Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Zia, Abdul Basit
Doctor of Philosophy - PhD
University of British Columbia
This dissertation explores the development and calibration of a cost-effective, automated microfluidic droplet-on-demand generation system by adapting fused deposition modeling (FDM) 3D printer to overcome the high costs and complexities traditionally associated with droplet microfluidics. The developed systems can move the inlet of the microfluidic system in cartesian coordinates and generate droplets from various analytes, termed automated dynamic inlet microfluidic systems (ADIM systems). A 3D printer (Prusa Mini+) is adapted with novel 3D printed modules into a droplet-on-demand system that generates combinatorial droplets from a standard 96-well plate in dual-phase microfluidics. The calibration methodology developed would allow any FDM printer to create monodisperse droplets (coefficient of variance (CV%) < 5%) from well plates or vials of any geometry submerged in oil. The system maintains precision across various volumes while maintaining a C.V. range of 0.81% to 3.61%. The cost of the system developed is 70% less than commercially available droplet generation packages. The developed ADIM system is used to generate trains of droplets of analyte and quantified across a broad concentration range (0.0128 μg/mL to 5000 μg/mL). The analysis methodology utilizes MATLAB for video frame extraction and processing, and develops multiple metrics for the droplet images, each providing a different insight. This image analysis approach successfully demonstrates the system’s ability to differentiate between agglutinated and non-agglutinated droplets across the entire range of concentrations. Moreover, the system can classify the agglutination into three regions (Band 1: 0.0128 to 0.32 μg/mL, Band 2: 1.6 to 40 μg/mL, and Band 3: 200 to 5000 μg/mL) with a 97.50% accuracy for image analysis. Building upon our initial model, the second ADIM system simplifies calibration methodology with an inverted inlet to produce mono-disperse droplets (CV% < 2% for a train of 100 droplets). The developed system is 30% more economical than the previous iteration. Additionally, the system’s utility in quantifying agglutination assays is highlighted using image analysis, which is capable of distinguishing between agglutinated and non-agglutinated samples. Image analysis metrics used in quantification include particle size and variance-based indicator (VBI).
Development of Microfluidic Platformsfor Low-Cost and High Precision DropletGeneration and Quantification ofAgglutination AssaysbyAbdul Basit ZiaB.Sc., Ghulam Ishaq Khan Institute, Pakistan, 2011M.Eng., University of Malaya, 2014A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe College of Graduate Studies(Electrical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)January 2025© Abdul Basit Zia, 2025The following individuals certify that they have read, and recommend to theCollege of Graduate Studies for acceptance, a dissertation entitled:Development of Microfluidic Platforms for Low-Cost and HighPrecision Droplet Generation and Quantification of Aggluti-nation Assayssubmitted by Abdul Basit Zia in partial fulfilment of the requirements ofthe degree of Doctor of PhilosophyDr. Ian G. Foulds, School of EngineeringSupervisorDr. Mohammad Hossein Zarifi, School of EngineeringSupervisory Committee MemberDr. Christoper Collier, School of EngineeringSupervisory Committee MemberDr. Jennifer Frattolin, School of EngineeringUniversity ExaminerDr. Xinyu Liu, University of TorontoExternal ExamineriiAbstractThis dissertation explores the development and calibration of a cost-effective,automated microfluidic droplet-on-demand generation system by adaptingfused deposition modeling (FDM) 3D printer to overcome the high costsand complexities traditionally associated with droplet microfluidics. Thedeveloped systems can move the inlet of the microfluidic system in cartesiancoordinates and generate droplets from various analytes, termed automateddynamic inlet microfluidic systems (ADIM systems).A 3D printer (Prusa Mini+) is adapted with novel 3D printed modules intoa droplet-on-demand system that generates combinatorial droplets from astandard 96-well plate in dual-phase microfluidics. The calibration method-ology developed would allow any FDM printer to create monodisperse droplets(coefficient of variance (CV%) < 5%) from well plates or vials of any geome-try submerged in oil. The system maintains precision across various volumeswhile maintaining a C.V. range of 0.81% to 3.61%. The cost of the systemdeveloped is 70% less than commercially available droplet generation pack-ages.The developed ADIM system is used to generate trains of droplets of analyteand quantified across a broad concentration range (0.0128 µg/mL to 5000µg/mL). The analysis methodology utilizes MATLAB for video frame extrac-tion and processing, and develops multiple metrics for the droplet images,each providing a different insight. This image analysis approach successfullydemonstrates the system’s ability to differentiate between agglutinated andnon-agglutinated droplets across the entire range of concentrations. More-over, the system can classify the agglutination into three regions (Band 1:0.0128 to 0.32 µg/mL, Band 2: 1.6 to 40 µg/mL, and Band 3: 200 to 5000µg/mL) with a 97.50% accuracy for image analysis.Building upon our initial model, the second ADIM system simplifies calibra-tion methodology with an inverted inlet to produce mono-disperse droplets(CV% < 2% for a train of 100 droplets). The developed system is 30% moreiiieconomical than the previous iteration. Additionally, the system’s utility inquantifying agglutination assays is highlighted using image analysis, whichis capable of distinguishing between agglutinated and non-agglutinated sam-ples. Image analysis metrics used in quantification include particle size andvariance-based indicator (VBI).ivLay SummaryTraditional microfluidic droplet-on-demand systems are expensive and com-plex, therefore this research outlines the development and calibration of FDM3D printer into a droplet-on-demand system. This dissertation describestwo systems with distinct mechanisms for generating uniform droplets intodroplet libraries for microfluidic applications in virology, blood typing, andmicrobiology. Both systems create droplets from multiple analytes of vari-able volume with a high degree of uniformity. To demonstrate the applicationof both systems, agglutination of biotin and Streptavidin, substances com-monly used in biological studies, are quantified using image analysis. Imageanalysis techniques categorize these interactions with 97.9% accuracy, mak-ing the system highly reliable and cost-effective by reducing the cost by 70%compared to commercial systems,The developed systems would benefit labs by providing a more accessibleand affordable way to conduct complex biological experiments without spe-cialized equipment.vPrefaceThe research described in this thesis was conducted under the primary su-pervision of Dr. Ian G Foulds. Chapters 3 to 5 are manuscripts accepted orsubmitted. All journal manuscripts are prepared by myself.Chapter 3 has been published in Lab on a Chip journal, J1 listed below. Thispublication outlines the first automated dynamic inlet microfluidic (ADIM)system development and calibration. Justin Farrell and I worked on themathematical modeling, and designed and performed the experiments to-gether. I did the data analysis, and visualization, while Justin Farrell andDr. Ian G. Foulds proofread the manuscript.Chapter 4 has been submitted to Sensors and Actuators: A. Physical journal,J2 listed below. This publication shows the implementation of the developedADIM system in performing biotin-streptavidin immunoassay across a wideconcentration. Justin Farrell and I collected the data for this study. He alsohelped with the analysis and reviewing the manuscript. The manuscriptrefers to a paper presented at IEEE Biosensors Conferences 2023, London,listed as C1. The analysis of line break sensor data for the same data setis presented in this study. I worked on the analysis with Justin Farrell andproduced the figures for the manuscript.Chapter 5 has been published in Lab on a Chip journal, J3 listed below. Thispublication outlines the second ADIM system developed and its applicationin the biotin-streptavidin immunoassays. I was the sole contributor to all thework involved in this publication under the supervision of Dr. Ian Foulds.The following list provides an overview of the journal articles and conferencepapers related to the chapters of this dissertation. It includes both publishedmaterials and those currently under review or awaiting submission.− Published, Submitted, and In-preparation Journal Papers:J1. A. B. Zia, J. Farrell, and I.G. Foulds, “Automated dynamic inletmicrofluidics system: 3D printer adaptation for cost-effective, lowvivolume, on-demand multi-analyte droplet generator,” Lab on aChip, vol. 24, no. 11, pp. 3015–3026, 2024. DOI: 10.1039/d4lc00075g (Chapter 3, [1])J2. A. B. Zia, J. Farrell, and I.G. Foulds, “Integrating Image Analy-sis and Low-Cost Optical Transmittance for Quantification of Ag-glutination in a Dual-Phase Automated Dynamic Inlet Microflu-idics System,” Sensors and Actuators: A. Physical,Early Access(Chapter 4, [2])http://dx.doi.org/10.2139/ssrn.4940936J3. A. B. Zia, I.G. Foulds, “Automated Dynamic Inlet MicrofluidicsSystem: Cost-effective Biaxial Nanoliter Droplet on Demand Gen-eration Platform and Its Application in Agglutination assays,”Lab on a Chip, vol. 25,no. 1, pp 57-68,2024 (Chapter 5, [3]) DOI:10.1039/D4LC00643G− Referred Conference Publications:C1. J. Farrell, A. B. Zia, I.G. Foulds, “Low-cost optical transmit-tance measurements for the detection and quantification of agglu-tination in dual-phase lab-in-tubing microfluidics", IEEE SensorsLett 2023 Sept,7(9), pp. 1–4. doi:10.1109/lsens.2023.3300808,(Referred to in Chapter 4, [4])C2. H. Juberi, A. B. Zia, J. Farrell and I.G. Foulds, “Deep Learningand Hybrid Deep Neural Network to Quantify Agglutination inDual-Phase Lab-in-Tubing Microfluidics," in IEEE Sensors Let-ters, vol. 8, no. 8, pp. 1-4, Aug. 2024, Art no. 7003704, doi:10.1109/LSENS.2024.3423395 (Referred to in Chapter 6, [5]).