. Yan, D. Yuan, G. Alici, N.-T. Nguyen, M. E. Warkiani and W. Li, Lab on a Chip, 2016, 16, 10-34. 314. H. Amini, W. Lee and D. Di Carlo, Lab on a Chip, 2014, 14, 2739-2761. 315. R. Nasiri, A. Shamloo, S. Ahadian, L. Amirifar, J. Akbari, M. J. Goudie, K. Lee, N. Ashammakhi, M. R. Dokmeci and D. Di Carlo, Small, 2020, 2000171. 316. X. Qi, D. M. Carberry, C. Cai, S. Hu, Z. Yuan, H. Rubinsztein-Dunlop and J. Guo, Biomedical optics express, 2017, 8, 934-942. 317. L. Ren, Y. Chen, P. Li, Z. Mao, P.-H. Huang, J. Rufo, F. Guo, L. Wang, J. P. McCoy and S. J. Levine, Lab on a Chip, 2015, 15, 3870-3879. 318. D. Dannhauser, D. Rossi, M. Ripaldi, P. A. Netti and F. Causa, Scientific reports, 2017, 7, 1-13. 319. G.-B. Lee, C.-C. Chang, S.-B. Huang and R.-J. Yang, Journal of Micromechanics and Microengineering, 2006, 16, 1024. 320. N. Panwar, P. Song, S. C. Tjin and K.-T. Yong, Journal of Micromechanics and Microengineering, 2018, 28, 105018. 321. J. B. Knight, A. Vishwanath, J. P. Brody and R. H. Austin, Physical review letters, 1998, 80, 3863. 322. T. Stiles, R. Fallon, T. Vestad, J. Oakey, D. Marr, J. Squier and R. Jimenez, Microfluidics and Nanofluidics, 2005, 1, 280-283. 142 323. G.-B. Lee, C.-I. Hung, B.-J. Ke, G.-R. Huang, B.-H. Hwei and H.-F. Lai, J. Fluids Eng., 2001, 123, 672-679. 324. J.-H. Lee, S.-K. Lee, J.-H. Kim and J.-H. Park, Sensors and Actuators A: Physical, 2019, 286, 211-219. 325. M. Nasir, D. R. Mott, M. J. Kennedy, J. P. Golden and F. S. Ligler, Microfluidics and nanofluidics, 2011, 11, 119-128. 326. N. Panwar, P. Song, K.-T. Yong and S. C. Tjin, 2017. 327. A. S. Basu, Lab on a Chip, 2013, 13, 1892-1901. 328. V. V. Swaminathan, M. A. Shannon and R. Bashir, Biomedical microdevices, 2015, 17, 29. 329. N. Crews, J. Darabi, P. Voglewede, F. Guo and A. Bayoumi, Sensors and Actuators B: Chemical, 2007, 125, 672-679. 330. S. Park, Y. Zhang, T.-H. Wang and S. Yang, Lab on a Chip, 2011, 11, 2893-2900. 331. J. Cao, P. Cheng and F. Hong, Journal of Electrostatics, 2008, 66, 620-626. 332. H. Sadeghian, Y. Hojjat and M. Soleimani, Journal of Electrostatics, 2017, 86, 41-49. 333. B. J. Sanghavi, W. Varhue, A. Rohani, K.-T. Liao, L. A. Bazydlo, C.-F. Chou and N. S. Swami, Lab on a Chip, 2015, 15, 4563-4570. 334. V. Gupta, I. Jafferji, M. Garza, V. O. Melnikova, D. K. Hasegawa, R. Pethig and D. W. Davis, Biomicrofluidics, 2012, 6, 024133. 335. X. Hu, P. H. Bessette, J. Qian, C. D. Meinhart, P. S. Daugherty and H. T. Soh, Proceedings of the national academy of sciences, 2005, 102, 15757-15761. 336. S. A. Faraghat, K. F. Hoettges, M. K. Steinbach, D. R. van der Veen, W. J. Brackenbury, E. A. Henslee, F. H. Labeed and M. P. Hughes, Proceedings of the National Academy of Sciences, 2017, 114, 4591-4596. 337. M. Antfolk, S. H. Kim, S. Koizumi, T. Fujii and T. Laurell, Scientific reports, 2017, 7, 46507. 338. Y.-C. Kung, D. L. Clemens, B.-Y. Lee and P.-Y. Chiou, 2015. 339. C. Zhao, Z. Ge, Y. Song and C. Yang, Scientific reports, 2017, 7, 1-12. 340. A. J. Smith, R. D. O’Rorke, A. Kale, R. Rimsa, M. J. Tomlinson, J. Kirkham, A. G. Davies, C. Wälti and C. D. Wood, Scientific reports, 2017, 7, 41872. 