Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Rellstab Sánchez, Pamela Inés
Master of Applied Science - MASc
University of British Columbia
Over the last decade, there has been an ever-increasing interest in microfluidic biosensors in the biomedical sector, especially for diagnosing infectious diseases. These are still one of the top ten causes of mortality around the world and for regions with limited resources where mortality rates are the highest, it is imperative to provide an on-site diagnosis. With early diagnosis, accurate treatments are possible, which can help contain and prevent disease outbreaks. However, there are still several challenges that need to be addressed to develop accurate and sensitive Point of Care (POC) detection devices. Improvements in sample processing, flow manipulation and signal amplification can support powerful emerging technologies for the development of fast, inexpensive, portable, easy-to-use and sensitive diagnostic devices. For the purpose of miniaturization, simple instrumentation, low production cost, and rapid response coupled with high sensitivity/selectivity, microfluidic DNA biosensors with an electrochemical transducer have shown great promise. This thesis focuses on experiments that aim to optimize three design aspects of an electrochemical microfluidic biosensor to improve its applicability: 1) flow cell geometry, 2) flow rate and 3) electrohydrodynamic mixing. Numerical simulations were carried out to model the fluid and particle behavior inside the detection cell. Understanding these flow characteristics led to the development of a new geometry providing uniform distribution of the sample flow across the detection area for increasing the biosensorâs efficiency. In addition, the identification of an optimal flow rate was required to analyze a higher sample volume. To achieve this, the binding efficiency of Streptavidin and Methylene Blue-Biotin was compared at five different flow rates with Square Wave Voltammetry. This resulted in finding an alternate flow rate that could increase the volume sample without sacrificing the selectivity/sensitivity of the detection technology. Lastly, a microdevice was fabricated to test the interaction between the electrostatic field and negatively charged polystyrene particles of different diameters. An electrohydrodynamic effect was observed in all samples, which can be utilized as an effective mixing technique. Overall, the results from the three design aspects could serve as a starting point to continue the optimization of electrochemical microfluidic biosensors and facilitate POC detection.
Optimization of Electrochemical Microfluidic Biosensor Design for Point of Care Devices by Pamela Inés Rellstab Sánchez B.Sc., Tecnológico de Monterrey, Mexico, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2020 © Pamela Inés Rellstab Sánchez, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis/dissertation entitled: Optimization of Electrochemical Microfluidic Biosensor Design for Point of Care Devices submitted by Pamela Ines Rellstab Sanchez in partial fulfillment of the requirements for the degree of Master of Applied Sciences in Mechanical Engineering Examining Committee: Dr. Mina Hoorfar, School of Engineering, Faculty of Applied Sciences Co-supervisor Dr. Homayoun Najjaran, School of Engineering, Faculty of Applied Sciences Co-supervisor Dr. Isaac Li, Department of Chemistry, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. Ray Taheri, School of Engineering, Faculty of Applied Sciences Supervisory Committee Member Dr. Shahria Alam, School of Engineering, Faculty of Applied Sciences University Examiner iii Abstract Over the last decade, there has been an ever-increasing interest in microfluidic biosensors in the biomedical sector, especially for diagnosing infectious diseases. These are still one of the top ten causes of mortality around the world and for regions with limited resources where mortality rates are the highest, it is imperative to provide an on-site diagnosis. With early diagnosis, accurate treatments are possible, which can help contain and prevent disease outbreaks. However, there are still several challenges that need to be addressed to develop accurate and sensitive Point of Care (POC) detection devices. Improvements in sample processing, flow manipulation and signal amplification can support powerful emerging technologies for the development of fast, inexpensive, portable, easy-to-use and sensitive diagnostic devices. For the purpose of miniaturization, simple instrumentation, low production cost, and rapid response coupled with high sensitivity/selectivity, microfluidic DNA biosensors with an electrochemical transducer have shown great promise. This thesis focuses on experiments that aim to optimize three design aspects of an electrochemical microfluidic biosensor to improve its applicability: 1) flow cell geometry, 2) flow rate and 3) electrohydrodynamic mixing. Numerical simulations were carried out to model the fluid and particle behavior inside the detection cell. Understanding these flow characteristics led to the development of a new geometry providing uniform distribution of the sample flow across the detection area for increasing the biosensorâs efficiency. In addition, the identification of an optimal flow rate was required to analyze a higher sample volume. To achieve this, the binding efficiency of Streptavidin and Methylene Blue-Biotin was compared at five different flow rates with Square Wave Voltammetry. This resulted in finding an alternate flow rate that could increase the volume sample without sacrificing the selectivity/sensitivity of the detection technology. iv Lastly, a microdevice was fabricated to test the interaction between the electrostatic field and negatively charged polystyrene particles of different diameters. An electrohydrodynamic effect was observed in all samples, which can be utilized as an effective mixing technique. Overall, the results from the three design aspects could serve as a starting point to continue the optimization of electrochemical microfluidic biosensors and facilitate POC detection. v Lay Summary Nowadays, despite the constant and fast-paced development in science and technology, infectious diseases are still one of the top ten causes of deaths around the world. However, most of them are treatable and curable with an early diagnosis. To achieve that, especially in regions with limited resources where disease outbreaks have a deadlier impact, any detection device must be fast, affordable, accurate, and easy to use and transport. Over the last decade, miniaturized sensors have shown great promise to meet those requirements - specifically, sensors based on electricity and the detection of genetic information. This thesis focuses on exploratory experiments to optimize three design aspects for this type of biological sensor to improve its applicability: 1) flow patterns, 2) flow rate, and 3) mixing. Overall, results from this study demonstrate the potential to continue their optimization until they meet the requirements for an accurate and early on-site detection. vi Preface The research presented in this thesis is the original work performed by the author with the help of collaborators. This thesis was supervised by Dr. Mina Hoorfar and Dr. Homayoun Najjaran at the Advanced Thermo-Fluidic Laboratory (ATFL) in the School of Engineering, University of British Columbia. The details of the authorâs and collaboratorsâ contributions to Chapters 2-4 are explained below: a. CHAPTER 2: Geometrical Optimization for Uniform Flow Distribution My contribution to this chapter included writing, supervising, as well as compiling, developing and analyzing the simulations that were carried out with the COMSOL® expertise of Erfan Taatizadeh and Arash Dalili and the help of Erfan and Trevor Sebastien in SolidWorks®. b. CHAPTER 3: Flow Optimization for Continuous Biosensors My contribution involved writing, designing and carrying out experimental procedures with the help of Jerold Morgan and Jordan Baeder from Bio-Rad. In addition, data analysis was completed in collaboration with Nishat Tamsin. c. CHAPTER 4: Electrostatic Effect: Electrohydrodynamic Mixing My contribution to this chapter included the microfabrication, design, experimental work, results analysis, the COMSOL® simulation in collaboration with Erfan and the CAD drafting of photolithography masks in collaboration with Arash. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Symbols ........................................................................................................................... xiii List of Abbreviations ...................................................................................................................xv Acknowledgements ................................................................................................................... xvii Dedication ................................................................................................................................. xviii Introduction ................................................................................................................1 1.1 Microfluidic Biosensors: Overview ................................................................................ 1 1.2 Microfluidic Biosensors and Point-of-Care (POC) ......................................................... 1 1.3 Microfluidic Fundamentals ............................................................................................. 2 1.4 Device Fabrication .......................................................................................................... 3 1.4.1 Materials ..................................................................................................................... 4 1.4.2 Microfabrication ......................................................................................................... 4 1.5 Fluidic Operations: µTAS ............................................................................................... 5 1.6 Biosensing ....................................................................................................................... 7 1.6.1 Transduction Mechanisms .......................................................................................... 7 1.7 DNA Voltage-Assisted Biosensors ................................................................................. 8 viii 1.8 Motivation: Bio-Rad Collaboration ................................................................................ 9 1.8.1 Challenges ................................................................................................................. 10 1.9 Objectives ..................................................................................................................... 11 1.10 Thesis Organization ...................................................................................................... 11 Geometrical Optimization for Uniform Flow Distribution ..................................12 2.1 Flow Cell Design .......................................................................................................... 12 2.2 Numerical Simulations.................................................................................................. 13 2.2.1 Laminar Flow Effect ................................................................................................. 14 2.2.2 Electrostatic Effect .................................................................................................... 15 2.2.3 Particle Tracing Simulation ...................................................................................... 18 2.3 Alternative Flow Cell Design ....................................................................................... 21 2.4 Quantitative Analysis .................................................................................................... 23 2.5 Summary ....................................................................................................................... 28 Flow Optimization for Continuous Biosensors ......................................................29 3.1 Overview ....................................................................................................................... 29 3.2 Background ................................................................................................................... 29 3.3 Experimental Procedure and Results ............................................................................ 31 3.4 Summary, Limitations and Future Work ...................................................................... 37 Electrohydrodynamic Mixing .................................................................................38 4.1 Overview ....................................................................................................................... 38 4.2 Experimental Procedure ................................................................................................ 38 4.2.1 Device Microfabrication ........................................................................................... 38 4.2.2 Flow Chamber Microfabrication ............................................................................... 