− Poster Presentations:P1. A. B. Zia, I.G. Foulds, “Low-cost droplet library generator formodular lab-in-tubing systems", Proceedings of the 26th Inter-national Conference on Miniaturized Systems for Chemistry andLife Sciences (MicroTAS 2022), 2022, China (Chapter 3, [6])P2. J. Farrell, A. B. Zia, I.G. Foulds, “Low-cost time-of-flight dropletcharacterization for microfluidic assays", Proceedings of the 25thInternational Conference on Miniaturized Systems for Chemistryand Life Sciences (MicroTAS 2021), 2022, China [7](Not part ofthis thesis)I utilized generative AI (ChatGPT, and Grammarly) during the drafting ofviithis thesis, primarily to enhance the writing style and improve the overalleffectiveness and quality of the text.Ethics approval was not required for this work.viiiTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . ixList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvList of Submitted Files . . . . . . . . . . . . . . . . . . . . . . . xxAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . xxiDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxiiChapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . 11.1 Background and Motivation . . . . . . . . . . . . . . . . . . 11.1.1 Agglutination in general . . . . . . . . . . . . . . . . . 21.1.2 Agglutination types . . . . . . . . . . . . . . . . . . . 31.1.3 Detection threshold and Clinical standards . . . . . . 31.1.4 Clinical significant standards . . . . . . . . . . . . . . 41.1.5 Commercial Kits available . . . . . . . . . . . . . . . . 51.2 Dimensionless numbers in fluid mechanics . . . . . . . . . . . 61.2.1 Reynold’s Number (Re) . . . . . . . . . . . . . . . . . 61.2.2 Capillary Number (Ca) . . . . . . . . . . . . . . . . . 71.2.3 Peclet Number (Pe) . . . . . . . . . . . . . . . . . . . 81.3 Mixing in microfluidics . . . . . . . . . . . . . . . . . . . . . . 91.3.1 Within a droplet . . . . . . . . . . . . . . . . . . . . . 9ixTABLE OF CONTENTS1.4 Dissertation Structure and Organization . . . . . . . . . . . . 12Chapter 2: Literature Review . . . . . . . . . . . . . . . . . . . 142.1 Agglutination in Dual-Phase Microfluidics . . . . . . . . . . . 152.2 Agglutination Biosensors . . . . . . . . . . . . . . . . . . . . . 182.2.1 Electrical Agglutination Biosensors . . . . . . . . . . . 182.2.2 Optical Agglutination Biosensor . . . . . . . . . . . . 202.3 Agglutination in Microfluidics . . . . . . . . . . . . . . . . . . 232.3.1 Electrical detection methods for agglutination in Mi-crofluidics . . . . . . . . . . . . . . . . . . . . . . . . . 242.3.2 Optical detection methods for agglutination in Mi-crofluidics . . . . . . . . . . . . . . . . . . . . . . . . . 282.3.3 Discussion on Optical detection of agglutination mi-crofluidics . . . . . . . . . . . . . . . . . . . . . . . . . 322.4 Droplet Microfluidics . . . . . . . . . . . . . . . . . . . . . . . 332.4.1 Droplet on demand in Microfluidic Chips . . . . . . . 342.4.2 XYZ platform . . . . . . . . . . . . . . . . . . . . . . . 352.5 Research Gap and Objectives . . . . . . . . . . . . . . . . . . 40Chapter 3: Automated Dynamic Inlet Microfluidics (ADIM)System: Version A . . . . . . . . . . . . . . . . . . . 423.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423.2 The Developed ADIM System . . . . . . . . . . . . . . . . . . 433.2.1 Calibration . . . . . . . . . . . . . . . . . . . . . . . . 473.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . 523.3.1 Calibration . . . . . . . . . . . . . . . . . . . . . . . . 523.3.2 Single Train of Droplets with Constant Volume . . . . 543.3.3 Single Train of Droplets with Variable Volume . . . . 583.3.4 Multi-Analyte droplet trains . . . . . . . . . . . . . . . 593.3.5 Droplet library . . . . . . . . . . . . . . . . . . . . . . 623.3.6 Applications of Developed System . . . . . . . . . . . 643.3.7 Analysis of Random and Systematic Errors . . . . . . 653.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66Chapter 4: Integrating Image Analysis and Low-Cost OpticalTransmittance:An Application of ADIM System Version A . . . 684.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 684.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . 694.2.1 Line Break Sensor Analysis Methodology . . . . . . . 71xTABLE OF CONTENTS4.2.2 Image Analysis Methodology . . . . . . . . . . . . . . 714.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 744.3.1 Peak Analysis . . . . . . . . . . . . . . . . . . . . . . . 744.3.2 Adapting Optical Transmittance Methodology for Im-age Data Analysis . . . . . . . . . . . . . . . . . . . . 774.3.3 Image Normalization and Variability Analysis . . . . . 794.3.4 Multi-Criteria Algorithm for Quantification . . . . . . 794.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 814.4.1 Implication and future works . . . . . . . . . . . . . . 824.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Chapter 5: Automated Dynamic Inlet Microfluidics (ADIM)System: Version B . . . . . . . . . . . . . . . . . . . 845.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 845.2 Methods and Materials . . . . . . . . . . . . . . . . . . . . . . 855.2.1 Calibration . . . . . . . . . . . . . . . . . . . . . . . . 875.2.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . 905.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 905.3.1 Calibration . . . . . . . . . . . . . . . . . . . . . . . . 905.3.2 Train of Droplets with Constant Volume of Single An-alyte . . . . . . . . . . . . . . . . . . . . . . . . . . . . 925.3.3 Train of Droplets with Variable Volume of Single Analyte 955.3.4 Train of Droplets with Constant Volume of MultipleAnalytes . . . . . . . . . . . . . . . . . . . . . . . . . . 955.3.5 Straptividin Beads Tests . . . . . . . . . . . . . . . . . 985.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025.4.1 Application of Droplet Generation Platform . . . . . . 1035.4.2 Analyte Selection . . . . . . . . . . . . . . . . . . . . . 1045.4.3 Impact of Oil and Analyte Viscosity on System Per-formance . . . . . . . . . . . . . . . . . . . . . . . . . 1055.4.4 Analysis of Random and Systematic Errors . . . . . . 1055.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106Chapter 6: Conclusion . . . . . . . . . . . . . . . . . . . . . . . . 1096.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1096.2 Comparison of ADIM System Version A and B . . . . . . . . 1106.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 1116.3.1 Use of Droplet Libraries . . . . . . . . . . . . . . . . . 1116.3.2 Machine Learning . . . . . . . . . . . . . . . . . . . . 112xiTABLE OF CONTENTSBibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130Appendix A: Dimensionless Number and Plug Sedimentation Cal-culation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130Appendix B: Supplementary Material to Chapter 3 . . . . . . . . . 141Appendix C: Supplementary Material to Chapter 4 . . . . . . . . . 143Appendix D: Supplementary Material to Chapter 5 . . . . . . . . . 151xiiList of TablesTable 1.1 Available commercial kits for C-reactive Protein, andMyoglobin. . . . . . . . . . . . . . . . . . . . . . . . . 5Table 1.2 Shields Parameter and Circulation Zone for M270 andC1/T1 Beads at Different Flow Rates and Tubing Types 11Table 2.1 Summary of Dectection of Agglutination in Biosensors 23Table 2.2 Summary of Electrical Detection of Agglutination inMicrofluidics . . . . . . . . . . . . . . . . . . . . . . . . 27Table 2.3 Summary of Optical Detection of Agglutination in Mi-crofluidics . . . . . . . . . . . . . . . . . . . . . . . . . 33Table 3.1 Comparison between this study and other works . . . 44Table 3.2 Results of 3D printer movement analysis for differentprogrammed delays . . . . . . . . . . . . . . . . . . . . 48Table 4.1 Success rate comparison between different algorithms . 82Table 5.1 Comparison between this study and other works . . . 86Table 5.2 Results of 3D printer movement analysis for differentprogrammed delay executed different methods . . . . . 91Table 5.3 Comparison between this study and other works . . . 108Table 6.1 Comparison of ADIM System A and ADIM System B 111Table A.2 Shields Parameter for M270 and C1 Beads at DifferentFlow Rates and Tubing Types . . . . . . . . . . . . . . 139Table A.3 Shields Parameter and Circulation Zone for M270 andC1/T1 Beads at Different Flow Rates and Tubing Types140Table B.1 Percentage of Coefficient of Variance for Conical (Fig-ure 5 a-c) . . . . . . . . . . . . . . . . . . . . . . . . . 141Table B.2 Percentage of Coefficient of Variance for Cylindrical(Figure 5 a-c) . . . . . . . . . . . . . . . . . . . . . . . 141xiiiLIST OF TABLESTable B.1 Summary of some biomarkers with their normal rangeand cutoff value which indicate high risk . . . . . . . . 149Table B.2 Added for Dissertation: Summary of some biomark-ers with their normal range and cutoff value which in-dicate high risk . . . . . . . . . . . . . . . . . . . . . . 149xivList of FiguresFigure 1.1 Inner vortices within a Droplet . . . . . . . . . . . . . 10Figure 1.2 Beads circulation patterns within a Droplet . . . . . . 11Figure 1.3 Flow diagram of the dissertation structure . . . . . . 13Figure 2.1 Steps for direct ELISA . . . . . . . . . . . . . . . . . 14Figure 2.2 David Castro’s System Schematic with Image and Neph-elometric analysis . . . . . . . . . . . . . . . . . . . . 16Figure 2.3 Magnetic Beads based Hemagglutination . . . . . . . 17Figure 2.4 Magnetic Beads based immunoagglutination . . . . . 17Figure 2.5 Fabricated FET biosensor and its readouts at differentCRP concentrations . . . . . . . . . . . . . . . . . . 19Figure 2.6 Electrochemical sensor developed by Tang group forCRP agglutination detection . . . . . . . . . . . . . . 20Figure 2.7 Colorimetric measurements of AuNP for different con-centrations of CRP . . . . . . . . . . . . . . . . . . . 21Figure 2.8 Optomagnetic and AC susceptibility measurement ofmagnetic beads . . . . . . . . . . . . . . . . . . . . . 22Figure 2.9 Image Processing of Nanoplasmonic platform . . . . . 