341. W.-P. Chou, H.-M. Wang, J.-H. Chang, T.-K. Chiu, C.-H. Hsieh, C.-J. Liao and M.-H. Wu, Sensors and Actuators B: Chemical, 2017, 241, 245-254. 342. V. Calero, P. Garcia-Sanchez, C. Honrado, A. Ramos and H. Morgan, Lab on a Chip, 2019, 19, 1386-1396. 343. K. Zhao, Larasati, B. P. Duncker and D. Li, Analytical chemistry, 2019, 91, 6304-6314. 344. S. Choi and J.-K. Park, Lab on a Chip, 2005, 5, 1161-1167. 345. A. Menachery, C. Kremer, P. E. Wong, A. Carlsson, S. L. Neale, M. P. Barrett and J. M. Cooper, Scientific reports, 2012, 2, 1-5. 346. G. Mernier, E. Duqi and P. Renaud, Lab on a Chip, 2012, 12, 4344-4349. 347. H. Hadady, D. Redelman, S. R. Hiibel and E. J. Geiger, AIMS Biophys., 2016, 3. 348. S. Yan, J. Zhang, M. Li, G. Alici, H. Du, R. Sluyter and W. Li, Scientific reports, 2014, 4. 349. S. Fiedler, S. G. Shirley, T. Schnelle and G. Fuhr, Analytical chemistry, 1998, 70, 1909-1915. 350. M. Dürr, J. Kentsch, T. Müller, T. Schnelle and M. Stelzle, Electrophoresis, 2003, 24, 722-731. 351. K.-H. Han and A. B. Frazier, Lab on a Chip, 2008, 8, 1079-1086. 352. K. Khoshmanesh, C. Zhang, S. Nahavandi, F. J. Tovar-Lopez, S. Baratchi, A. Mitchell and K. Kalantar-Zadeh, Journal of applied physics, 2010, 108, 034904. 353. J. Jung, S.-K. Seo, Y.-D. Joo and K.-H. Han, Sensors and Actuators B: Chemical, 2011, 157, 314-320. 354. J. Park, B. Kim, S. K. Choi, S. Hong, S. H. Lee and K.-I. Lee, Lab on a Chip, 2005, 5, 1264-1270. 355. J. Cao, P. Cheng and F. Hong, Journal of Electrostatics, 2008, 66, 620-626. 143 356. X. Wang, X.-B. Wang, F. Becker and P. R. Gascoyne, Journal of physics D: applied physics, 1996, 29, 1649. 357. H. Morgan, A. G. Izquierdo, D. Bakewell, N. G. Green and A. Ramos, Journal of Physics D: Applied Physics, 2001, 34, 1553. 358. S. Kinio and J. K. Mills, 2015. 359. T. Z. Jubery and P. Dutta, Electrophoresis, 2013, 34, 643-650. 360. S. Kinio and J. K. Mills, Biomedical microdevices, 2016, 18, 69. 361. P. Puri, V. Kumar, M. Ananthasubramanian and N. Sharma, Microsystem Technologies, 2017, 23, 3371-3379. 362. C. H. Chuang and Y. W. Huang, Electrophoresis, 2013, 34, 3111-3118. 363. B. Mathew, A. Alazzam, S. Khashan and B. El-Khasawneh, Microsystem Technologies, 2016, 22, 1721-1732. 364. M. Hajari, A. Ebadi, M. J. F. Heydari, M. Fathipour and M. Soltani, Microsystem Technologies, 2019, 1-13. 365. Q. Chen and Y. J. Yuan, RSC advances, 2019, 9, 4963-4981. 366. D. Qin, Y. Xia and G. M. Whitesides, Nature protocols, 2010, 5, 491. 367. Y. Tao, Y. Ren, H. Yan and H. Jiang, Chinese Journal of Mechanical Engineering, 2016, 29, 325-331. 368. I. S. Park, K. Eom, J. Son, W.-J. Chang, K. Park, T. Kwon, D. S. Yoon, R. Bashir and S. W. Lee, ACS Nano, 2012, 6, 8665-8673. 369. Y. Wang, F. Du, G. R. Pesch, J. Koeser, M. Baune and J. Thoeming, Chemical Engineering Science, 2016, 153, 34-44. 370. I.-F. Cheng, H.-C. Chang, D. Hou and H.-C. Chang, Biomicrofluidics, 2007, 1, 021503.
Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Developing a Lob-On-a-Chip platform for manipulation and separation of microparticles
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