41 ix 4.2.3 Chip Assembly .......................................................................................................... 41 4.2.4 Experimental Setup ................................................................................................... 42 4.2.5 Electrostatic simulation ............................................................................................. 43 4.3 Experimental Results and Discussions ........................................................................ 44 4.4 Summary ....................................................................................................................... 49 Summary, Limitations and Future Work ..............................................................50 5.1 Summary ....................................................................................................................... 50 5.2 Limitations and Future Work ........................................................................................ 52 Bibliography .................................................................................................................................54 x List of Tables Table 2-1. Comparison of microparticles at the surface between 3 different geometries. ........... 24 Table 2-2. Comparison of microparticles at 2 distances from the chip surface in the proposed geometry. ................................................................................................................... 25 Table 2-3. Effect of four flow rates on the particle count within 1 µm of the chip surface in 4 chamber designs. ........................................................................................................ 26 Table 3-1. Experimental conditions according to the flow rate used and the four different time intervals at which measurements were taken. ............................................................ 31 Table 3-2. Absolute average peak current values (μA) measured in all experimental conditions ... ................................................................................................................................... 36 xi List of Figures Figure 2-1. The flow area of detection cell. .................................................................................. 12 Figure 2-2. Laminar flow simulation: a) geometry, b) mesh, c) & d) velocity magnitudes ......... 14 Figure 2-3. Simulation of the original geometry with a bank of electrodes. A) geometry; B) bank of electrodes; C) mesh; D) velocity field; E) electrical potential; F) electrical field .... ................................................................................................................................... 17 Figure 2-4. Simulation of the original geometry with one electrode (A-B) of the electrical potential (B-C) ........................................................................................................... 18 Figure 2-5 Particle tracing simulation of original chamber design at t=10s from four different views. ......................................................................................................................... 19 Figure 2-6 Top view of microparticle tracing simulation in the original chamberâs design from t=0s (upper left corner) to t=10s (lower right corner). .............................................. 20 Figure 2-7 Alternative flow geometry .......................................................................................... 21 Figure 2-8. Particle simulation of the proposed design from t=0 s to t=11 s (side and front view) ................................................................................................................................... 22 Figure 3-1. A) Example of a voltammogram overlay for the static condition at the four different time intervals shown in image B. B) Voltammograms of the three technical repetitions for each time interval. .............................................................................. 32 Figure 3-2. Forward (blue), reverse (green), and net (red) current curves for SWV. Reproduced from [101] .................................................................................................................. 32 Figure 3-3. Box plot of peak measurements in µA. Comparison of different flow rate conditions at each time period: T1, T2, T3, T4 ........................................................................... 33 Figure 3-4. Peak measurements in µA. Individual flow rate results (0, 1, 2, 5, 10 µl/s) .............. 35 xii Figure 4-1. Chip design (a) and dimensions (b). .......................................................................... 38 Figure 4-2. Photomask for the photolithography microfabrication. ............................................. 39 Figure 4-3. General microfabrication procedure .......................................................................... 40 Figure 4-4. Design for 3D printed PDMS mold ........................................................................... 41 Figure 4-5. Finalized chips ........................................................................................................... 42 Figure 4-6. Experimental setup. (Used and edited with the permission of Arash Dalili) ............. 43 Figure 4-7. Electrostatic simulations. A) Electric potential B) Electric field. .............................. 44 Figure 4-8. The electrohydrodynamic effect of negatively charged PS particles of 1 µm diameter. ................................................................................................................................... 45 Figure 4-9. The electrohydrodynamic effect of negatively charged PS particles of 3 µm diameter. ................................................................................................................................... 46 Figure 4-10. The electrohydrodynamic effect of negatively charged PS particles of 6 µm diameter. .................................................................................................................... 47 xiii List of Symbols Ï Density ?â? Electric Displacement Field ?â? Electric field vector ?â? Electric Field Vector ?â? Polarization Vector Field ð0 Applied voltage ðððð¥ Maximum Velocity ððµ Bulk viscosity ðð Electric Susceptibility of a Material ð0 Permittivity of Free Space µL/s microliter per second â« Angstrom Cb Bulk Concentration Cb-Red Bulk Concentration of Reduction Species Cs Surface Concentration CV Cyclic Voltammetry E Potential h height I Current Ip Current Peak Ka Binding Constant xiv kDa Kilodalton kHz kilohertz mg Milligram MHz Megahertz min Minute mm millimeter mM millimolar s Second t Time V Volts x Distance ð Dynamic viscosity ðL Microliter ðm Micrometer ð Space Charge Density ð Electrical potential xv List of Abbreviations 3D Three-dimensional AC Alternating current ARC Advanced Research Computing Au Gold AVE Average B Biotin BCs Boundary conditions BioMEMS Biological Microelectrochemical Systems CE Counter Electrode Cr Chromium DI Deionized water DNA Deoxyribonucleic Acid dsDNA Double-stranded DNA EOF Electroosmotic Flow FEA Finite Element Analysis FEM Finite element method HPV Human papillomavirus IPA Isopropyl alcohol MB Methylene Blue MBB Methylene Blue Biotin MEMS Microelectromechanical Systems xvi PBS Phosphate-buffered saline PDMS Polydimethylsiloxane POC Point-of-Care PS Polystyrene PS PolystyreneRE Reference Electrode Re Reynoldâs number S/V Surface to Volume ratio SA Streptavidin ssDNA single-stranded DNA SWV Square Wave Voltammetry UV Ultraviolet WE Working Electrode ðTAS Micro-total-analysis systems xvii Acknowledgements The last two years have been one the hardest periods of my life, so I am sincerely and will forever be grateful to everyone that has crossed my path and helped me overcome. Special thanks to my parents, María Elda and Alberto, who have always inspired me to dream a little bigger, to give a little more, and for reminding me that no matter what, Iâm always fully and unconditionally loved. My deepest appreciation to my siblings, Jesús and Ãngel, my extended family and my oldest and closest friends, Karla and Sandy; your kind and inspiring words of support have kept me going during hard times, please know that you are always in my heart. Thanks to MITACS, Sofía Mercado, and my supervisors for the educational opportunity. Thanks as well to everyone in the lab made my time there so memorable! Special thanks to Nish, Arash and Erfan for all your support, my best thoughts are always with you! My enduring gratefulness to friends in the community who have supported me in one way or another. To Sandra, Angie, Viviana (and the Bahaâi Group), Cynthia, Dario and Andrea, I thank you all for your support! I hope I can be there for all of you as much as you have been there for me. Lastly, to my partner, my eternal gratitude for always being there for me, through thick and thin, for helping me keep my sanity and reminding me that the best is yet to come. To you, all my love. xviii Dedication To strength, hope, and kindness, May you always be by our side to remind us of our humanity. 1 Introduction 1.1 Microfluidic Biosensors: Overview Nowadays, with the constant and fast-paced development of science and technology, multiple disciplines have been able to converge and successfully translate research into commercially available devices. The intersection of biosensing and microfluidics has not been an exception[1]â[4]. Over the past 25 years, major advancements in these two areas have opened the possibility of portable, disposable and high-throughput analysis for a variety of fields, from food safety[5], [6] to environmental monitoring[7]â[9] and from bioterrorism[10] to health diagnostics[11]â[13]. 1.2 Microfluidic Biosensors and Point-of-Care (POC) As in many of its other applications, microfluidic biosensors in health diagnostics can provide multiple advantages over traditional assays[14]â[16]. For example, benefits include reduction of volume of reagents required, potential use in the field (Point-of-Care), integration of pre-sample treatments, miniaturization that enhances portability, higher assay efficiency and reduction of cross-contamination[17]â[22]. All of these potentially lead to a decrease in cost and faster and more accessible diagnosis. Even today, one of the most urgent health-related issues worldwide is infectious diseases. They still represent part of the top ten causes of mortality around the world [23]. Despite the successful development of treatments and cures for different infectious diseases, remote locations and limited resources frequently hinder the prompt access to healthcare that could potentially save millions of lives by monitoring and preventing epidemics or pandemics[17], [23], [24]. For this reason, an 2 early Point-of-Care (POC) diagnosis is essential. However, there are certain parameters that must be met to achieve an efficient diagnosis. According to the World Health Organization, the desirable diagnostic tool should comply with the acronym âASSUREDâ that stands for: â1) affordable, 2) sensitive, 3) specific, 4) user-friendly, 5) rapid and robust, 6) equipment free and deliverable to end-users.â[25] To that end, advancements in biosensing technologies have allowed the identification of different biomarkers (proteins[26], nucleic acids[27], metabolites[28] or cells[29]) related to specific diseases without the need for invasive testing techniques. Through collecting biofluids as samples (such as saliva, urine, blood, among others), on-site diagnosis can be performed. In addition, new materials, fabrication processes, chip and component design and integration procedures to biosensing have allowed microfluidic platforms to improve controllability, reliability and throughput in their applications. [30]â[33] 1.3 Microfluidic Fundamentals As the word implies, microfluidics focuses on the manipulation of fluids at the micro-scale (1 to 1000 μm). But it was not until the successful miniaturization of electronics into microelectronics and microelectromechanical systems (MEMS) in the 1990âs that the question of extrapolating this technology to other disciplines emerged [4]. In areas such as biology and chemistry, the idea of approaching traditional laboratory methods with new technology that allowed for the processing of smaller sample sizes at faster speeds, lower costs, while still exhibiting high sensitivity, drew more and more attention. [2], [34] 3 By using microfluidic channels, small overall volumes (sample, reagents or waste) are required or generated, and this represents an advantage regarding fluid handling. Unlike the macro scale, micro-fluids have an increased surface-to-volume ratio (S/V) and do not mix convectively[2]. This means that inertia doesnât dominate over viscosity, which explains the low Reynoldâs number (Re) that describes the expected laminar flow in microsystems. Because of this parallel flow, spontaneous mixing can only occur through diffusion. This diffusion through layers of laminar flow is the foundation of microfluidic sensors. Therefore, the smaller the distance required for interaction, the faster the process will become. [2], [34], [35] On the other hand, additional characteristics involved with handling