23Figure 2.10 Electrochemical detection of CRP agglutination . . . 25Figure 2.11 Detection of CRP agglutination using FET . . . . . . 26Figure 2.12 Detection of CRP agglutination using Voltammetry . 27Figure 2.13 Fluorescence measurement in a commercial chip usingCMOS camera . . . . . . . . . . . . . . . . . . . . . . 29Figure 2.14 Nephelometric measurement of magnetic beads in mi-croflow cytometry-based agglutination immunoassay . 29Figure 2.15 Optical Density measurement in PDMS 3D microwellchips . . . . . . . . . . . . . . . . . . . . . . . . . . . 30Figure 2.16 Microfluidic chip to collect blood sample and the ROIindicated where the variance-based and correlation-based agglutination (VBA) indicator . . . . . . . . . . 31xvLIST OF FIGURESFigure 2.17 Schematic of the continuous high-throughput microflu-idic system by David Castro . . . . . . . . . . . . . . 32Figure 2.18 Valved based droplet on demand microfluidic chip . . 35Figure 2.19 Latest version of DropLab with a customized chipwith 4 microwells (XYZ system that moves control-ling mechanism) . . . . . . . . . . . . . . . . . . . . . 36Figure 2.20 Original DropLab with a) slotted-vial array and b)multiwell plate c) droplet library stored in capillarysealed with wax . . . . . . . . . . . . . . . . . . . . . 37Figure 2.21 The ’Serial Sampling Loading’ (SSL) system . . . . . 38Figure 2.22 Schematic of droplet-on-demand platform a) with anoil-filled carousel b) Sequential operation of the devicec) Dolomite Mitos Dropix device . . . . . . . . . . . 39Figure 3.1 ADIM system version A system schematic . . . . . . 44Figure 3.2 Schematics of droplet cartridge connectors (male andfemale). a) CAD Model with O-ring b) 3D-printed(on Prusa SL1S) cartridge connectors. . . . . . . . . . 47Figure 3.3 Illustration of the mathematical model for the geom-etry of the well in a standard 96 well plate. . . . . . . 50Figure 3.4 Results of calibration of three versions of the systemfor Tsystem parameter . . . . . . . . . . . . . . . . . . 52Figure 3.5 Plots of droplet trains generated at different flow ratewith different target volume(1 µL, 1.5 µL, 2 µL) fordifferent flow rates (150 µL/min, 100 µL/min, 50 µL/min)55Figure 3.6 Box plots for variations within a train of droplets andfrom their target volumes at different flowrates (150µL/min, 100 µL/min, 50 µL/min) . . . . . . . . . . . 56Figure 3.7 A droplet train of variable target volume sequencerepeated four (4) times a) Sequence of 1.0 µL, 1.50 µL,and 2.0 µL repeated b) images of the actual dropletsgenerated of variable target volume. . . . . . . . . . . 58Figure 3.8 3D Printer movements to generate multi analyte droplettrain. . . . . . . . . . . . . . . . . . . . . . . . . . . . 60xviLIST OF FIGURESFigure 3.9 Droplet trains consisting of multiple analytes in differ-ent wells of the 96 well plate a) droplet train generatedat 100 µL/min flow rate with target volume of 1 µLconsisting of three different analytes (A,B, and C) b)droplet train generated at 100 µL/min flow rate witha sequence of 1.0 µL from analyte A, 1.50 µL fromanalyte B, and 2.0 µL from analyte C is repeated ten(10) times . . . . . . . . . . . . . . . . . . . . . . . . 61Figure 3.10 Image of a droplet train from a single analyte storedin a cartridge using custom connectors. . . . . . . . . 63Figure 3.11 Schematic of how to use the droplet cartridges . . . . 63Figure 4.1 ADIM system version A setup with PTFE tubing forimage and light transmittance (LBS) analysis a) Animage of a droplet is split into bins in both verti-cal and horizontal orientation b) Agglutination signalwaveform captured by the LBS for droplet train of 1.6µg/mL concentration . . . . . . . . . . . . . . . . . . 70Figure 4.2 Image of a droplet is split into bins of 1 pixel eachin both vertical and horizontal orientation. a) meanand standard deviation of gray values of horizontalbins b) mean and standard deviation of gray valuesfor vertical bins . . . . . . . . . . . . . . . . . . . . . 73Figure 4.3 Histogram analysis a) Full Width at Half Maximum(FWHM) for every droplet for different concentrationsb)Mean FWHM of all the droplet images for differentconcentrations . . . . . . . . . . . . . . . . . . . . . . 76Figure 4.4 Three metrics for quantification of agglutination: a)Average of image’s vertical bins standard deviationfor one image per droplet(1PerDroplet) b)The stan-dard deviation along a trace through the midline ofthe droplet’s width for one image per Droplet (1Per-Droplet) c) Mean Standard deviation calculated ofone image per droplet for all droplets of each concen-tration after image normalization (AllPerDroplet) . . 78Figure 5.1 The system diagram: setup of the system with mod-ified Prusa Mini (3D printer) and the modified 3D-printed nozzle with J bend . . . . . . . . . . . . . . . 86xviiLIST OF FIGURESFigure 5.2 Schematic to show movements of the 3D printer togenerate droplets a) one analyte scheme b) multi an-alyte scheme . . . . . . . . . . . . . . . . . . . . . . . 88Figure 5.3 Train of 20 droplets generated at 35 µL/min with atarget volume of 1 µL and the minimum possible vol-ume that can be generated at this flow rate . . . . . . 92Figure 5.4 Train of 20 droplets generated with the target volumeof 30 nL at different flow . . . . . . . . . . . . . . . . 94Figure 5.5 a) A train of droplets with variable volume, sequenceof 0.3 µL, 0.5 µL, 0.6 µL, 0.7 µL, and 0.8 µL is re-peated five (5) times b) Boxplot shows variation foreach target volume in the train of droplet . . . . . . . 96Figure 5.6 A train of droplets with constant volume from two(2) different analytes a) droplet train generated at35 µL/min flow rate with a target volume of 0.5 µLconsisting of two different analytes (A and B) b) im-age of droplet train with multiple analytes placed ina 3D-printed holder . . . . . . . . . . . . . . . . . . . 97Figure 5.7 a) Image processing steps for b) mean particle size c)variance based indicator (VBI) . . . . . . . . . . . . . 100Figure 5.8 Schematic of how a droplet cartridge can be installedin the system to generate droplet libraries (Createdwith BioRender.com) . . . . . . . . . . . . . . . . . . 104Figure 6.1 Schematic of how droplet cartridges can be used withmicrofluidic chips to run experiments (Created withBioRender.com) . . . . . . . . . . . . . . . . . . . . . 112Figure B.1 Train of 100 single analyte constant volume (0.75µL)droplets from 100µL . . . . . . . . . . . . . . . . . . . 142Figure B.2 Pressure stabilization for vacuum pump in ADIM sys-tem version A to maintain negative pressure . . . . . 142Figure C.1 Image processing steps . . . . . . . . . . . . . . . . . 146Figure C.2 Mean for Vertical Bins (Bin Size 4 pixels) for differ-ent concentration. Multiple lines are for each imagecaptured of different droplets for that concentration . 146Figure C.3 Mean for Horizontal Bins (Bin Size 4 pixels) for differ-ent concentration. Multiple lines are for each imagecaptured of different droplets for that concentration . 147xviiiLIST OF FIGURESFigure C.4 Droplet of 1.6 µg/mL captured at different times withinthe viewing window. The red circle shows the largestagglomerate’s shape-changing as it tumbles . . . . . . 150Figure D.1 Train of 100 droplet of 1 µL target volume is generatedat 35µL/min. . . . . . . . . . . . . . . . . . . . . . . 151Figure D.2 5 droplet of generated at 35µL/min to show the min-imum droplet volume possible when tdelay is equal tozero compared to when G4 P0 command is used fortdelay. . . . . . . . . . . . . . . . . . . . . . . . . . . 152Figure D.3 Train of 100 droplet of 1 µL target volume is generatedat 35µL/min. . . . . . . . . . . . . . . . . . . . . . . 153Figure D.4 Train of 15 droplets of 12 nL target volume is gen-erated at 1.5µL/min. Repeated experiment three (3)times . . . . . . . . . . . . . . . . . . . . . . . . . . . 154Figure D.5 Encircled parts show water droplets adhering to theTygon tubing wall . . . . . . . . . . . . . . . . . . . 154Figure D.6 Train of 20 droplets 0f 30 nL with varying delay. De-lays of 100ms, 125ms,150ms, and 200ms are programmed.155Figure D.7 Train of 10 droplets 0f 0.3 µL from two different ana-lytes is generated with minimum timing between chang-ing the analyte resulting in droplet merging. Resultsof three (3) experiments where 100% is achieved isshown . . . . . . . . . . . . . . . . . . . . . . . . . . . 156Figure D.8 C1 Streptavidin Beads set is used with biotinylatedBSA, resulting in leakage through 0.7 mm orifice inEppendorf tube, resulting in irregular droplet size andoil droplets emulsification within each droplets . . . . 157xixList of Submitted Files1. Filename: ubc_2025_may_zia_abdulbasit_file01_zipZip file with all the STL files for 3D printed modules in this Dissertation2. Filename: ubc_2025_may_zia_abdulbasit_file02_mp4How to measure conduction level (CL)(Supplementary Video for Chapter 3)3. Filename: ubc_2025_may_zia_abdulbasit_file03_mp4How to calibrate ADIM System Version A(Supplementary Video for Chapter 3)4. Filename: ubc_2025_may_zia_abdulbasit_file04_mp4ADIMS_A: Droplet Generation From A Single Analyte(Supplementary Video for Chapter 3)5. Filename: ubc_2025_may_zia_abdulbasit_file05_mp4ADIMS_A: Droplet Generation From Multiple Analytes(Supplementary Video for Chapter 3)6. Filename: ubc_2025_may_zia_abdulbasit_file06_gifFlowWithinDroplet.gif file of agglomerate movements within a droplet(Supplementary for Chapter 4)7. Filename: ubc_2025_may_zia_abdulbasit_file07_mp4How to align ADIM System Version B(Supplementary Video for Chapter 5)8. Filename: ubc_2025_may_zia_abdulbasit_file08_mp4ADIMS_B: Droplet Generation From A Single Analyte(Supplementary Video for Chapter 5)9. Filename: ubc_2025_may_zia_abdulbasit_file09_mp4ADIMS_B: Droplet Generation From Multiple Analytes(Supplementary Video for Chapter 5)xxAcknowledgementsThroughout my graduate studies, there have been many people I must ex-press my gratitude to. First and foremost, I would like to extend my sincerestthanks to my supervisor Dr. Ian Grant Foulds. Without his guidance andsupport, I would not have been able to pursue this path. I would miss ouroff-topic chats, I learned so much from him.I also thank the members of my research group who have provided valuableinput throughout all stages of my work. Justin Farrell and Sean Lee arewonderful members of the group; their support was indispensable.xxiDedicationThis dissertation, a culmination of the past 6 years of hard work, sweat, andtears, is dedicated to:− my wife and daughter. This would not be possible without their sup-port and sacrifice.− parents: Ammi, Abbu, mein Doctor ban gaya!! Thank you for yourprayersxxiiChapter 1Introduction1.1 Background and MotivationIn 2018, David Castro, under the supervision of Dr. Ian Foulds, presenteda two-phase Lab-in-Tubing microfluidic system to quantify microbead-basedagglutination assays, with a minimum detection limit of 0.100 µg/mL. How-ever, the system generated the droplets manually, therefore, there was apotential to increase the throughput of the system and lower the limit ofdetection. This research builds upon this foundation and automates dropletgeneration to increase throughput. Droplet libraries of varying droplet vol-umes generated and stored for later use will increase the utility of microflu-idic agglutination detection systems. The upcoming sections provide a briefbackground of the core concepts related to this study for novice readers,while expert readers may choose to explore the background material moreselectively.The next section provides definitions for biomarkers and immunoassays. Sub-sequently, the classification of immunoassays is discussed followed by a listof different types of agglutination. The concepts of level of blank and levelof detection are presented before the discussion on clinically significant stan-dards for selected biomarkers (C-reactive protein and Myoglobin).Section 1.2 discusses dimensionless numbers in fluid mechanics and theirrole in this research. Section 1.3 discusses the mixing mechanism in mi-crofluidics with a specific focus on the movements of particulates within adroplet. Chapter 2 provides a detailed literature review of agglutinationin microfluidics organized according to detection mechanisms. The secondhalf of chapter 2 presents literature on droplet microfluidics with a focuson droplet generation before a discussion on the research gap and thesisstructure.11.1. BACKGROUND AND MOTIVATION1.1.1 Agglutination in generalBio-markers are biological molecules present in the body, which allow med-ical professionals to assess the extent of a condition based on the concentra-tion of the relevant bio-marker. Microfluidic immunodiagnostics involvesmeasuring the concentration of Biomarkers. There are hundreds of Bio-markers related to a vast number of medical conditions. For instance, cardiacbiomarkers are a measure of what is happening in cells and tissues relatedto the human heart.Cardiac Biomarkers are molecules such as C reactive Protein, Myoglobin,B-type natriuretic Peptide, D-dimer, etc. Detection of these biomarkers isan enormous field of study and involves several types of testing, systems,and mechanisms. Immunoassays are a subset of all the diagnostics tech-niques available in research and labs. Immunoassays are a large family ofquantification techniques that use the probe-target(e.g. antibody-antigen)interaction to measure the concentrations of biomarkers.Immuno assays can be classified in several ways. They can be classifiedbased on whether they are labeled or non-labeled. Further still, the type oflabel used can also be used to differentiate them. They can also be classifiedas homogeneous or heterogeneous. Moreover, immunoassays can be classifiedas either qualitative or quantitative, based on their results.Labeled immunoassays are immunoassays where an antigen or antibody ischemically linked or conjugated to a label. Labels can be enzymes, radioac-tive isotopes, fluorescent dyes, or chemiluminescent dyes. In non-labeled im-munoassays, unmodified reagents are used, which leads to a simpler systemwith lower cost. A variety of methods have been developed for detection innon-labeled, such as precipitation, immunodiffusion, immunoelectrophoresis,and agglutination.Homogeneous immunoassays are the ones in which analytes and reagentsare simply mixed. On the other hand, in the heterogeneous assays, the un-bounded analyte is washed away to ensure that the signal from the boundedanalyte is measured without background noise. The signal-to-noise ratio isgenerally higher for heterogeneous assays as compared to homogeneous as-says. However, due to the washing step, heterogeneous immunoassays aremore difficult to automate and require longer incubation times.Agglutination is defined as the formation of clumps of cells or inert particlesby specific antibodies to surface antigenic components (direct agglutination)21.1. BACKGROUND AND MOTIVATIONor to antigenic components adsorbed or chemically coupled to red cells orinert particles (passive hemagglutination and passive agglutination, respec-tively). Agglutination forms the basis of several medical tests in laboratoriesaround the world.1.1.2 Agglutination typesAgglutination tests can be divided into several categories. Classificationof immunoassays is also applied to agglutination tests i.e. a test can belabeled either homogeneous or heterogeneous. On the other hand, we candifferentiate the agglutination based on its mechanism:− Gel Card Agglutination− Paper-based Agglutination− Beads based– Magnetic Beads– Latex Beads− ELISA (enzyme-linked immunosorbent assay)− Robust Column Agglutination testMoreover, agglutination tests can be sorted according to the detection mech-anism of concentration of biomarkers e.g. nephelometry, turbidimetry, im-munochromatographic, fluorescence, etc. Agglutination tests and their clas-sification are discussed in more detail in Chapter 2.1.1.3 Detection threshold and Clinical standardsEach biomarker provides information about a different aspect of the patient’shealth. Including the details of all cardiac biomarkers will be excessive,thus clinically significant levels of only CRP and Myoglobin are included toillustrate relevant information. First, the clinically relevant levels of eachbiomarker are outlined according to the Ministry of Health BC Canada thenthe commercial kits available in the market are discussed. The level of blank(LOB) and level of detection (LOD) are common metrics used in assays tomeasure the probe-target interactions. The LOB is the highest concentrationwhich generates a signal when the target (e.g. antigen) is not present, abaseline signal. On the other hand, the LOD is the lowest concentration oftarget distinguishable from the background noise.31.1. BACKGROUND AND MOTIVATION1.1.4 Clinical significant standardsThe concentration of any biomarkers established and used by laboratoriesaround the world is clinically significant. The concentration detection achievedin research (i.e. LOD) is often much lower than the clinically significantrange1.C-Reactive Protein (CRP)C-reactive protein tests are used to identify and keep track of infections anddiseases that cause inflammation. According to Health Canada, clinicallysignificant levels are listed below [8, 9].− Clinically significant : > 10mg/L− Healthy: < 5mg/L− Acute inflammatory Event: 20mg/L < Event < 500mg/LAs one can see the ranges are wide and broad because the CRP test is usuallysemi-quantitative. A high-sensitivity CRP (hs-CRP) test exists which helpsin showing a person’s risk for heart disease and stroke. Clinically significantranges for hs-CRP, according to the Ministry of Health BC Canada are [10]:− Low risk: 1.0 mg/L− Average risk: 1.0 to 3.0 mg/L− High risk: > 3.0 mg/LAlthough the ranges of hs-CRP are narrower, the diagnosis is still semi-quantitative. One possible reason for this might be that the CRP test isusually conducted in conjunction with a variety of tests to develop an overallpicture of a patient’s cardiac health. Therefore, an immunoassay system thatcan perform tests on multiple cardiac biomarkers will be highly useful in labsaround the world.MyoglobinMyoglobin is a protein found in muscles, e.g. heart tissue, released into bloodafter damage to the muscle tissue. Damage can occur from a cardiac eventsuch as a heart attack.Clinically significant ranges for Myoglobin are [11]:1Disclaimer: the concentration levels mentioned might differ from lab to lab.41.1. BACKGROUND AND MOTIVATIONTable 1.1: Available commercial kits for C-reactive Protein, and Myoglobin.C reactive protein (CRP)Company Kit Name Detection Range Accuracy/Sensitivity CostThermoFisherInvitrogen™CRP MouseProcartaPlex™Simplex Kit40.62 - 166,400 pg/mLIntra-Assay CV: 5.2%Inter-Assay CV: 7.1%Sensitivity: 250 pg/mLCAD $554.59Sigma AldrichHCVD3MAG-67KMILLIPLEX® HumanCardiovascular DiseaseMagnetic Bead Panel 3Kit0.012-50 ng/mL Intra-Assay CV: 10%Inter-Assay CV: 20% CAD$ 1371.29bioTechne R&D Systems(LOBM1707)Human CRPMagneticPerformance Assay20.3 - 14,800 pg/mLIntra-Assay CV: 5.8%Inter-Assay CV: 9.6%Sensitivity: 1.4 pg/mLCAD $ 1091.00ThermoFisherBMS288INSTCRP HumanInstant ELISAKit78 - 5000 pg/mLIntra-Assay CV: 6.9%Inter-Assay CV: 13.1%Sensitivity: 3 pg/mLCAD$ 680Creative DiagnosticsC ReactiveProtein HumanELISA Kit(DEIA1408)0.5 - 5 ng/mLIntra-Assay CV: 5.2%Inter-Assay CV: 7.1%Sensitivity: 250 pg/mLMyoglobin (Myo)Company Kit Name Detection Range Accuracy/Sensitivity CostbioTechne R&D Systems(LXSAHM-01)Luminex HumanDiscovery Assay(Myoglobin)15.4 - 3800 pg/mLIntra-Assay CV: 5.8%Inter-Assay CV: 9.6%Sensitivity: 1.3 pg/mLCAD $ 804.00Creative Diagnostics(IHILA-NS004)InflaHiLA™Human (Myo) Kit20-3000 ng/mL Intra-Assay CV: 15%Inter-Assay CV: 10% CAD $ 1,659Sigma AldrichHCVD2MAG-67KHuman MB /MyoglobinELISA Kit0.819 - 200 ng/mLIntra-Assay CV: 10%Inter-Assay CV: 12%Sensitivity: 0.82 ng/mLCAD$1371.29ThermoFisherBMS2259MyoglobinHuman ELISAKit0.39 - 25 ng/mLIntra-Assay CV: 10%Inter-Assay CV: 12%Sensitivity: 0.08 pg/mLCAD$ 662− Healthy: 25-70 µg/L− High risk: >210 µg/L1.1.5 Commercial Kits availableTable 1.1 provides a list of commercial kits available for C-reactive proteinand Myoglobin from major medical supplies providers. Kits with latex ormagnetic beads are included in the Table along with ELISA kits. ELISAkits reduce the need for the user to immobilize beads on the walls of the wellplate as they come with 96 well plates pre-coated with beads conjugatedwith antibodies.51.2. DIMENSIONLESS NUMBERS IN FLUID MECHANICS1.2 Dimensionless numbers in fluid mechanicsDimensionless numbers play an important role in analyzing the behavior offluids. Dimensionless numbers are essential for scaling fluid dynamics anddroplet generation processes, allowing research findings to be generalizedacross different systems and conditions. Moreover, droplet formation is in-fluenced by factors such as viscous and inertial forces, often captured bydimensionless numbers which help in characterizing the behavior of fluid in-terfaces. Understanding fluid interfaces is critical to understanding dropletgeneration mechanisms. The dimensionless numbers are the ratio of forcespresent in the fluid (listed below).