fluids at this scale are the parabolic velocity profile that will form when a pressure-driven flow occurs, and the electro-osmotic flow (EOF) effect that allows liquid plugs to be formed by applying an electrical potential through a microchannel (an ionic fluid is manipulated over a surface that has been previously charged). Taking advantage of the novel flow characteristics in microfluidic systems has advanced the development of microfluidic sensors [36]. Through applying characteristics of laminar microflows, these highly portable devices perform multiple purposes like on-sample processing[31], [37]â[40], flow manipulation[41] and signal amplification[5], thus leading to emerging powerful technologies that aim to provide faster, inexpensive, portable, easy-to-use and more sensitive analyses. [20] 1.4 Device Fabrication Microfabrication processes have enabled the development of microfluidics devices. Integrating micro-scale components that have all the advantages of their macro-counterparts offers great 4 potential of increased portability and ease of use. Nevertheless, for most of its applications, microfluidic devices demand a reproducible and robust fabrication process. [13], [42], [43] 1.4.1 Materials Following the materials and manufacturing techniques of MEMS, microfluidics also had a start with silicon and glass [2], [44]. However, for biological purposes the characteristics of those first materials were quickly deemed inadequate. Instead, polymeric devices gained more traction due to their ease of fabrication, ease of coupling to other microfluidic components (micropumps or micromixers), biocompatibility, visibility and cost [45]. For all those reasons, the elastomer polydimethylsiloxane (PDMS) is one of the most used soft polymers in laboratory settings [26], [46], [47]. However, commercialization with this material has been difficult because of scalability issues as well as chemical properties that limit its applications [3]. As a result, devices with engineered plastics[2], [48] and paper[2], [49]â[51] have been developed to address some of these limitations. In general, materials used for BioMEMs are categorized as inorganic, polymeric (rigid/soft) or paper, and their respective advantages and limitations have been compared in several review papers [2], [52], [53]. Nevertheless, the three main factors to consider when selecting a material should be: 1) function required, 2) compatibility with manufacturing purposes, and 3) application. [2] 1.4.2 Microfabrication Due to the wide variety of applications these devices can have, a successful microfabrication process should include all stages between product design and manufacture. From material selection to fabrication techniques, prototyping, mass production, and assembling procedures, every 5 criterion needed to achieve full functionality should be considered in microfabrication. However, depending of the advantages and limitations of the material selected, there are numerous manufacturing methods that can be used or combined to achieve the desired product quality and finish [1]. For example, for glass, the combination of photolithography and etching allow for more versatile channel or feature designs and are used in Section 4.2.1 and described in Figure 4-2 [2]. For rigid polymers, micromilling techniques can allow the fabrication of lab-on-chip devices by directly removing the material from a stock polymer to form the desired features or by producing molds that can be later used in injection molding or hot embossing. Or for soft polymers, cure molding allows the development of a mold where the polymer is poured in and left to cure at temperatures from 60 to 70 °C, an example of this is explained in Section 4.2.2. [53], [54] 1.5 Fluidic Operations: µTAS Micro-total-analysis systems (ðTAS), also known as laboratories-on-a-chip, are the chemical analogue of MEMS and represent one of the most attractive applications of microfluidic devices within the biomedical or life sciences field [55]. By integrating a microfluidic device that can automate multiple processes relevant to laboratory sciences, lab-on-a-chip devices can reduce the time and cost of clinical diagnostics and make the difference in health crises such as epidemics or biowarfare pathogen detection [41], [56]. However, to develop these autonomous platforms, different components must be integrated to control the fluid transport, sample preparation and sample analysis. The most common examples of these microfluidic components are pumps, valves and mixers. [2], [54] 6 Perhaps the most basic component used in fluid manipulation, microvalves are responsible for allowing or hindering the flow in a system. They are commonly classified as either passive or active. While passive valves rely on the manipulation of fluid through their intrinsic physical or chemical structures; active ones require an actuation from an external source, which allows the user to open and close the channels as desired. This last category can also be divided in mechanical (i.e. magnetic, electric, piezoelectric and thermal), non-mechanical (i.e. electrochemical, phase change, rheological) and external (i.e. pneumatic) depending on their operating mode and actuation mechanism. Correspondingly, passive valves can be mechanical (i.e. check valve) or non-mechanical (i.e. diffuser, capillary). [2], [34] Because microvalves generate some flow displacement upon closing and this motion can be directed, they share a couple of designs concepts with micropumps. They can be classified based on their principle of operation: displacement or dynamic. Displacement pumps operate on stimulations such as piezoelectrical, thermopneumatic, electrostatic, electromagnetic, electrowetting, thermocapillary, osmotic, among others; while the dynamic ones have design principles like centrifugal or electroosmotic pumping, electrohydrodynamic, acoustic streaming, etcetera. Although a great diversity of micropumps have been developed to match the unique physical properties of the fluid in question, there are still several opportunities for optimization, integration and parallelization with ðTAS to increase their applications. [2], [19], [35], [55] Many of the biological processes that microfluidic devices perform require thorough and fast on-chip mixing of the reagents and the sample [57]â[60]. Most of the time, the inherent diffusion within a laminar flow canât provide this fast and thorough mixing on its own. For this reason, 7 different types of micromixers have been implemented in microfluidics. Similar to microvalves, they can be categorized as passive or active, if they require an external actuator or not. Active mixers include different actuation principles such as pressure, electrohydrodynamic[61], [62], electrokinetic, magnetic, piezoelectric, thermal or acoustic. Passive mixers operate via lamination, injection or the chaotic stacking principle. [2] 1.6 Biosensing To provide a better understanding on the biosensing advancements, it is important to clarify that a biosensor does not only imply the identification of a biomolecule, but also the transduction of a biological response into a signal that can be further analyzed [28], [37]. The process includes the need for a bioreceptor that generates a signal upon interaction with the desired analyte in a process is called bio-recognition. After this, signalization will occur where a transducer will convert said signal into a different type of change that can be measured. An electronic device will then be used to process the transduced signal, which may include signal amplification, conversion to digital form and/or direct measurement. Finally, a display will present the output signal in a user-friendly form so it can be further analyzed. [39], [63]â[65] 1.6.1 Transduction Mechanisms The sensing mechanism through which biological responses are converted into readable signals can be based on several different principles. The most commons ones are electrochemical[66]â[68], gravimetric, optical[69] or electrical[70]â[72]. From the first category, biosensors can be based on amperometry where they measure the current in redox processes; impedance, when its value is measured as a function of excitation frequency; potentiometry, when there is a potential 8 difference seen as a change in voltage; or voltammetry, when the current measured is a function of potential. On the other hand, optical biosensors can measure fluorescence, absorbance, luminescence, changes in refractive index (interferometry) or color. Electrical sensing mechanisms can provide the measurement of resistance, capacitance or inductance. Depending on the complexity of the application and the chemical/physical properties of the bioreceptor being used, tailored biosensor designs that combine different transduction mechanisms may be required to provide robust detection. [2], [37], [65] 1.7 DNA Voltage-Assisted Biosensors For biosensors that use DNA as a biomarker, hybridization has become one of the main principles for their operation [73], [74]. During this process, probes of single-stranded DNA (ssDNA) are immobilized on a surface layer where they will form a more stable double helix configuration (dsDNA) after recognizing their complementary DNA [75]. This biological response can be then be converted into a quantitative signal by different types of transduction mechanisms, including optical, piezoelectric, thermal, electrochemical, etc. However, electrochemical transducers have become more popular because they allow for the hybridization signal to be converted into an electrical one in a simple and direct way [76]â[79]. Different mechanisms can be used to accomplish this, including conductometry, amperometry, potentiometry and voltammetry. More specifically, electrochemical transducers can monitor redox reactions, where peaks or high points of electrical current are generated by electroactive DNA bases, hybridized indicators, or the hybridization with DNA probes tagged with enzymes or nanoparticles. Therefore, the two methods frequently used can be classified as 1) label-based or 2) label-free [74]. All of this provides several advantages over other types of biosensors with regard to miniaturization, simple instrumentation, 9 high sensitivity and selectivity, rapid response, ease of use, facilitated microfabrication processes, low cost for production, low power requirements and high portability [32], [80]â[83]. In other words, electrochemical biosensors show great potential to meet the requirements for POC detection methods for infectious diseases. [21], [28], [82], [84]â[86] 1.8 Motivation: Bio-Rad Collaboration As part of an industry project, I collaborated with the USA-based company Bio-Rad, working to improve the sensitivity of a patented DNA-based electrical detection technology for a wide range of infectious diseases. The proposed project aimed to design and fabricate a flow-through system that could increase the volume of the analyzed sample (from 0.1 to 1 ml) per minute and optimize fluidic parameters for an accurate and robust detection. The liquid handling system was expected to distribute the flow uniformly across the detection chamber while preventing clogging or bypassing of the biochipâs receptors. This would allow the receptors to be optimally used and to maximize the accuracy and throughput rate of the biochip. A detection cell with the appropriate configuration for the inlet and outlet manifolds would be designed to maximize the hybridization efficiency between the targets and receptors at the desired flow rates, as well. During the first year of the project, work was focused on three different areas of Bio-Rad's proprietary detection technology and their diagnostic device: 1) Buffer solution, 2) Flow cell design and 3) Data analysis. Despite the work done in the first one with the buffer's characterization to develop a new formula for publication purposes and the data analysis done in R for the third aspect, the main focus was on the second area, the flow cell. Within this aspect, work was carried out to develop a new sensing chamber design, experiment with different flow rates and uncover 10 the transport mechanisms that occurred during detection. The respective objectives for each area were: 1) Increase the number of particles that remain at 1 μm distance from the surface to increase hybridization/binding; 2) Determine optimal or maximum flow rates for hybridization/binding; 3) Understand transport mechanisms of negatively charged particles in nanogap sensors. For each objective, a numerical modeling perspective and their experimental counterpart were designed. To respect the confidentiality of the project, real measurements and specific details of the proprietary technology might be excluded. 1.8.1 Challenges Nevertheless, the main constraint in the work to characterize and improve most microfluidic parameters was our dependency on receiving bioactive chips and Human papillomavirus (HPV) positive control samples from the company. At the time, Bio-Rad was constantly working on improving the biochipâs manufacturing technique and addressing some challenges caused by DNA bridging. Equally limiting was the lack of availability of key equipment in our cleanroom facility (i.e. sputtering machine and mask aligner) to fabricate the required chips for running experiments that could corroborate the data. This caused the project to focus on numerical modeling; however, because of the complexity of the flow cell design, as well as the many electrodes it contained, the simulations were computationally expensive. This was addressed by using Compute Canada (an advanced research computing (ARC) system for Canadian researchers). Even so, we experienced limiting factors as there were only two license permits to be used across the country for the module we required for the simulations. 11 1.9 Objectives Following the work done in collaboration with the industry partner, the three objectives of this research are: 1) Develop a new design for the detection cell where its geometry and inlet and outlet manifold provide an uninterrupted and uniform flow to optimize the use of the biochipâs receptor. 2) Determine the effect of different flow rates in a hybridization/binding reaction. 3) Identify the effect of applied voltage on negatively charged particles within the biosensor. 1.10 Thesis Organization For a clearer presentation, this thesis is organized in 5 sections. This first chapter aimed to establish the context of the research as well as a general background of microfluidic biosensors. In the following three chapters, each one of the three objectives will be reviewed respectively. Chapter 2 focuses on the design and numerical simulations of a new detection cell. Chapter 3 discusses the effect of different flows in the binding reaction of the biotin-streptavidin complex. Chapter 4 analyzes the mixing effect presented by negatively charged particles in a micro-gap sensor. Finally, Chapter 5 summarizes past and future work for the current research. 12 Geometrical Optimization for Uniform Flow Distribution 2.1 Flow Cell Design To optimize the geometry of the detection cell, the flow area was modeled in SolidWorks® from the original measurements of the flow chamber, which is created by an O-ring pressed between the surface of the chip and the fabricated flow manifold (inlet & outlet) as shown in Figure 2-1. The resulting geometry was used in numerical simulations with the software package COMSOL Multiphysics® were different modules like âLaminar Flowâ, âParticle Tracingâ and âElectrostaticsâ were used to characterize flow parameters through this initial geometry. After a new geometry design was proposed, complementary simulations for âParticle Tracingâ were run. Quantitative analyses were also made to compare the number of particles within a certain distance of the sensing surface in each of the different geometries, as well as the effect of applying different flow rates. Figure 2-1. The flow area of detection cell. 13 2.2 Numerical Simulations In order to reduce the time and experimental costs of iterative design optimization, numerical simulations have provided a great alternative to achieve this theoretically. In microfluidics and many other fields, this has allowed a better understanding of how systems work [87]â[90]. Simulation software has recently become more accessible as well. It has improved its user interface and now includes a wide range of preset modules with equations that account for different physical phenomena. As previously mentioned, the finite element analysis (FEA) software used for this research is COMSOL Multiphysics® (version 5.4, www.comsol.com) because it allowed the coupling of multiple physical properties when required. Once it was established that numerical simulations were necessary because the proposed questions were not easily answered by analytical calculations, it was important to identify the information we wanted to obtain from the simulations, recognize the physics involved and understand how the two were connected. For this research, we were interested in characterizing the fluid profile and electrostatic effect of the initial chamber design to determine the particleâs distribution within the detection cell. Considering there is a specific number of receptors anchored to a determined area of the chipâs surface (Figure 2-1), it was necessary to verify that the flow pattern was achieving uniform particle distribution across the chip without saturating or creating dead zones within the detection zone. This step would also establish a baseline that we could compare against the new detection cell design that was developed as part of the objectives of the industry project. After finalizing the 3D geometry describing the shape and structure of the flow cell, the physics and their respective boundary conditions were defined for each study. Then, using a finite element 14 mesh the flow domain was subdivided into smaller domains called elements, where the set of equations of the selected physics modules were solved for each element in the simulated flow field. Specific parameters and the results of each physics module are presented in the following subsections. 2.2.1 Laminar Flow Effect For the âLaminar flowâ module, the Navier-Stokes equations (Eq. 2-1) govern the modeling of fluid flow by representing the conservation of momentum. These equations are solved together with the continuity equation (Eq. 2-2) that represents the conservation of mass. Assuming steady state conditions, the equations can be simplified to: ð(?â? â ð»)?â? = ð» â [âðð¼ + ð(ð»?â? + (ð»?â? )ð)]â¦â¦â¦â¦â¦â¦â¦â¦â¦...(2-1) ðð» â (?â? ) = 0 â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦..â¦â¦â¦(2-2) Where ?â? is the fluid velocity, p the fluid pressure, Ï the fluid density, and μ the fluidâs dynamic viscosity [91]. In order to solve them numerically and predict the fluid velocity and pressure of the geometry, a set of boundary conditions (BC) for the inlet, the outlet and the walls were required. The fluid velocity was entered as the BC for the inlet, while the normal outflow velocity was Figure 2-2. Laminar flow simulation: a) geometry, b) mesh, c) & d) velocity magnitudes 15 selected for the outlet. For the walls, a no-slip boundary condition, where the velocity is set to zero relative to the boundary, was defined. Because the viscosity of Bio-Radâs proprietary buffer was similar to water (dynamic viscosity of the proprietary buffer was measured to be 1.01138 ððð âð , while the dynamic viscosity of water was measured to be 1.0005 ððð â ð ), the properties of water were used to define the fluid flowing through the geometric domain. Figure 2-2, images C and D show the numerical solution in the laminar regime for the velocity magnitude at different segments of the detection cell. Assuming an incompressible fluid (i.e. constant density), the slices in each image can be interpreted by their respective single legend. As expected, the smaller cross- section of the inlet and outlet domain present a higher velocity magnitude (ðððð¥ = 30ð¥10â3 m/s) than the middle section of the detection cell above the surface of the chip as shown by the slice in image D (ðððð¥ = 1.5ð¥10â3 m/s). 2.2.2 Electrostatic Effect As the names implies it, electrostatics defines the effect of static or nonmoving charges in the generation of an electric field. Mathematically, it can be described as follow: ð» â ?â? =ðð0â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦.(2-3) Where ð represents space charge density, â is the vector partial differential operator, ?â? is the vector electric field, and ð0 is the permittivity of free space. The dot product operation between â and ?â? , evaluated at a point in the vector field returns a scalar value indicating the divergence of the field at that point. This scalar divergence value is equal to the space charge density in that region of space divided by the permittivity of free space. Divergence is the tendency of a field to flow out of a very small volume centered on the point. Therefore, this equation states that the electric field emanates from 16 volumes that contain electric charges, which is the basis of our understanding of the electric field. In other words, positive electric charges are the âsourceâ of electric field lines in an electric field, and negative electric charges are the âsink.â Only differential volumes in space that contain a non-zero space-charge density will have non-zero divergence values associated with their coordinates. The vector electric field, ?â? , is a field defined as the 3-dimensional gradient of the scalar electric potential field in space, written as: ?â? = ââV.â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦(2-4) Where ð is the scalar electric potential field. An electric potential field is the voltage field, with each point having some electric potential value relative to the potential at a reference point. Therefore, the electric field vector associated with a point in space gives the direction and magnitude of the electric potential gradient at that point. For all gradients, the direction of a gradient indicates the direction of the greatest ârate of changeâ, and the magnitude of the gradient is the magnitude of that greatest ârate.â Another equation, known as Gaussâ Law, used to model the electrostatic field was: â â ?â? = ðâ¦â¦â¦â¦â¦â¦....â¦â¦â¦â¦â¦...(2-5) Where ?â? is the electric displacement field, and ð is the space charge density. ?â? = ð0?â? + ?â? â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦.(2-6) Where ?â? is the polarization vector field, which is necessary to describe the polarizing effect of an electric field inside of a dielectric material. In linear dielectric materials, the polarization field is equal to a scalar multiple of the electric field, ?â? . In those special circumstances, ?â? = ð0?â? + ?â? = ð0?â? + ð0ðð?â? = ð0(1 + ðð)?â? = ð0ðð?â? = ð?â? â¦â¦â¦.(2-7) ?â? = ð0ðð?â? ..â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦.......(2-7a) 17 Where ðð is the electric susceptibility of the material [92]. This simplification helps to give an intuition into the nature of ?â? , the electric displacement field. In more âidealâ materials, the electric displacement field turns out to be nothing more than a scaled version of the electric field (2-6a). Figure 2-3 shows the geometry of the detection cell with a bank of 10 electrode pairs, where the streamline of fluid velocity shows the path of particles within the sample flow (D). Image E shows the electric potential of the ðm-size electrodes at the surface of the chip while image F presents its electric field. On the other hand, because of the computational cost of simulating a bank of A B C D E F Figure 2-3. Simulation of the original geometry with a bank of electrodes. A) geometry; B) bank of electrodes; C) mesh; D) velocity field; E) electrical potential; F) electrical field 18 electrodes in the actual nano-scale of the biosensorâs electrode, only one pair of electrodes was modeled and its respective electric potential is shown in Figure 2-4. 2.2.3 Particle Tracing Simulation In order to achieve the desired uniform flow across the detection surface where the receptors were located, particle tracing simulations were carried out for the initial geometry and the ones designed after that for comparison. In them, fluid is treated as a continuum, rather than a collection of particles, because the characteristic length of the system is much larger than the free mean path, or the average distance between molecules. Therefore, particle tracing simulations are a visualization of Lagrangian reference frame for fluid mechanics, or the tracking of one particle as it moves through space and time. For simplification purposes, it is also assumed that there is no interaction Figure 2-4. Simulation of the original geometry with one electrode (A-B) of the electrical potential (B-C) C D A B 19 between the particle and the fluid aside from drag force acting upon the particles from the flow. Figure 2-5 shows the simulation of the initial geometry of the detection cell from different views. For a clearer understanding of the path followed by the particles upon introduction to the cell, Figure 2-6 shows the simulation with respect to time. Remembering where the receptorâs area is located from Figure 2-1, it can be seen how particles donât reach them until ð¡ â¥2 s, and when they do, it is not done uniformly. Considering how valuable time is for POC diagnostics, the optimal device should take maximum advantage of the full receptor area immediately. Therefore, the flow cell geometry was redesigned. Figure 2-5 Particle tracing simulation of original chamber design at t=10s from four different views. 20 Figure 2-6 Top view of microparticle tracing simulation in the original chamberâs design from t=0s (upper left corner) to t=10s (lower right corner). 