− Inertia Force = ρv2L2− Viscous Force = τA = µvL− Pressure Force = (∆p)A− Gravity Force (G) = mg− Surface tension (γ) = 12 FL− Compressibility (β) = − 1V ∂V∂pwhere ρ is density, v is linear velocity, L is length, µ is dynamic viscosity,p is pressure, A is cross-sectional area, m is mass, g is acceleration due togravity 9.81m/s2, γ is surface tension, F is force , Er is bulk modulusThere are numerous dimensionless numbers, however, the dimensionless num-bers relevant to this study of agglutination immunoassays are discussed inthis section.1.2.1 Reynold’s Number (Re)The Reynold’s number is the ratio of inertia forces to viscous forces. Usingthe mathematical expressions for each from above we get:Re =Inertial forcesViscous forces=ρv2LµvL=ρvLµ(1.1)The magnitude of Reynold’s number indicates which force is dominating,inertial, or viscous force. At low Reynolds numbers, viscous forces dominategiving a laminar flow (Re ≤ 2300), while at high magnitude inertial forcesdominate resulting in a turbulent flow (2300 ≤ Re) [12].61.2. DIMENSIONLESS NUMBERS IN FLUID MECHANICSDiscussion on Reynold’s numberConsidering David’s system’s parameters of flow velocity of 1 cm/s, tubing’sinternal diameter of 0.51 mm, and assuming PBS as the fluid (ρ = 1.0g/cm2and µ = 0.01g/cm.s) equation 1.1 yields a Reynolds number of 5. In theseranges the flow is laminar[12], and it results in inner vortices of latex beads(2.8µm diameter) within the plug. More on this topic in the section 1.3.1The tubing used in this study has an internal diameter of either 0.559mmfor PTFE and 0.508mm for Tygon and a linear flow velocity between 0.4−1cm/s. Assuming PBS as the fluid with the aforementioned parameters, theReynolds number is in the range of 2-5.6. The flow is still laminar.1.2.2 Capillary Number (Ca)The capillary number is the ratio of the relative effect of viscous drag forcesversus surface tension forces acting between two immiscible liquids and isgiven by equation 1.2.Ca =Viscous drag forcesCapillary forces=µvσ(1.2)When the capillary number is small the axial velocity of the plug’s exteriorsurface is small enough to be negligible, because the wetting film of continu-ous phase separating the channel wall and the plug is < 5% of channel width[13]. The thickness of this wetting film h scales according to Bretherton’slaw h ∝ RCCa2/3, where Ca is the capillary number and RC is the radius ofthe channel [14].Moreover, Kurup and Basu [15] state that when Ca << 1 and µplugµcontinuous <<1, inner circulation velocity VC of vortices scales in proportional with thevelocity of the plug VP . For our system, with a dynamic viscosity of lightmineral oil (0.0119 cP), velocity in the range of 0.4 - 1 cm/s, and surfacetension between oil and water (0.03), equation 1.2 yields average Ca of 2.74×10−7. This value leads to a wetting film of 1.4 Åaround the dispersed phase,which is approximately 1 molecule wide. Detailed calculations for capillarynumber can be found in appendix 6.3.2.Discussion on Capillary numberOur system operates with the capillary number in the range where a thinwetting film will exist to allow the assumption that the axial velocity at71.2. DIMENSIONLESS NUMBERS IN FLUID MECHANICSthe plug’s external is negligible. The capillary number and viscosities of theplug and continuous phase are in the range mentioned by Karup and Basu.Therefore, an increase in the plug’s velocity will increase the velocity ofinternal vortices, resulting in better mixing of analyte and reagent. However,the increase in the plug’s velocity will increase the capillary number. A highercapillary number leads to droplet breakage at generation and transport [16].Moreover, the fact that plugs have a higher chance of breaking at highervelocities as viscous forces increase leading to greater deformation of droplets.This will limit the maximum plug velocity this study can use and Loweringthe plug’s velocity will lower the overall throughput of the immunoassays.1.2.3 Peclet Number (Pe)The Peclet number is the ratio of advective transport over molecular diffusivetransport and can be used to determine the dominant mode of transport.Equation 1.3, where L is the characteristic length, v is the velocity and D isthe diffusion coefficient, shows how to calculate the Peclet number.Pe =Advective transportDiffusive transport=LvD(1.3)The Peclet number will express how many channel widths (multiples of w)the length of the channel must be to achieve full mixing of the two fluids. Alarge Pe number indicates an advectively dominated transport scheme anda small Pe number indicates a diffusive transport scheme.For instance, fluid containing small proteins (5 nm diameter, Dc = 40µm2/s)with channels in the order of 500 µm in width, equation 1.3 gives Pe in orderof 107 for velocity range of 0.4 − 1cm/s. This means that the advectiondominates over diffusion.Discussion on Peclet numberConsidering David’s system with a flow velocity of 1 cm/s, tubing’s internaldiameter of 0.51 mm, and a diffusion constant 0.2µm2/s (for a particle of1 µm diameter) equation 1.3 yield Pe = 2.55 × 107 [17]. A high Pecletnumber suggests that the system depends on advection as the transportmechanism. A low Peclet number means that diffusion is the sole transportmechanism which in turn means that a very long tubing is required to mixanalyte and reagents. At the Peclet number of 2.55 × 107, it would take≈ 13m tubing for the beads and analyte to mix in a single-phase flow. In81.3. MIXING IN MICROFLUIDICSdual-phase flow, David achieved agglutination of beads and analyte in 0.75m tubing. Therefore, our system will be using a dual-phase Lab-in-tubingwhich depends on circulation, inertial effects, and sedimentation to incubatethe assays.1.3 Mixing in microfluidicsIn microfluidics, the mixing of two or more reagents and analytes is an im-portant function. The number of phases in the immunoassays has a hugeimpact on the mechanism of mixing.Single-phase flow has two inherent problems, especially for agglutination thatis the Taylor dispersion and analyte’s contact with the channel. Firstly, Tay-lor dispersion is the result of the parabolic laminar flow profile, which leadsto particles having different incubation times depending on their location.Secondly, the analyte is always in contact with the channel walls leading toparticulates getting deposited on the wall and contaminating the consecutiveassays.Two-phase flow is where two immiscible liquids flow together, one as a con-tinuous phase, and the second as a dispersed phase. As discussed in section1.2.2 Capillary Number (Ca), this leads to a thin film of continuous phaseforming outside the dispersed phase, isolating it from the walls of the chan-nel. Each droplet or plug becomes a separate reactor, hence solving bothissues of single-phase flow immunoassays. Therefore, this study will focuson two-phase immunoassays, and all the techniques for the mixing of mi-crofluidics mentioned in the upcoming sections will concern a two-phase flow(droplet microfluidics).In two-phase microfluidics the mixing of reagent and analyte occurs at twolevels, firstly within a plug/droplet and secondly by combining two droplets.Internal mixing in a plug is discussed in detail, while the mechanisms ofcombining two droplets are out of the scope of this study.1.3.1 Within a dropletThere is no Taylor dispersion instead there are circulating vortices withinitself as shown in Fig. 1.1. As discussed in section 1.2.2 Capillary Number(Ca), a low capillary number determines the thin film’s thickness surroundingthe plug, which leads to negligible axial velocity. Velocity differences atthe exterior and interior results in the vortices. Inner circulation enhances91.3. MIXING IN MICROFLUIDICSFigure 1.1: Plug flow for liquid-liquid. a)Schematic of internal vortices de-veloped due to flow b) Analytical model [15] ©Kurup, G. K. (2012) usedwith Permissionthe mixing of analyte and reagent and incubation in immunoassays. If theplug contains particulates or beads, then their behavior can range from fullcirculation within the plug to aggregation at the back cap of the plug.The flow pattern of the beads in the plug depends on the size of the beadsand the plug’s velocity. Both the circulation effect and the aggregation effectcan be related to the shield parameter θ, which is a dimensionless numberrelating the ratio of the drag force on the particle to the particle’s weight,Fig. 1.2. Equation 1.4 shows the two forms of the shield’s parameter usefulto this discussion2.θ =Particle DragParticle Weight=τa∆ρg= 9µPa2∆ρgVP (1.4)When θ is less than θM (movement threshold) particles remain in a sedimentbed at the back cap of the plug and there is no circulation. When θ issimilar to θM , the particles circulate within a defined zone which begins atthe back cap of the plug. When θ >> θM , particles circulate full length ofthe plug [15].LcRp=5√2Vp24Vs=5√248θ (1.5)The next important parameter is Lc defined as the circulation length and2where τ is shear stress on the particle by the liquid, µP is plug viscosity, ∆ρ is thedensity difference between the plug and the continuous phase, VP is plug’s velocity and gis the gravitational constant101.3. MIXING IN MICROFLUIDICSFigure 1.2: Circulation patterns of beads in the plug depending on shieldparameter, modified from [15] ©Kurup, G. K. (2012) used with Permissionis given by equation 1.5, where Rc is the radius of the tubing (plug’s ra-dius). Table 1.2 gives the results of shield number and circulation length fordifferent beads at different flow rates and in different tubing. Detailed cal-culations regarding the shield parameter and circulation length are providedin appendix 6.3.2Table 1.2: Shields Parameter and Circulation Zone for M270 and C1/T1Beads at Different Flow Rates and Tubing TypesBead Type Flow Rate PTFE Tygon PTFE TygonBead Type µL/min θ θ Lc (mm) Lc (mm)M270 60 3125 3785 129 142M270 90 4690 5678 193 212M270 120 6254 7571 258 283C1 60 13840 16762 571 627C1 90 20768 25144 857 941C1 120 27693 33527 1142 1255111.4. DISSERTATION STRUCTURE AND ORGANIZATIONConcluding Remarks− Table 1.2 gives the results for the circulation zone Lc for different typesof beads in each PTFE and Tygon.− The length of 1 µL droplet is ≈ 4mm in either PTFE or Tygon tubing,which is several magnitudes smaller than the circulation zone for M270,C1, or T1 beads. This means the beads should circulate the full lengthof the droplet.1.4 Dissertation Structure and OrganizationThis Dissertation has six chapters and four appendices, as shown in Fig. 1.3.After the Introduction, the second chapter is the literature review which in-cludes the research gap and objectives. Appendix A contains detailed calcu-lations regarding dimensionless numbers and plug sedimentation which arediscussed in Chapter 1. Chapter 2 provides a literature review on aggluti-nation in microfluidics and droplets on demand, leading to the research gapand objectives of this study. Then the next three chapters correspond tothree manuscripts accepted or submitted in academic journals.− Chapter 3 outlines the calibration and setup of the first ADIM system(Version A) developed in this research.– Appendix B contains the supplementary documents that accom-pany the journal article presented in Chapter 3.− Chapter 4 describes an application of the ADIM system (Version A).ADIM system is used to generate trains of droplets with varying con-centrations of Streptavidin latex beads and biotinylated BSA (bBSA).Image analysis is used to quantify the level of agglutination.– Appendix C contains the supplementary documents that accom-pany the journal article presented in Chapter 4.− Chapter 5 outlines the calibration and setup of the second ADIMsystem (Version B) developed in this research improving upon the firstversion.– Appendix D contains the supplementary documents that accom-pany the journal article presented in Chapter 3.The last chapter is the conclusion, which summarizes the findings of thisdissertation and outlines future research directions.121.4. DISSERTATION STRUCTURE AND ORGANIZATIONFigure 1.3: Flow diagram of the dissertation structureAdditional paragraphs and sections have been incorporated into Chapters3 to 5 and the Appendices to address questions raised during the defenseand to implement the recommended corrections for this dissertation; theseadditions have been highlighted for clarity.13Chapter 2Literature ReviewThe gold standard test for any biomarker is performed by collecting venousblood or urine and using an enzyme-linked immunosorbent assay (ELISA)or an immunoassay applied to ELISA, providing accurate information aboutthe biomarker level. There are several kinds of ELISA, i.e. direct, indirect,sandwiched, competitive, and blocking. There are 6 general steps to a directELISA assay, shown in Fig. 2.1: 1) Coating the analyte 2) washing ex-cess analyte 3) blocking steps 4) washing excess blocking agent 5) detectionantibody 6) washing excess detection antibody 7) reaction, and 6) analysisand calculation. In the first step, the analyte is coated on the walls of theFigure 2.1: Steps for direct ELISA. Created with BioRender.com142.1. AGGLUTINATION IN DUAL-PHASE MICROFLUIDICSwell and then washed. Well plates precoated with analyte are commerciallyavailable for purchase, (ELISA kits in Table 1.1). If the detection antibodyis added directly, it can bind to the plate as well as the analyte leading toa high background signal and low sensitivity. Therefore, a blocking agent isapplied to the plate next, Bovine Serum Albumin (BSA) is a popular proteinblocker. Excess blocking agent is washed away. The next step is adding thedetection antibody which binds to the immobilized analyte. Excess anti-body is washed away in the subsequent step. The last step before analysis isthe incubation which allows the color reaction to occur. For analysis, platereaders are used to read the ELISA plate using a spectrophotometer.The ELISA is a complicated procedure requiring several steps and is labor-intensive. Therefore, alternative methods are investigated, such as microflu-idic chips which can run multiple processes on a chip requiring a smallamount of analyte and is not labor intensive.2.1 Agglutination in Dual-Phase MicrofluidicsAgglutination in dual two-phase microfluidic systems is an emerging area ofresearch with limited literature available [18]. While microfluidic approachesto emulsification and two-phase flows are well- documented the specific ap-plication of agglutination in this context remains relatively unexplored.Potential advantages of utilizing two-phase microfluidics for agglutinationassays include high-throughput capabilities and precise quantification [18].This method significantly reduces reagent consumption and speeds up ag-glutination reaction times. Furthermore, microfluidic systems can over-come limitations such as mass transport issues commonly encountered inbatch two-phase biocatalysis [19]. David Castro implemented a streptavidin-biotinylated BSA model assay in a dual-phase Lab-in-Tubing configuration.Optical image analysis and nephelometry are compared as quantificationmethods for agglutination. A minimum detection limit of 50 ng/mL usingoptical image analysis for the model assay. The system, shown in Fig. 2.2,consists of a mineral oil reservoir connected to a 2 meter silastic tubing thatpasses under a camera and the nephelometry apparatus before connectingto a syringe pump. The assays are pipetted into the oil reservoir at a rateof 360 assays/h. The throughput of this system is the limiting factor thatneeds to be improved. Specific findings of the research are included in thediscussion on image analysis and nephelometry in later sections.There is limited research work on agglutination in dual-phase microfluidics.152.1. AGGLUTINATION IN DUAL-PHASE MICROFLUIDICSFigure 2.2: Schematic of the System for a continuous high-throughput mi-crofluidic system: open oil reservoir with silicone tube (2m length) to syringepump with both detection methods: Nephelometry (laser and photodetec-tor) and Image processing (CC BY-NC. D. Castro (2018))In addition to David’s work, the other two prominent works are by Kline andTeste. Kline uses a plug-based (droplet) microfluidic platform to conductmultiple agglutination reactions in parallel without cross-contamination, us-ing only microliter volumes of blood [20]. They can successfully differentiateABO and D (Rh) blood types and resolve subtypes, such as A1 and A2, usinga lectin-based dilution assay. Additionally, it distinguishes weakly aggluti-nating rare subtypes by analyzing agglutination kinetics under controlledshear conditions. Fig. 2.3 shows how an array of droplets are exposed toRBC solution to perform ABO and D typing. It also shows agglutinated andnon-agglutinated droplets. The droplet microfluidics offers faster assay timesand reduced sample consumption, making it highly suitable for point-of-careapplications, especially in blood banks and clinical settings.Teste et. al. developed an automated platform, shown in Fig. 2.4, toperform immuno-agglutination assays using magnetic beads (MBs) withindroplets. The assay leverages droplet microfluidics and magnetic tweezers toenhance collision rates and agglutination kinetics by magnetically confiningMBs inside droplets, leading to faster assay times. After magnetic confine-162.1. AGGLUTINATION IN DUAL-PHASE MICROFLUIDICSFigure 2.3: a) A Droplet train of analytes (Anti-body array) used in ABOand D blood typing b) merging of plugs in the antibody array with a so-lution of RBCs c) bright-field images showing different agglutination states[20]©T.R. Kline (2008) used with Permission.Figure 2.4: Droplets were generated in Teflon tubing from a microtiter plate.Magnetic tweezers used to induce confinement by magnetic fields to enhanceaggregate formation. [21] ©B. Teste (2013) used with Permission172.2. AGGLUTINATION BIOSENSORSment, the beads are redispersed by internal recirculation flows, allowing foroptical detection based on the extent of aggregation. The platform demon-strated a high throughput of 300 assays per hour with a detection limit of 100pM for a model assay of biotinylated phosphatase alkaline. It significantlyreduces reagent consumption, making it suitable for cost-effective immuno-diagnostics, while offering sensitivities suitable for many medical applications[21]. Teste used the same system for chromatin immunoprecipitation assay[22], demonstrating the versatility of the platform.Droplet microfluidics provides precise control over fluid volumes and enablesrapid detection of agglutination by monitoring the interaction between tar-get and probe within droplets. Due to the lack of literature on the subject,the literature about agglutination biosensors is discussed first, leading thediscussion to agglutination in microfluidics. Then droplet-on-demand gener-ation is explored leading to the research gap and objectives.2.2 Agglutination BiosensorsA biosensor is a device consisting of a receptor, a transducer, and a bio-logical compound. It detects the signal generated as a result of biologicalinteraction (e.g. probe-target). Biosensors provide a user-friendly detectionmethodology with low volumes of reagents and can be used in point-of-caredevices. There are biosensors in literature detecting probe-target interactionthat do not include agglutination. A few of them are explored next.In research, there are two main detection mechanisms for detecting biomark-ers, electrical and optical. Electrical methods include the use of Field EffectTransistors[23, 