21 Figure 2-7 Alternative flow geometry 2.3 Alternative Flow Cell Design Taking into account the information provided by the previous simulations, a new geometry for the detection cell was modeled (Figure 2-7). An individual inlet was incorporated at the center of the geometry to directly deliver the sample to the top of the receptorâs area. As the distance from the surface decreased, the width of the inlet channel increased in a bell-shape as shown in image B, allowing for a large cross-section of bulk flow normal to the receptors. This helped to slow down the particles that struck the receptor area, decreasing their kinetic energy and holding them near the sensing surface for a longer period of time, which would also help increase the chances of biorecognition. Initially, only one outlet was considered as shown in image A and B. For internal flow, the critical Reynolds number corresponding to transitioning from laminar to turbulent flow is 2,300, therefore having 4 outlets instead of one allowed for lower outlet flow velocity, decreasing the Re of the flow at the outlet and generating a more orderly flow at the exit. In addition, by making the outlets symmetric the flow can reach the surface of the sensor uniformly. Figure 2-8 shows the constant and uniform distribution of the particles in the simulation of the proposed geometry at different times after their release. 22 Figure 2-8. Particle simulation of the proposed design from t=0 s to t=11 s (side and front view) 23 2.4 Quantitative Analysis To facilitate the comparison between the different geometries, quantitative simulations were done to model the maximum number of particles that would be suspended within 1µm of the chipâs surface. The first results are shown in Table 2-1 and compare 3 different geometries: 1) the proposed design (Figure 2-7), 2) the initial detection cell (Figure 2-1) and 3) a model of our proposed design adapted to the proprietary chip of Bio-Rad. As per real dimensions, an O-ring created a 1.12 mm gap between the surface of the chip where the receptors were located and the upper flow âceilingâ of the cell. This is considered as the chamber height. All geometries were modeled to reflect this value. As it can be observed from Table 2-1, no particles where detected within 1 µm of the receptors in the initial geometry. However, there was a maximum of 11 particles out of 1000 released during the simulation in the other 2 designs. Despite the low values, they represented an improvement on the previous design. In addition, the proposed geometry (2) was used in further quantitative simulations. Because reducing the height of the chamber would bring the bulk of the flow closer to the bioreceptors, the chamber was modified to have a 500 µm height instead the original 1.12 mm. Particle counts within two distances (10 µm and 50 µm) from the surface were then compared and shown in Table 2-2. From the same 1000 particles released in the simulation, the first height yielded a maximum of 23 particles within the 10 µm distance from the surface, while the further distance of 50 µm yielded 90. 24 Table 2-1. Comparison of microparticles at the surface between 3 different geometries. Flow Cell Design h=1.12 mm Maximum Number of microparticles at 1 µm from the surface of the chip (N=1000) 1.Initial Flow Cell Design 2.Proposed Geometry 3.Proposed Geometry Adapted to Proprietary Chip 25 Table 2-2. Comparison of microparticles at 2 distances from the chip surface in the proposed geometry. Distance from the surface Effect of reducing the height of the chamber on the maximum number of microparticles (N=1000) at the surface of the chip h=10 ðm h=50 ðm Because the proposed geometry had to be implemented in already fabricated chip designs, the new flow cell was incorporated in design 3 as shown in Table 2-1.This new geometry was used to model the effect on particle concentration near the receptorâs surface when four simulated flow rates were compared (0.5µL/s [Blue], 1µL/s [Green], 1.5µL/s [Red], 2µL/s [Aqua]). To see how this particle count near the chip surface would react to changes in flow cell design parameters, three modifications were made. All results are presented in Table 2-3. 26 Flow Cell Design Effect of Flow rate on the Maximum Number of Microparticles at the Surface (N=1000) at from t=0s to t=60s 1. Adapted design 2. Reduced volume 3. h= 500 µm 4. Eccentric center Table 2-3. Effect of four flow rates on the particle count within 1 µm of the chip surface in 4 chamber designs. 27 The first design presented is no different from design 3 (Table 2-1). In the second one, the center inlet was kept narrow till nearer to the chip surface. The third change in design kept the narrow inlet of the second design, but also reduced the chamber height from 1.12 mm to 500 µm. The last design modification kept all previous two changes but offset the inlet to one side creating an eccentrically shaped outlet. This was to probe the effect of imperfections in the assembly process during device manufacturing. Design 1 yielded the highest number of particles at the surface of the chip with 75 particles at two different flow rates: 1.5µL/s and 2µL/s. It is also worth mentioning that it was the only design that provided uniform and constant particle amount at the chip surface during the 60 s simulation window regardless of the flow rate. This can be attributed to the bell-shape at the inlet which allows a larger cross section of bulk to flow normal to the receptors and results in the particles slowing down as they hit the receptorâs area. As for the other 3 modifications, the highest yield was provided by design number 3, where reducing the chamber height to 500 µm and omitting the âbell-shapeâ geometry resulted in a maximum of 45 particles at 2µL/s. Similarly to the last 25s in the graph of design 1, a direct relationship can be observed between the flow rates and the particle amounts, where the highest flow rates are the ones that present more particles within the 1µm distance from the surface. Design 2 shows the greatest variability between the highest flow rate (2µL/s) and the rest of the flow rates, and it also yielded the lowest number of particles out of the 4 designs. By eliminating the âbell-shapeâ from this design, the geometry doesnât allow the particles to reduce their kinetic energy, which might prevent them from staying close to the receptor before exiting 28 through the outlet. In order to increase the particle interaction with the sensing surface, the geometry would need to undergo further modifications such as a smaller chamber height as shown in design 3. This reduced height forces more particles to pass nearer to the chipâs surface, and thereby increases the particle count within 1 µm of the receptors. Lastly, considering the previous values associated with the geometry number 3, design 4 does show a decrease in the number of particles for all flow rates except for the 0.5µL/s. Therefore, imperfections in the manufacturing process would influence the particle distribution, especially at higher flow rates. Depending on the sensitivity of the application, this should be addressed or carefully monitored when designing the manufacturing process. 2.5 Summary After analyzing the different numerical simulations that characterized and compared the different geometries, the proposed design (Figure 2-7) provides a unique flow regime to enhance the detection of particles. It not only allows for a more uniform flow, but an increased number of particles interacting with the bioreceptor area, thus improving the efficiency and sensitivity of the detection, even with the incorporation of a determined flow rate. However, because of the complexity of experimental phenomena that occur during the detection process such as hybridization reactions, adsorption to the sensorâs surface or behavior of charged particles in an electric field, flow simulations are only accurate to a certain extent. For this reason, the proposed design should be fabricated and experimentally tested to verify the results presented in this Chapter. 29 Flow Optimization for Continuous Biosensors 3.1 Overview In order to increase the volume that Bio-Radâs proprietary technology could analyze, the detection system had to go from monitoring stationary fluid to analyzing a continuous flow while still ensuring accurate detection. For this, the identification of an optimal flow rate was crucial. To achieve this, an experiment was designed to determine the effect of four different flow rates (1 ðl/s, 2 ðl/s, 5 ðl/s, and 10 ðl/s) on the binding of a widely known complex in molecular science: Streptavidin (SA) and Biotin (B). Measurements were compiled by Square Wave Voltammetry (SWV) and experiments were conducted off-campus in Seattle, USA, at the facilities of the industry partner during consecutive 5 days in May 2019. 3.2 Background The high selectivity and stable interaction between Streptavidin and Biotin are just a couple of factors that explain their ubiquitous presence in experimental designs. Biotin has a low molecular weight (MW) of 2.44.31 g/mol, so it can be classified as a small molecule. This facilitates the interaction with the four binding sites of the ~70 kDa homotetramer protein, Streptavidin, and provides a very high affinity of Ka = 1 Ã 10â 15 M, which is very close to a covalent binding. Considering a label-based electrochemical detection method, the redox-active indicator Methylene Blue (7-bis(dimethylamino)phenazathio-nium chloride) (MB) was used [70], [74]. It provides several advantages like high discrimination between binding affinities of ssDNA & dsDNA when used for DNA detection, low required potential (0.1 Vâ0.4 V), low cost to produce, and environmentally friendly. In general, the strategy of this type of detection is based on the soak or 30 flow of the buffer solution that contains the redox indicator to facilitate the interaction with the previously attached probes at the surface of the chip. Higher interactions (accumulations) of the MB onto the SA surface would result in a higher electrochemical response [89]â[92]. The detection of these changes is key for this type of biosensor. In addition, the indicatorâs redox reaction current flow is highly reversible, even with low voltage levels. This allows the indicator to provide high selectivity/sensitivity and makes it commonly used in voltammetry responses. [97], [98] For electrochemical transducers, voltammetry includes a wide range of techniques where interfacial charge transfer processes, caused by an external electrical potential difference in an electrolytic cell, can be analyzed [78]. Its main objective is to characterize redox species by providing information about the interactions occurring at the working electrode [93]â[95]. In the same way as other electrochemical mechanisms, it relies on the use of the three-electrode systems: the working electrode (WE), the reference electrode (RE) and the counter electrode (CE). The reference electrode is used to determine the constant electrical potential because of its robust and constant chemical composition, and this allows it to be a reference to the relative potential of the WE with the use of the Potentiostat. As for the WE and RE, there is a current flow between them that allows the WE to carry information of the charge transfer occurring at its electrode-electrolyte interface. The electrical potential difference between the WE and the solution drives the electrochemical reaction. The outcome of voltammetric experiments is graphically represented in a voltammogram. Its curve presents the variation between the current (I) and the controlled potential (E) at the WE (relative to the constant potential of the RE) [97], [101]. A voltammogram example is shown below in Figure 3-1 and Figure 3-2. 31 Table 3-1. Experimental conditions according to the flow rate used and the four different time intervals at which measurements were taken. 3.3 Experimental Procedure and Results Using the proprietary chips and protocols of Bio-Rad, 14 chips were cleaned, coated with SA, and analyzed by flowing Methylene Blue-Biotin (MBB) in 5mM Borate (pH 8.5) at different flow rates using SWV with the aid of a GAMRY Reference 600+TM Potentiostat. All reagents were acquired from Sigma Aldrich. The procedure was as follows: 1. 16 chips were cleaned with Cyclic Voltammetry (CV). During this process, one of the chips was damaged during the cleaning process and another one was used to compare the readings between cyclic voltammetry (CV) and SWV. After analyzing the results, SWV provided a higher peak than CV, thus SWV was used for all further measurements (Figure 3-1). 2. The remaining 14 chips were tested using the experimental conditions shown in Table 3-1. Experimental Condition Time Flow rate T1 (min) T2 (min) T3 (min) T4 (min) I Negative Control-Static (2 replicates) 5 10 15 30 II Static (3 replicates) 5 10 15 30 III 1 ðl/s (3 replicates) 5 10 15 30 IV 2 ðl/s (2 replicates) 2.5 5 7.5 15 V 5 ðl/s (2 replicates) 1 2 3 6 VI 10 ðl/s (2 replicates) 0.5 1 1.5 3 32 3. For each experiment, the same volume of sample was used, and current variation was measured after the 4 different time points described in Table 3-1. Each measurement had 3 technical repetitions and their respective average was used for the results (Figure 3-1B). 