24], Piezoelectric, electrochemical [25, 26], Capacitance[27],Voltammetry[28, 29], or impedimetric measurements [29]. Optical methodsinclude spectrometry[30], turbidimetry[31, 32], nephelometry[17, 33], fluores-cent measurement[34], and image analysis [35–40]. The difference betweenturbidimetry and nephelometry is that in turbidimetry the light transmittedthrough the sample is measured, while in nephelometry the light scattered bythe sample is measured. There are reactions that detect the probe-target re-action based on the change of color, i.e. colorimetric measurement. However,the color change is detected using other optical detection methods.2.2.1 Electrical Agglutination Biosensors− FET: Biosensors using FETs are currently under research due to theiradvantages of miniaturization, mass production, and inexpensive pro-182.2. AGGLUTINATION BIOSENSORSFigure 2.5: left: AlGaN/GaN high electron mobility (differential) transis-tor (HEMT), right: drain current vs gate voltage for different biologicalmolecules adsorped[23] ©H. H. Lee (2016) used with Permissionduction processes. The FET biosensor is a method that measures thecurrent change of the transistor through the reaction between the sur-face of the thin film transistor and the target; the current change issensed through the change in the threshold voltage and conductivityof the gate voltage. Lee bae et al. conducted a study [23], which pro-posed a sensor system composed of heterogeneous AlGaN/GaN layers.Ni/Au (20/50 nm) was deposited as a gate metal, and a CRP antibodywas immobilized thereon. As shown in Fig. 2.5, one transistor struc-ture was used to detect CRP, and the other was used as a reference toeliminate the noise factor. With drain-source current (IDS = 2.5mA),drain-source voltage (VDS = 1.5V ). When specific binding betweenCRP and CRP antibody occurs, Vthreshold shifted (from -2.25 V to-1.64 V), due to the negative charge of CRP. The level of detectionachieved in this study was 0.01 ng/mL.− Capacitance: Christos Sapsanis et al. showed the capacitance ofParylene C coated Au interdigitated electrodes changes when the anti-CRP antibodies interact with CRP protein (antigen). An LCR meteris used to measure the capacitance change, with the reported LOD of0.5 mg/mL [27].− Electrochemical: The electrochemical sensor detects an antigen us-ing current or electron transfer resistance generated by redox species.Nanomaterials are used in such sensors as a sensing material to en-hance the reaction, shown more clearly in Fig. 2.6 . The Tang group192.2. AGGLUTINATION BIOSENSORSFigure 2.6: Illustration the synthesis procedure of BSA−Ab2−Cu3(PO4)2hybrid nanoflowers as signal labels (A) and the fabrication process of theelectrochemical immunosensor (B) [25]published a study on improving the signal strength of electrochem-ical reactions using organic-inorganic hybrid nanoflowers [25]. Thenanoflowers used in the Tang group are BSA-antibody-copper phos-phoric acid hybrid nanoflowers (BSA-Ab2-Cu3(PO4)2), which act assignal enhancers for enzyme-free electrochemical immunoassay devices.The three-dimensional porous nanoflower form, with its high specificsurface area, greatly increases the sensitivity of the manufactured sen-sor. Using the square wave voltammetry (SWV) 3 the level of detectionof 1.2 pg/mL for CRP was reported.2.2.2 Optical Agglutination Biosensor− Colorimetric In a study by António, shown in Fig. 2.7, CRP was de-tected quickly using an AuNP-aptasensor, where AuNP is gold nanopar-ticles. An aptamer solution adds the AuNPs colloid, and then CRPsolutions are added and incubated in a shaker. UV-vis spectropho-tometry analysis is used to measure the absorbance in the spectra of3Voltammetry is the application of a specific voltage profile to electrodes and measuringthe corresponding current profile. Voltage profile could be cyclic, differential, or squarewave pulses202.2. AGGLUTINATION BIOSENSORS400 nm to 800 nm wavelength. Level of detection achieved was 100ng/mL [30].− Turbidimetry: Jeppe Fock et al. used magnetic particles to presenta comparison of the optomagnetic and AC susceptibility readouts fordetection of CRP concentration [31]. Optomagnetic measures the mod-ulation of the intensity of the transmitted light using a photodetector.AC susceptibility measures the voltage in a coil induced by the time-dependent magnetization variation of the sample. Both modes of de-tection are shown in Fig. 2.8. Discrete levels of CRP concentrationswere tested and the lowest concentration tested was 1mg/L.− Image Analysis: Mengxing Ouyang and Dino Di Carlo [35] used aCMOS color camera to image the hue change of individual gold nano-particles(Au-NP) by extracting the RGB information for each Au-NPfrom dark field images as shown in Fig. 2.9. Au-NPs were used toenhance the color shift, which helped in lowering the detection level. AUV–Vis spectrophotometer (GENESYS™ 10S, Thermo Fisher) is usedFigure 2.7: UV-visible absorption spectra and the color change of AuNPprobes in the presence of different concentrations of CRP [30] ©J. Y. Byun(2013) used with Permission212.2. AGGLUTINATION BIOSENSORSFigure 2.8: a-c): Schematics of assay and readout principles with magneticnanoparticles, laser, photodiode, and coil d-e): graphs the real and imagi-nary components V2’ and V2" of the optomagnetic signal normalized withthe total light intensity V0 [31] ©J. Fock (2021) used with Permissionalongside the image processing to verify the concentration of CRP. Thelevel of detection (LOD) achieved was 10 pM (1.2 µg/mL)4 with thedetection range of 10 pM to 10 nM. The system required 10-15 minutesof incubation and a detection time of 30 minutes.Discussion of Agglutination Detection in BiosensorsThe works discussed in this section, summarized in Table 2.1, focus on ag-glutination detection but do not involve the use of microfluidic systems.Microfluidic systems specifically designed for agglutination detection are cov-ered in the next section.410 pM is 10× 10−12 Molarity and CRP molecular weight is 120kDa.Molarity (M) × Molecular mass(Da) = Concentration (g/mL)222.3. AGGLUTINATION IN MICROFLUIDICSFigure 2.9: Schematic of the nanoplasmonic sensing platform using a swarmof single nanoparticle colorimetric sensors [35] ©M. Ouyang (2019) usedwith PermissionTable 2.1: Summary of Dectection of Agglutination in BiosensorsProbe Detection Method LOD RefElectricalNi/Au Nanoparticle FET 0.01 ng/mL [23]BSA-antibody-copperphosphoric acidhybrid nanoflowersElectrochemical 1.2 pg/mL [25]Latex beads withAnti-CRP antibodies Interdigited Capacitor 0.5 mg/mL [27]OpticalAuNP-aptasensor UV-vis spectrophotometry 100 ng/mL [30]Magnetic Nanoparticleswith CRP antibodiesOptomagnetic andAC susceptibility 1 mg/mL [31]Au-NP UV–Vis spectrophotometer 1.2 ug/mL [35]2.3 Agglutination in MicrofluidicsThis study aims to detect the agglutination reaction for biomarkers in mi-crofluidics because Degré establishes that microfluidic chips improve the de-tection of latex agglutination [41]. The detection mechanisms remain thesame for detecting agglutination in microfluidics (i.e. Electrical and Opti-232.3. AGGLUTINATION IN MICROFLUIDICScal). Microfluidic technologies can integrate sample preparation, reaction,and detection steps into a miniaturized chip. Microfluidic systems take ad-vantage of multiple processes on a single platform, small reaction volume,and short diffusion lengths, thus reducing sample consumption and the as-say time, and potentially improving the sensitivity [42, 43]. A disclaimerhas to be added here, a large portion of the literature about agglutinationin microfluidics is related to haemagglutination (blood agglutination).2.3.1 Electrical detection methods for agglutination inMicrofluidicsMicrofluidic chips using electrical methods for biomarker detection are fab-ricated with functionalized electrodes that result in either a change in ca-pacitance, current, or voltage due to a chemical reaction.− Electrochemical: Horn-Jiunn Sheen et. al. fabricated an experi-mental platform comprising an electronic chip (circular array of Auelectrodes), a PDMS microfluidic test chamber, an imaging platform,and a power unit, shown in Fig. 2.10 [26]. It can detect CRP even atlow concentrations of 30 and 100 pg/ml, while the concentration rangeof 100 − 107 pg/mL showed a linear range of results. A sample-to-answer time of 90 seconds was achieved. Electrochemical characteri-zation of the developed microfluidic biosensor is done by plotting theimpedance after different surface modifications on Nyquist plots. Toverify the surface modifications, a hybrid-frequency AC signal was ap-plied to the electrode array and time-sequenced aggregation of avidinon the surface was recorded. The average fluorescence intensity fromopposing electrodes is compared using ImageJ to quantify aggregatesat electrodes. Faradic impedance spectra before and after incubationwith varying CRP concentrations were quantified on the selected elec-trode (ϕ = 90) with and without providing a driving signal. However,the electrodes on the glass substrate and the PDMS microfluidic chiphad to be microfabricated separately. Moreover, for biotin/ antiCRPadsorption onto the electrodes needed 40 minutes.− Acoustics: Ferreira et. al. utilizes ultrasonic forces within microflu-idic systems to enhance the agglutination of CRP and streptolysin Otoxin (ASO)[44]. A piezoelectric ceramic (PZT-4, Eleceram, China)affixed to a glass slide generates a standing acoustic wave when a sinu-soidal signal of 3 MHz is applied. The progression of agglutination isobserved in the microactivity chamber of the microfluidic chip above242.3. AGGLUTINATION IN MICROFLUIDICSFigure 2.10: Schematic of the experimental setup. a) The proposed biosen-sor’s setup with circularly arranged electrodes, a microchannel, function gen-erator, impedance analyzer, and an imaging platform b) arrangement of thecircular array of electrodes. c) A hybrid frequency driving signal is providedto electrodes. [26] ©H. J. Sheen (2022) used with Permissionthe PZT actuator by a camera. Images are manually evaluated. Itshows 8 times better results compared to the PCR test. Arlene Doraiand the team also showed an improvement in CRP agglutination dueto acoustics [45].