4. Peak height and position were determined for each experimental graph by calculating the difference of the maximum and minimum points of the net current curve: ð¼ððð¡ = ð¼ðððð¤ððð â ð¼ððð£ððð ð (Figure 3-2), using GAMRY ECHEM ANALYSTTM software. Figure 3-1. A) Example of a voltammogram overlay for the static condition at the four different time intervals shown in image B. B) Voltammograms of the three technical repetitions for each time interval. Figure 3-2. Forward (blue), reverse (green), and net (red) current curves for SWV. Reproduced from [101] 33 5. After calculating the absolute peak value of each measurement, the average of their 3 technical repetitions and their respective standard deviation were summarized in table Table 3-2 and graphed in Figure 3-4. Additionally, without the static experimental condition being graphed, the variability found between the 14 chips that were tested can be clearly observed in Figure 3-3, as well as the current (I) differences between the four different times at which the measurements were taken. Figure 3-3. Box plot of peak measurements in µA. Comparison of different flow rate conditions at each time period: T1, T2, T3, T4 34 By analyzing the five different flow rates and the four different measurement times in Figure 3-4 it is clear that the static conditions provide the highest accumulation of MB at the surface of the chip generating the greatest peak height. It is important to remember that at the beginning of the experiment, it is assumed that only the reduction reaction of the dissolved species (SA-MBB) is happening, and that they are homogenously distributed throughout the electrolyte solution at some bulk concentration. Therefore, during the experiment, the potential of the WE progressively increases, which in turn shifts the electrode reaction to the right causing the current flow to occur. Both Reduction and Oxidation species concentrations at the electrode surfaces (also referred to as interface) will then differ from the bulk ones. In other words, as the species of the reduction reaction are consumed and the oxidation ones are produced, the concentration of the Reduction species at the interface will be less than the bulk concentration while the concentration of Oxidation species at the interface will be greater than the bulk (ð¶ð). Mathematically, since they change with time (ð¡) and distance (ð¥), their concentration can become a function of those variables: ð¶(ð¥, ð¡). For a surface concentration (ð¶ð ), this would be equal to ð¶(0, ð¡). The concentration gradients that occur close to the electrode surface cause mass transfer through diffusion. Because of this phenomenon, the peak current (ð¼ð) becomes a linear function of the ð¶ð of the initial electroactive reactant by ð¼ð = ð â ð¶ðâð ðð, where ð is the constant for determined experimental conditions [97], [101]. This linear relationship between the bulk concentration and the peak current is the basis for the analytical applications of voltammetry and is what allows us to evaluate the redox reactions that occur during the binding of SA-MBB by comparing the peak values. 35 In general, when analyzing the current variation between the different flows and the static condition, MB oxidation decreased when any of the flows were applied (Figure 3-4). However, after comparing the data quantitively (Table 3-2), the results indicate the possibility of an optimal flow rate that could allow for similar binding efficiency to static conditions with a reduced processing time. This would enable the analysis of a higher sample volume within similar timeframes. For cases such as 10 ðL/s, it was observed that after 3 minutes it could achieve a Figure 3-4. Peak measurements in µA. Individual flow rate results (0, 1, 2, 5, 10 µl/s) 36 similar peak height (29.35 ± 16.66) to that of 1 ðL/s for 15 minutes (29.47 ± 13.33), and even ½ of the static condition for 5 minutes (57.69 ± 74.48). Nevertheless, as shown by the error bars in Figure 3-3 & Figure 3-4 and the different peak measurements between the chips that were tested under the same experimental conditions that there is a high variability between them that would require more data collection from experimental replicates to validate results. Table 3-2. Absolute average peak current values (μA) measured in all experimental conditions 37 3.4 Summary, Limitations and Future Work After comparing the binding of SA and MBB between the four different flow rates of 1 ðl/s, 2 ðl/s, 5 ðl/s, and 10 ðl/s and the static condition, the possibility of finding an optimal flow rate that increases the volume sample without sacrificing the selectivity and the sensitivity of the detection technology was confirmed. However, because of experimental, time and cost limitations, further experiments are required to complement and validate the results. Future work should include fabricating in-house chips to reduce the variability observed, increasing the number of replicates and repetitions for robustness and reproducibility as well as changing SA concentration during the immobilization procedure and further verifying the density of SA at the WEâs surface. It is equally relevant to consider that the ultimate goal is the identification of an optimal flow rate for a DNA detection system, so the results from these experiments should be seen as a starting point in analyzing the effect of flow rates on DNA hybridization. 38 Electrohydrodynamic Mixing 4.1 Overview While particle simulations and flow experiments where being performed, another aspect of the proprietary electrochemical biosensor was being analyzed: electrostatic effects on DNA transport mechanisms. Several models and experiments have been done to study the concentration of DNA or other biomolecules at the surface of a chip due to electrophoretic accumulation. In order to analyze the movement and behavior of the particles in this biosensor a chip was microfabricated using photolithography to initially test the effect of applied voltage in negatively charged polystyrene particles of 1 µm, 3 µm, and 6 µm size. 4.2 Experimental Procedure 4.2.1 Device Microfabrication A combination of standard photolithography and wet etching processes were used to microfabricate the chips according to the design of Figure 4-1. As one of the first steps, a photo mask was designed in the CAD software DraftSight® (version 2018, www.draftSight.com) as shown in figure. They are later used in the process to transfer a determined patter onto the wafer that has been previously been coated with a positive photoresist. In this case, to maximize the Figure 4-1. Chip design (a) and dimensions (b). 39 space of the glass slides, 2 designs of bright masks were created: A) 3 sets of electrodes with 50 pairs of electrodes each and with a 10 µm gap, while B) 2 sets of electrodes with 25 pairs of electrodes each and a 10 µm gap. Once the masks were being printed in mylar plastic, the rest of the procedure continued. The next step was to fabricate the electrodes. In general, different materials have been used for electrode patterning; however, gold (Au) has become widely used because of its advantages such as oxidation resistant, high stability and inert nature. Nevertheless, one of its disadvantages is that it requires an oxide interface to adhere. Therefore, an additional layer of an oxide-forming metal (i.e. aluminum (Al), chromium (Cr)) is required for the gold to adhere to the substrate. Using the Applied Micro & Nanosystems Facility (AMNF) on campus, the glass slides used as a substrate were sputtered using equipment from Angstrom Engineering Inc. with a bilayer of Cr/Au with a thickness of 35 â« and 100 â«, respectively. The positive photoresist S1813 was then spin coated onto the sputtered substrate, followed by UV Exposure for patterning in the mask aligner (model 200, OAI). Figure 4-2. Photomask for the photolithography microfabrication. 40 The etching process was carried afterwards with MicroChem Corp. reagents: MF319 as developer and the photoresist remover 1165 as stripper. The complete process, except for the mask modeling is described in 9 steps in Figure 4-3. Figure 4-3. General microfabrication procedure 41 4.2.2 Flow Chamber Microfabrication Because of the advantages of PDMS previously mentioned in Chapter 1, as well as its cost-effectiveness and availability in the lab, the elastic polymer was used to produce the flow chamber as shown in Figure 4-1. As part of its fabrication process, a mold was 3D printed, then the PDMS was poured into it and immediately entered a vacuum chamber to remove the air bubbles generated when the PDMS monomer was mixed with its crosslinker agent. Once there were no visible bubbles, the mold was cured at 75° C and left overnight. The inlets and outlets were made the next day by drilling 2 holes with a biopsy punch tool. 4.2.3 Chip Assembly Finally, to assemble the PDMS chamber with the patterned electrodes, a contact bonding was used. Since PDMS is strongly hydrophobic, it wonât adhere to other materials unless its surface is previously exposed to oxygen plasma to modify it. For this reason, they were both placed in an oxygen plasma machine (PE-50 series, Plasma Etch Inc.) for 60 s at an oxygen flow rate of 40 sccm, 40 W power and 170 mTorr chamber pressure. Immediately after the oxygen treatment, the Figure 4-4. Design for 3D printed PDMS mold INLET OUTLET FLUID CHAMBER 42 PDMS chamber is placed against the glass substrate to form a permanent bond. Figure 4-5 shows the result of the microfabrication process. 4.2.4 Experimental Setup Since experiments were carried out in static conditions, the syringe pumps were only used to introduce the flow at the beginning. The sample injected was prepared by adding 4 µl of 2.5% aqueous suspension of carboxylate dyed polystyrene (PS) microparticles (Polybead®) with an initial concentration of 2.10 x 108 particles/ml to 20 µl of Bio-Radâs proprietary buffer ( Ω =832 ðâ1). The 3 different sizes and colors of the PS microparticles where 1 Green µm, 3 Red µm and blue 6 µm. The microfabricated chip was place on the stage of a Revolve 4, ECHO Microscope as shown inf Figure 4-6, which facilitates visualization and image recording through an iPad Pro. The RF signal generator model 81110A from Agilent Technologies IN. was used for AC voltage (1.1 V) and frequency applied (250 kHZ). Figure 4-5. Finalized chips 43 4.2.5 Electrostatic simulation Similarly to the simulations done in Chapter 1 (Section 2.3), a numerical simulation was carried out to determine the electrostatic forces involved in the detection system that could be acting on the negatively charged particles. The upper two images of Figure 4-7 (A) correspond to the electrical potential of the chip. The one to the left represents the entire fluid domain of the chamber, while the one on the right, a closer view of the electrodes. On the contrary, section B depicts the electrical field of the geometry on the right and the electric field of the gap between the electrodes on the left. Figure 4-6. Experimental setup. (Used and edited with the permission of Arash Dalili) 44 4.3 Experimental Results and Discussions After testing the effect of the applied voltage (1.1 V) in the three different diameter sizes of the negatively charges particles (1 µm, 2 µm and 3 µm), an electrohydrodynamic effect was found. This type of phenomena occurs due to the interaction between an electric field and an electrically charged fluid. Applying a voltage to the coplanar electrodes induced circulating zones inside the channel, which can be observed as 4 symmetrical vortices centered in the gap between any of the electrodes pair. However, depending on the size of the particle, the mixing effect varied. Different studies have shown that electrohydrodynamic effects depend on the electrical conductivity of the liquid. Samples with high conductivity will tend to exhibit this phenomenon at higher frequencies, while samples with lower conductivity show this effect at lower frequencies. a b Figure 4-7. Electrostatic simulations. A) Electric potential B) Electric field. 45 The behavior of the smallest sized particles clearly demonstrated a circular vortex structure which drew the particles vertically down toward the electrode tips. Once the particles reached the center between the two electrode tips, the electrohydrodynamic effect rushed them laterally. This flow pattern is initially represented in Figure 4-8, where the pattern can be seen in the fluid and a sketched cyclic pattern is included in some of the images for clarification. Figure 4-8 shows the same 1 µm particles observed under a 10x objective on the left (blue background) and under a 40x objective on the right (yellow background). Although the vortices are clearer when zoomed out, the effect on the particleâs behavior can be seen better at the higher magnification. For all figures Figure 4-8. The electrohydrodynamic effect of negatively charged PS particles of 1 µm diameter. 46 of this experiment, T0 represents the sample with no applied voltage. T1 through T5 reveal the generation of the vortex pattern due to the presence of a saturated electric field. Figure 4-8Figure 4-9 shows the same effect on bigger negatively charged PS particles (3 µm). Despite providing a clear image of the pattern developing in the particles, the mixing effect was Figure 4-9. The electrohydrodynamic effect of negatively charged PS particles of 3 µm diameter. 47 greatly reduced. The higher charge of the larger particles appeared to be repelled by the strong electric field near the electrode tips. Instead, the electrohydrodynamic effect experienced by these 3 µm particles caused them to form circular arcs around the electrode pairs, as shown. Only few of them entered the vortex and replicated the streamlines observed with 1 µm particles. Figure 4-10. The electrohydrodynamic effect of negatively charged PS particles of 6 µm diameter. 48 Lastly, Figure 4-10 shows the effect of the electrohydrodynamic effect on the negatively charged PS particles of 6 µm diameter. Similar to the 3 µm particles, the presence of the electric field caused the organization of the particles in a distinct pattern rather than their mixing. The particles were repelled to the edges of the channel, away from the electrode tips. The number of particles that got caught in the electrohydrodynamic vortex was reduced even farther than before. Considering the rapid mixing observed with 1 µm PS particles, this electrohydrodynamic effect could be harnessed in POC devices as previously demonstrated by Samiei et al. in digital microfluidic devices [61]. The agitation of the sample before detection increases the interaction between the biomolecules and the receptors, thus increasing the likelihood and/or rate of hybridization/binding of the target molecule. Experimental results show that depending on the size of the particles, controlling the frequency or voltage can enhance the mixing [61], [62]. Considering low frequencies might not provide the desired mixing effects and higher ones can disrupt the detection system, an optimal field for both parameters will exist for a determined biomolecule and sensing system. Optimizing the frequency and applied voltage instead of the geometry allows for a more versatile application since no change to the electrochemical biosensor would be needed. Overall, advantages of using this phenomenon for POC mixing would include shorter mixing times and because it can be achieved with few electrodes, miniaturization could also be simplified. 49 4.4 Summary While studying the effect of electrostatic forces on the sample, an electrohydrodynamic mixing effect was observed on negatively charged particles of PS, particularly for the smaller size of 1 µm. In many applications related to health sciences, mixing is crucial to providing the sample components with enough surface contact to increase the reaction rate. Biological samples also require mixing to avoid adsorption to the chip surface. As previously discussed, mixing in microfluidics devices can only happen through diffusion unless aided by an external actuator. Therefore, understanding how to apply the intrinsic mixing effect of electrochemical devices and what factors govern its optimization could increase the efficiency of the biochips without the need to add or integrate additional modules. This simple mixing technique is therefore highly compatible with POC device requirements. 50 Summary, Limitations and Future Work 5.1 Summary Since this study originated from an industry collaboration, the objectives for this thesis had to be translated to optimize different design aspects not only for the proprietary technology, but for any other electrochemical microfluidic biosensor. By focusing on analyzing the effect of general, but relevant, design parameters such as 1) flow distribution, 2) flow rates and 3) electrostatic effect, the optimization process was initialized. This would enable these types of devices to increase their efficiency and sensitivity/specificity in the detection of infectious diseases. For the first aspect discussed in Chapter 2, numerical simulations were carried out to provide an initial understanding of the flow distribution. This allowed the development of new geometry that provides a more uniform distribution of the sample flow, thus allowing the particles to efficiently reach the bioreceptorâs area. By increasing the symmetry of the flow pattern and the surface area that the particles reach at a given time, the hope was to facilitate the interaction between the attached bioreceptor at the surface of the chip and the target biomarker within the sample. Since this interaction is the operational basis for any type of biosensing, this optimization step would be required regardless of the sensing mechanism. Additionally, geometry variations were also compared by quantifying the number of microparticles that could reach their surfaces at a given time with the incorporation of different flow rates. The proposed geometry still provided the highest yield, even at the highest tested flow rate of 2 µL/s. 51 The next design aspect focused on the effect of different flow rates on the binding complex Streptavidin and Methylene Blue-Biotin (Chapter 3). Static conditions and the flow rates of 1 ðl/s, 2 ðl/s, 5 ðl/s, and 10 ðl/s were compared through square wave voltammetry with the objective of identifying an optimal flow rate. An optimal flow rate would allow the detection system to decrease its response time and increase the volume of sample analyzed while maintaining/increasing the selectivity and sensitivity of the detection system. Despite observing similar current responses between 3 minutes at 10 ðL/s and 5 minutes at the flow rate of 15 ðL/s, there was a very high variation among the proprietary chips that would require further experiments for result validation. Finally, an initial analysis of the electrostatic effect on negatively charged particles resulted in the observation of electrohydrodynamic mixing in Chapter 4. After comparing the three particles with different diameters (1 µm, 3 µm and 6 µm), the smallest presented the clearest streamlines of the four symmetric vortices centered in the gap of every electrode pair. Because this type of mixing does not require the implementation of additional modules and can be optimized by changing the voltage or frequency, it increases its applicability. For many biological purposes, a thorough mixing is required to prevent adsorption to the surface of the chip and/or increase the interactions between the target molecules and the bioactive surface of the chip. Both aspects lead to an increase of the reaction rate, thus increasing the detectorâs sensitivity and specificity. Overall, the results of all the experiments introduced the different factors involved in the optimization process for every design parameter considered in this study: flow distribution, flow rate and electrohydrodynamic mixing. 52 5.2 Limitations and Future Work As previously mentioned in the Challenges section of Chapter 1, the initial dependency on chips and samples from the industry partner, as well as the limitations on university equipment to fabricate chips of the same dimensions as the proprietary technology, and the high computational cost of the complex design, considerably shaped the scope of this study. As useful as the numerical simulations were for the initial geometry optimization in Chapter 2, future work for this design aspect would include coupled physics simulations, as well as fabrication and experimental testing. This would help compare the efficiency of the flow distribution in the different geometries. In other words, it would verify if the particles/biomolecules can reach the surface of the detection chip in a faster and more uniform way in order to increase the binding/hybridization rate. Before moving on to DNA molecules, building upon the work presented here, the Biotin-Streptavidin complex could be used once more to carry out an experiment for this end. A fluorescently labelled biotin could help analyze qualitatively and quantitatively the binding rate of this complex. This would complement the numerical simulation results of the proposed geometry and provide more information for its optimization. For Chapter 3, in-house microfabrication of chips with a simplified design could help reduce the chip variability presented and remove the reliance on proprietary chips. This would also allow for more replicates and repetitions that increase the robustness of the study. Equally as important, future work should include the quantification of the Streptavidin concentration at the surface, as the current study was only considered supersaturation of the surface. Future studies can then include the effect of the flow rates when the concentration of SA varies. An initial calibration curve 53 could help the understanding and quantification of the binding efficiency of Streptavidin-Methylene Blue-Biotin. Results from these future studies could then become the starting parameters to identify an optimal flow rate for a DNA-based biosensor. Regarding the electrohydrodynamic mixing effect observed in Chapter 4, future experiments to optimize this aspect should consist of determining the effect of different voltages and frequencies on different particle sizes and the mixing patterns developed. In addition, the effect of concentration should also be studied as this could modify the sampleâs conductivity, and with it, the voltage, frequency and time required for mixing. Finally, because of the complexity of experimental phenomena that can occur during electrochemical detection, the coupling of each optimized design aspect would represent the final step. The implementation of all individual optimizations would then allow microfluidic electrochemical biosensors to provide the fast, robust, more sensitive and specific detection required for Point-of-Care devices. 54 Bibliography [1] A. T. Yousefi, S. Bagheri, and N. Adib, âIntegration of biosensors based on microfluidic: A review,â Sens. Rev., vol. 35, no. 2, pp. 190â199, 2015. [2] J. C. W. E. Svendsen, Lab-on-a-Chip Devices and Micro-Total Analysis Systems. 2015. [3] C. M. Klapperich, âMicrofluidic diagnostics: Time for industry standards,â Expert Rev. Med. Devices, vol. 6, no. 3, pp. 211â213, 2009. [4] G. M. Whitesides, âThe origins and the future of microfluidics,â Nature, vol. 442, no. 7101, pp. 368â373, 2006. [5] Y. Li and X. L. Su, âMicrofluidics-based optical biosensing method for rapid detection of Escherichia coli O157:H7,â J. Rapid Methods Autom. Microbiol., vol. 14, no. 1, pp. 96â109, 2006. [6] N. Lopez-Barbosa et al., âDetection of Pathogens Using Microfluidics and Biosensors,â Biosensing Technol. Detect. Pathog. - A Prospect. W. Rapid Anal., 2018. [7] H. A. Budz, M. M. Ali, Y. Li, and R. R. Lapierre, âPhotoluminescence model for a hybrid aptamer-GaAs optical biosensor,â J. Appl. Phys., vol. 107, no. 10, 2010. [8] I. Palchetti, F. Bettazzi, and T. Baussant, Electrochemical hybridization-based biosensor in environmental monitoring. Elsevier Inc., 2018. [9] B. R. Wenner et al.,âGenetically designed biosensing systems for high-throughput screening of pharmaceuticals, clinical diagnostics, and environmental monitoring,â Adv. Fluoresc. Sens. Technol. V, vol. 4252, no. May 2001, pp. 59â70, 2001. [10] B. Hyacinthe, âConceptual design of a microfluidic suppressor to protect against potentially lethal printing devices: A scenario-based physical cyber security measure,â in 55 ICIW 2007: 2nd International Conference on i-Warfare and Security, 2007, pp. 101-11-. [11] J. Hu et al., âPortable microfluidic and smartphone-based devices for monitoring of cardiovascular diseases at the point of care,â Biotechnol. Adv., vol. 34, no. 3, pp. 305â320, 2016. [12] A. Manuscript, âinfectious diseases,â vol. 14, no. 2, pp. 225â244, 2014. [13] K. Mahato, P. Kumar, and M. Pranjal, âFundamentals and commercial aspects of nanobiosensors in point â of â care clinical diagnostics,â 3 Biotech, vol. 8, no. 3, pp. 1â14, 2018. [14] A. Tay, A. Pavesi, S. R. Yazdi, C. T. Lim, and M. E. Warkiani, âAdvances in microfluidics in combating infectious diseases,â Biotechnol. Adv., vol. 34, no. 4, pp. 404â421, 2016. [15] K. Mahato, A. Srivastava, and P. Chandra, âPaper based diagnostics for personalized health care: Emerging technologies and commercial aspects,â Biosens. Bioelectron., vol. 96, no. February, pp. 246â259, 2017. [16] S. E. McCalla and A. Tripathi, âMicrofluidic Reactors for Diagnostics Applications,â Annu. Rev. Biomed. Eng., vol. 13, no. 1, pp. 321â343, 2011. [17] P. Yager, G. J. Domingo, and J. Gerdes, âPoint-of-Care Diagnostics for Global Health,â Annu. Rev. Biomed. Eng., vol. 10, no. 1, pp. 107â144, 2008. [18] S. Choi, âPowering point-of-care diagnostic devices,â Biotechnol. Adv., vol. 34, no. 3, pp. 321â330, 2016. [19] L. Gervais, N. De Rooij, and E. Delamarche, âMicrofluidic chips for point-of-care immunodiagnostics,â Adv. Mater., vol. 23, no. 24, 2011. [20] S. Kumar et al., âMicrofluidic-integrated biosensors: Prospects for point-of-care 56 diagnostics,â Biotechnol. J., vol. 8, no. 11, pp. 1267â1279, 2013. [21] M. Zarei, âPortable biosensing devices for point-of-care diagnostics: Recent developments and applications,â TrAC - Trends Anal. Chem., vol. 91, pp. 26â41, 2017. [22] W. Jung, J. Han, J. W. Choi, and C. H. Ahn, âPoint-of-care testing (POCT) diagnostic systems using microfluidic lab-on-a-chip technologies,â Microelectron. Eng., vol. 132, pp. 46â57, 2014. [23] WHO, âWorld Health Statistics Sdg,â World Heal. Stat. 2016, p. 130, 2016. [24] P. O. Gubbins, M. E. Klepser, A. J. Adams, D. M. Jacobs, K. M. Percival, and G. B. Tallman, âPotential for Pharmacy â Public Health Collaborations Using Pharmacy-Based Point-of-Care Testing Services for Infectious Diseases,â vol. 23, no. 6, pp. 593â600, 2017. [25] Grace Wu & Muhammed HZaman, âLow-cost tools for diagnosing and monitoring HIV infection in low-resources settings,â Bulletin of the World Health Organization, 2012. [26] A. Dodge et al., âPDMS-based microfluidics for proteomic analysis,â Analyst, vol. 131, no. 10, pp. 1122â1128, 2006. [27] A. Niemz, T. M. Ferguson, and D. S. Boyle, âPoint-of-care nucleic acid testing for infectious diseases,â Trends Biotechnol., vol. 29, no. 5, pp. 240â250, 2011. [28] C. Zhao, M. M. Thuo, and X. Liu, âA microfluidic paper-based electrochemical biosensor array for multiplexed detection of metabolic biomarkers,â Sci. Technol. Adv. Mater., vol. 14, no. 5, 2013. [29] S. M. Kim, H. Lee, K. Yang, P. Sun, M. Kim, and S. M. Kim, âCell research with physically modified microfluidic channelsâ¯: A review,â pp. 1015â1023, 2008. [30] Y. Song, Y. Y. Huang, X. Liu, X. Zhang, M. Ferrari, and L. Qin, âPoint-of-care technologies for molecular diagnostics using a drop of blood,â Trends Biotechnol., vol. 57 32, no. 3, pp. 132â139, 2014. [31] F. Cui, M. Rhee, A. Singh, and A. Tripathi, âMicrofluidic Sample Preparation for Medical Diagnostics,â Annu. Rev. Biomed. Eng., vol. 17, no. 1, pp. 267â286, 2015. [32] S. Mishra et al., âRecent advances in salivary cancer diagnostics enabled by biosensors and bioelectronics,â Biosens. Bioelectron., vol. 81, pp. 181â197, 2016. [33] A. W. Martinez et al., âSimple Telemedicine for Developing Regions: Camera Phones and Paper-Based Microfluidic Devices for Real-Time, Off-Site Diagnosis,â Anal. Chem., vol. 80, no. 10, pp. 3699â3707, 2008. [34] K. F. Lei, âMicrofluidic systems for diagnostic applications: A review,â J. Lab. Autom., vol. 17, no. 5, pp. 330â347, 2012. [35] C. K. Dixit, Fundamentals of fluidics. 2016. [36] M. Zarei, âAdvances in point-of-care technologies for molecular diagnostics,â Biosens. Bioelectron., vol. 98, no. July, pp. 494â506, 2017. [37] G. Luka et al., âMicrofluidics Integrated Biosensorsâ¯: A Leading Technology towards Lab-on-a-Chip and Sensing Applications,â pp. 30011â30031, 2015. [38] L. Gervais, N. De Rooij, and E. Delamarche, âMicrofluidic chips for point-of-care immunodiagnostics,â Adv. Mater., vol. 23, no. 24, 2011. [39] M. I. H. Ansari, S. Hassan, A. Qurashi, and F. A. Khanday, âMicrofluidic-integrated DNA nanobiosensors,â Biosens. Bioelectron., vol. 85, pp. 247â260, 2016. [40] M. Sonker, V. Sahore, and A. T. Woolley, âRecent advances in microfluidic sample preparation and separation techniques for molecular biomarker analysis: A critical review,â Anal. Chim. Acta, vol. 986, pp. 1â11, 2017. [41] J. M. Morrissette, P. S. Mahapatra, A. Ghosh, R. Ganguly, and C. M. Megaridis, âRapid, 58 self-driven liquid mixing on open-surface microfluidic platforms,â Sci. Rep., vol. 7, no. 1, pp. 1â13, 2017. [42] C. D. Chin, V. Linder, and S. K. Sia, âCommercialization of microfluidic point-of-care diagnostic devices,â Lab Chip, vol. 12, no. 12, pp. 2118â2134, 2012. [43] L. R. Volpatti and A. K. Yetisen, âCommercialization of microfluidic devices,â Trends Biotechnol., vol. 32, no. 7, pp. 347â350, 2014. [44] M. Safavieh, S. Nahar, M. Zourob, and M. U. Ahmed, Microfluidic biosensors for high throughput screening of pathogens in food. Elsevier Ltd., 2015. [45] P. N. Nge, C. I. Rogers, and A. T. Woolley, âAdvances in Micro fl uidic Materials , Functions , Integration , and Applications,â 2013. [46] L. D. Williams, J. Bishop, S. Blair, D. W. Peters, and M. Okandan, âToward a disposable real-time DNA biosensing platform,â Microfluid. BioMEMS, Med. Microsystems II, vol. 5345, no. December 2003, p. 61, 2004. [47] M. Dou, X. Li, J. Sanchez, and G. Scott, âA Versatile PDMS/Paper Hybrid Micro fl uidic Platform for Sensitive Infectious Disease Diagnosis,â 2014. [48] J. Moon, N. Thapliyal, K. Khadim, and R. N. Goyal, âBiosensors and Bioelectronics Conducting polymer-based electrochemical biosensors for neurotransmittersâ¯: A review,â Biosens. Bioelectron., vol. 102, no. November 2017, pp. 540â552, 2018. [49] A. W. Martinez, S. T. Phillips, G. M. Whitesides, and E. Carrilho, âDiagnostics for the Developing World: Micro fluidic Paper-Based Analytical Devices,â Anal. Chem., vol. 82, no. 1, pp. 3â10, 2010. [50] Z. Li et al., âPen-on-paper strategy for point-of-care testing: Rapid prototyping of fully written microfluidic biosensor,â Biosens. Bioelectron., vol. 98, no. June, pp. 478â485, 59 2017. [51] J. Hu et al., âAdvances in paper-based point-of-care diagnostics,â Biosens. Bioelectron., vol. 54, pp. 585â597, 2014. [52] T. Review, âA review of microfabrication techniques and dielectrophoretic microdevices for,â 2014. [53] G. P. Nikoleli, C. G. Siontorou, D. P. Nikolelis, S. Karapetis, and S. Bratakou, Prototype biosensing devices: Design and microfabrication based on nanotechnological tools for the rapid in the field detection of food toxicants and environmental pollutants. Elsevier Inc., 2018. [54] D. Erickson and D. Li, âIntegrated microfluidic devices,â Anal. Chim. Acta, vol. 507, no. 1, pp. 11â26, 2004. [55] A. T. Yousefi, S. Bagheri, and N. Adib, âIntegration of biosensors based on microfluidic: A review,â Sens. Rev., vol. 35, no. 2, pp. 190â199, 2015. [56] N. Cohen, P. Sabhachandani, A. Golberg, and T. Konry, âApproaching near real-time biosensing: Microfluidic microsphere based biosensor for real-time analyte detection,â Biosens. Bioelectron., vol. 66, pp. 454â460, 2015. [57] B. H. Weigl, R. L. Bardell, and C. R. Cabrera, âLab-on-a-chip for drug development,â Adv. Drug Deliv. Rev., vol. 55, no. 3, pp. 349â377, 2003. [58] F. Raynal, A. Beuf, and P. Carrière, âNumerical modeling of DNA-chip hybridization with chaotic advection,â Biomicrofluidics, vol. 7, no. 3, 2013. [59] J. K. Chen and R. J. Yang, âVortex generation in electroosmotic flow passing through sharp corners,â Microfluid. Nanofluidics, vol. 5, no. 6, pp. 719â725, 2008. [60] X. B. Zhao, F. Pan, P. Coffey, and J. R. Lu, âCationic copolymer-mediated DNA 60 immobilization: Interfacial structure and composition as determined by ellipsometry, dual polarization interferometry, and neutron reflection,â Langmuir, vol. 24, no. 23, pp. 13556â13564, 2008. [61] E. Samiei, M. D. De Leon Derby, A. Van Den Berg, and M. Hoorfar, âAn electrohydrodynamic technique for rapid mixing in stationary droplets on digital microfluidic platforms,â Lab Chip, vol. 17, no. 2, pp. 227â234, 2017. [62] A. Castellanos, A. Ramos, and A. Gonz, âElectrohydrodynamics and dielectrophoresis in microsystemsâ¯: scaling laws,â 2003. [63] G. P. Nikoleli, C. G. Siontorou, D. P. Nikolelis, S. Bratakou, S. Karapetis, and N. Tzamtzis, Biosensors based on microfluidic devices lab-on-a-chip and microfluidic technology. Elsevier Inc., 2018. [64] J. P. Lafleur, A. Jönsson, S. Senkbeil, and J. P. Kutter, âRecent advances in lab-on-a-chip for biosensing applications,â Biosens. Bioelectron., vol. 76, pp. 213â233, 2016. [65] C. Karunakaran, R. Rajkumar, and K. Bhargava, âIntroduction to Biosensors,â Biosens. Bioelectron., no. June, pp. 1â68, 2015. [66] S. Ruffin, I. A. Hung, U. M. Koniges, and R. Levicky, âElectrostatic cycling of hybridization using nonionic DNA mimics,â ACS Sensors, vol. 2, no. 7, pp. 892â896, 2017. [67] L. Yuan, W. Tu, J. Bao, and Z. Dai, âVersatile biosensing platform for DNA detection based on a DNAzyme and restriction-endonuclease-assisted recycling,â Anal. Chem., vol. 87, no. 1, pp. 686â692, 2015. [68] J. Veselinovic, Z. Li, P. Daggumati, and E. Seker, âElectrically guided DNA immobilization and multiplexed DNA detection with nanoporous gold electrodes,â 61 Nanomaterials, vol. 8, no. 5, 2018. [69] J. Bishop, C. Wilson, A. M. Chagovetz, and S. Blair, âReal-time optical detection of competitive surface hybridization on microarrays,â Adv. Biomed. Clin. Diagnostic Syst. V, vol. 6430, no. February 2007, p. 643002, 2007. [70] M. Stelzle, M. Dürr, M. Cieplik, and W. Nisch, âOn-chip electrophoretic accumulation of DMA oligomers and streptavidin,â Anal. Bioanal. Chem., vol. 371, no. 2, pp. 112â119, 2001. [71] B. N. Johnson and R. Mutharasan, âReduction of nonspecific protein adsorption on cantilever biosensors caused by transverse resonant mode vibration,â Analyst, vol. 139, no. 5, pp. 1112â1120, 2014. [72] I. Y. Wong and N. A. Melosh, âAn electrostatic model for DNA surface hybridization,â Biophys. J., vol. 98, no. 12, pp. 2954â2963, 2010. [73] J. Wang, âElectrochemical nucleic acid biosensors,â vol. 469, pp. 63â71, 2002. [74] J. I. A. Rashid and N. A. Yusof, âThe strategies of DNA immobilization and hybridization detection mechanism in the construction of electrochemical DNA sensor: A review,â Sens. Bio-Sensing Res., vol. 16, no. April, pp. 19â31, 2017. [75] A. N. Rao and D. W. Grainger, âBiophysical properties of nucleic acids at surfaces relevant to microarray performance,â Biomater. Sci., vol. 2, no. 4, pp. 436â471, 2014. [76] L. Wang, X. Chen, X. Wang, X. Han, S. Liu, and C. Zhao, âElectrochemical synthesis of gold nanostructure modified electrode and its development in electrochemical DNA biosensor,â Biosens. Bioelectron., vol. 30, no. 1, pp. 151â157, 2011. [77] E. E. Ferapontova, âHybridization Biosensors Relying on Electrical Properties of Nucleic Acids,â Electroanalysis, vol. 29, no. 1, pp. 6â13, 2017. 62 [78] Y. Bo, H. Yang, Y. Hu, T. Yao, and S. Huang, âA novel electrochemical DNA biosensor based on graphene and polyaniline nanowires,â Electrochim. Acta, vol. 56, no. 6, pp. 2676â2681, 2011. [79] T. S. Ramulu et al., âNanowires array modified electrode for enhanced electrochemical detection of nucleic acid,â Biosens. Bioelectron., vol. 40, no. 1, pp. 258â264, 2013. [80] S. Roy, X. Chen, M. H. Li, Y. Peng, F. Anariba, and Z. Gao, âMass-produced nanogap sensor arrays for ultrasensitive detection of DNA,â J. Am. Chem. Soc., vol. 131, no. 34, pp. 12211â12217, 2009. [81] A. Sassolas, B. D. Leca-Bouvier, and L. J. Blum, âDNA biosensors and microarrays,â Chem. Rev., vol. 108, no. 1, pp. 109â139, 2008. [82] S. Cagnin et al., âOverview of electrochemical DNA biosensors: New approaches to detect the expression of life,â Sensors (Switzerland), vol. 9, no. 4, pp. 3122â3148, 2009. [83] S. Krainer, âMicroarray based real-time analysis of nucleic acid hybridization kinetics and thermodynamics,â 2011. [84] A. M. Chiorcea-Paquim, R. Eritja, and A. M. Oliveira-Brett, âElectrochemical and AFM characterization of G-quadruplex electrochemical biosensors and applications,â J. Nucleic Acids, vol. 2018, 2018. [85] E. Povedano et al., âElectrochemical affinity biosensors for fast detection of gene-specific methylations with no need for bisulfite and amplification treatments,â Sci. Rep., vol. 8, no. 1, pp. 1â11, 2018. [86] J. Izuan, A. Rashid, and N. Azah, âSensing and Bio-Sensing Research The strategies of DNA immobilization and hybridization detection mechanism in the construction of electrochemical DNA sensorâ¯: A review,â Sens. Bio-Sensing Res., vol. 16, no. August, pp. 63 19â31, 2017. [87] T. M. Wynne, C. McCallum, J. T. Del Bonis-Odonnell, P. Crisalli, and S. Pennathur, âHybridization thermodynamics of DNA oligonucleotides during microchip capillary electrophoresis,â Anal. Chem., vol. 87, no. 5, pp. 2811â2818, 2015. [88] J. R. Pascault and H. S. Zhou, âEnhancement of DNA hybridization kinetics in microarrays by convective transport,â Chem. Eng. Commun., vol. 195, no. 2, pp. 167â186, 2008. [89] A. Munir et al., âModeling hybridization kinetics of gene probes in a DNA biochip using FEMLAB,â Microarrays, vol. 6, no. 2, 2017. [90] I. Cheng, âCOMSOL Multiphysics for the Designs and Applications on Biomicrofluidic Chips Curriculum Vitae,â 2013. [91] COMSOL, âNavier-Stokes Equations,â 2017. [Online]. Available: https://www.comsol.com/multiphysics/navier-stokes-equations. [92] âElectrostatics Theory,â 2019. [Online]. Available: https://www.comsol.com/multiphysics/electrostatics-theory. [93] S. Kyu Kim, H. Cho, H. J. Park, D. Kwon, J. Min Lee, and B. Hyun Chung, âNanogap biosensors for electrical and label-free detection of biomolecular interactions,â Nanotechnology, vol. 20, no. 45, 2009. [94] D. Bai et al., âA rapid biosensor for highly sensitive protein detection based on G-quadruplex-Thioflavin T complex and terminal protection of small molecule-linked DNA,â Sensors Actuators, B Chem., vol. 252, pp. 1146â1152, 2017. [95] R. Santacroce et al., âAnalysis of clinically relevant single-nucleotide polymorphisms by use of microelectronic array technology,â Clin. Chem., vol. 48, no. 12, pp. 2124â2130, 64 2002. [96] J. Strömqvist et al., âBinding of Biotin to Streptavidin: A combined fluorescence correlation spectroscopy and time-resolved fluorescence study,â Eur. Phys. J. Spec. Top., vol. 199, no. 1, pp. 181â194, 2011. [97] D. Ho, âDetection and Melting of Surface-Bound DNA using a Purely Electrochemical Approach,â 2018. [98] F. Fixe, H. M. Branz, N. Louro, V. Chu, D. M. F. Prazeres, and J. P. Conde, âElectric-field assisted immobilization and hybridization of DNA oligomers on thin-film microchips,â Nanotechnology, vol. 16, no. 10, pp. 2061â2071, 2005. [99] N. Akkilic, M. Kamran, R. Stan, and N. J. M. Sanghamitra, âVoltage-controlled fluorescence switching of a single redox protein,â Biosens. Bioelectron., vol. 67, pp. 747â751, 2015. [100] Y. U. Kayran, D. Jambrec, and W. Schuhmann, âNanostructured DNA Microarrays for Dual SERS and Electrochemical Read-out,â Electroanalysis, vol. 31, no. 2, pp. 267â272, 2019. [101] V. Mirceski, S. Skrzypek, and L. Stojanov, âSquare-wave voltammetry,â ChemTexts, vol. 4, no. 4, p. 0, 2018.
Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Optimization of electrochemical microfluidic biosensor design for point of care devices
Text