− FET/capacitance: Wei-Chieh Kao et. al. developed an integratedmicrofluidic system with an aptamer-FET capable of multi-step im-munoassay to detect CRP with the linear range of concentrations 12.5µg/L to 10 mg/L [24]. The level of detection achieved in this study is0.34 mg/L. Fig. 2.11 shows the device. Whereas FET-based biosensorsare not reusable, this chip can wash and re-immobilize the aptamer tothe surface of the Au FET gate. However, the incubation time requiredis 20 hours and the signal (total charge) detection takes 30 minutes.− Voltammetry: Yaw-Jen Chang and the team [28] applied a linearsweep voltage (-0.5 to +0.5 V) to interdigitated electrodes to detect252.3. AGGLUTINATION IN MICROFLUIDICSFigure 2.11: FET-based microfluidic chip (a) schematic diagram of the mi-crofluidic chip with multiple chambers, valves, and a micromixer/pump. (b)Exploded view of the designed chip (c) Image of the fabricated microfluidicchip (d) the sensing region of the FET [24] ©W. C. Kao (2017) used withPermissionthe degree of red blood cell agglutination in a rotating microfluidicdisc, shown in Fig. 2.12. The device places three chips, each to detectanti-A, anti-B, and anti-D antibodies. Five degrees of agglutination arequantified based on the drain current (semi-quantification). Similarly,Mahdee Samae et. al. used impedance measurement in a hybrid chip(paper-plastic) with a pair of coplanar graphite electrodes to detectthe hemagglutination [29]. Impedance changes from 14-18 kΩ to 30-38kΩ for non-agglutinated and agglutinated samples respectively.Discussion on Electrical biosensorsTable 2.2 summarizes the works discussed in this section. FET-based biosen-sors are either for one-time use or require steps to wash and re-immobilizeantibodies/antigens. If the device is for one-time use, it should be cheapand easy to fabricate, which is not the case with FET-based biosensors. Forthe FET biosensor to have the ability to reuse, multiple steps have to becarried out, e.g. washing, similar to the steps in ELISA. Anirban Sinhaaet al. designed a chip that was capable of such steps, which resulted inthe 0.14 mg/L level of detection for CRP [46]. As compared with other262.3. AGGLUTINATION IN MICROFLUIDICSFigure 2.12: Microfluidic chip for blood typing a) Composition of the chipb) schematic of the chip with chambers, valves and reservoirs c check valveoperation and fabricated valve d) Final Fabricated chip [28] ©Y. J. Cheng(2023) used with Permissionbiosensors, the level of detection is rather high though still in the diagnosticrange. Although impedance spectrometry gives the lowest LOD, 30 pg/mL,the electrochemical method has the limitation that the redox potential ofthe target substance must be sufficiently distinct from that of interferingsubstances.Electrical microfluidic biosensors give a quick sample-to-answer time andTable 2.2: Summary of Electrical Detection of Agglutination in MicrofluidicsElectricalProbe Detection Method LOD RefCRP Antibodies Impedancespectroscopy 30 pg/mL [26]Latex withCRP antibody Acoustic – [44]CRP aptamer FET 0.34 mg/mL [24]Blood Typing Voltammetry Positive/Negative [28]Blood Typing Impedance Positive/Negative [29]272.3. AGGLUTINATION IN MICROFLUIDICSare easy to control. However, the fabrication process is complex and willlimit production if the device is taken beyond proof of concept. Keepingeverything discussed above in mind, it is necessary to look into other methodsof analysis to overcome all the limitations aforementioned.2.3.2 Optical detection methods for agglutination inMicrofluidicsIn a microfluidic system, optical detection mechanisms for agglutination typ-ically involve using light to monitor changes in the physical or chemical prop-erties of the sample, such as changes in color(colorimetric), scatter (neph-elometry), absorption (turbidimetry), or emission of light(fluorescence). Theinteraction between the light and the agglutinated particles allows for sensi-tive and precise measurements.− Fluorescence: Franziska Dinter et al. achieved a level of detection of0.2 ng/µL for CRP using PMMA beads coded with two fluorescencedyes (Rhodamin 6G and Coumarin 334) and a commercial microfluidicchip [34]. C-reactive protein (CRP), brain natriuretic peptide (BNP),and low-density lipoprotein (LDL) were tested in a single chip withmeasurement taken every 3 minutes for an hour, as shown in Fig.2.13.− nephelometry: A study conducted by Jianxi Qu, Fig. 2.14 usingmagnetic particles, the average transit time was employed to estimatethe extent of the agglutination reaction and improve the detection ac-curacy [33]. The detection limit of CRP in this study was 0.12 µg/mLCRP with a detection range of 0 to 1 µ g/mL.David Castro also usedNephelometry (light scattering) as a detection method and achieved thedetection limit of 0.5 mg/L. The system is shown in Fig. 2.2, it consistsof an open oil reservoir (4mL), which has a silastic tube (2 meters) con-necting it to a syringe pump. A pipette was used to insert the assaysinto the tube, which then flowed the entire length and was discardedonce they reached the syringe pump[17]. The detection mechanismused a blue LED laser diode (1.6 W, 450 nm), and DET100A/M pho-todetectors (Thorlabs). The laser w pointed at a fixed position of themicrochannel, from two angles (45 degrees and 90 degrees), and as theplug containing the assay flows past the measurement point reading isrecorded. A wide range of concentrations (0.5-100 mg/L) of CRP weretested. Light scattering has lower variability between repetitions andcan distinguish concentrations as low as 0.5 mg/L282.3. AGGLUTINATION IN MICROFLUIDICSFigure 2.13: a) commercial microfluidic chip (flex-flow, BiFlow SystemsGmbH) b) image taken during the measurement showing the flow cell of thechip c) Time-dependent detection of the biomarkers anti-CRP [34]©(2019)F. Dinter used with permissionFigure 2.14: a) A schematic diagram of the microflow cytometry-based ag-glutination immunoassay (MCIA) setup, b) One second of data recorded bythe LabView program,c) A detailed view of the pulse produced when parti-cles passed through the interrogation region.[33] ©J. Qu (2021) used withpermission292.3. AGGLUTINATION IN MICROFLUIDICS− Optical density: Sung-Wook Nam et al. used the Lambert-Beer law(log(I0/I)) to detect the level of Hemagglutination in 3D microwellchips using optical density [32]. A 3D printed mold was used to fabri-cate trapezoid microwells along a serpentine channel, as shown in Fig.2.15. The chip is designed for a benchtop microplate reader to readeach microwell just like a commercial wellplate. The blood sample ismanually pipetted into the microfluidic chip, followed by either anti-Aor anti-B. A customized iMark microplate reader (Bio-Rad) is used tocharacterize absorbance. The results are qualitative, whether there isagglutination or not.Image processingImage analysis is a critical tool for quantifying agglutination processes invarious fields such as virology, blood typing, and microbiology. A dominantpart of the literature focuses on hemagglutination or fluorescently labeledbiomarkers. A few of the prominent research studies are mentioned below.Shaohua Ding et al. employed a machine learning approach based on His-togram of Oriented Gradients (HOG) and Support Vector Machines (SVM)to process the captured images and accurately determine blood types [36].HOG is a feature descriptor used in image processing and computer visionto detect objects. Ana Ferraz et al. underscore the application of image pro-cessing techniques for detecting agglutination reactions in pre-transfusiontests [47]. Habets et al. discuss the limitations of traditional methods likelight microscopy for bacterial agglutination quantification, emphasizing thenecessity for innovative approaches [48].Huet et al. have devised an image processing algorithm to automaticallyFigure 2.15: a-d) Schematic of RBC and antibody loading procedures e)Schematic of hemagglutination assay in 3D microwell chips [32]©S.W. Nam(2023) used with permission302.3. AGGLUTINATION IN MICROFLUIDICSFigure 2.16: a) Microfluidic chip fabricated with embedded reagents b) 3DSchematics of the fabricated chip c) image of blood sample and the ROI indi-cated where the variance-based and correlation-based agglutination (VBA)indicator is calculated d)Results of agglutinated and non-agglutinated bloodsamples with the VBA indicator [37]©M. Huet (2018) used with permissionmeasure red blood cell agglutination for blood typing within passive microflu-idic biochips (shown in Fig. 2.16) [37, 38]. For this study, variance-basedand correlation-based algorithms were developed to quantify agglutinationin images. Using both indicators agglutination was observed in real-timeand ABO blood typing was carried out as proof of concept. Fig. 2.16shows the microfluidic chip designed for this study and the result of thecorrelation-based indicator. Sklavounos et. al. used a similar approach todevelop a hemagglutination indicator where the grayscale intensities withinthe droplets were flattened into a one-dimensional array and normalized tocover the full 8-bit range [0-255] and then sorted the pixel values in ascendingorder [39].David Castro: used a commercial camera (5D Mark II) to capture videos312.3. AGGLUTINATION IN MICROFLUIDICSof agglutination assays with latex beads [40]. A silastic tube is cast inPDMS to form a viewing window at regular intervals (30cm) for the camerato capture the agglutination in assays. Video of the plugs passing throughthe tube is captured with 1920 x 1080 resolution at 30 frames/second rate,Fig. 2.17. MATLAB (MathWorks Inc., Natick, MA, USA) was used toextract frames from the video and the extent of agglutination was quantifiedusing the standard deviation of the grayscale. The lowest level of detectionof CRP was 50 ng/mL at 175 µL/min flow rate and achieved a sample-to-answer time of 2.5 minutes. Mahdee Samae and the team also used grayscalemean intensity to detect hemagglutination in a paper plastic hybrid device(PPHD) [29].2.3.3 Discussion on Optical detection of agglutinationmicrofluidicsTable 2.3 summarizes the works discussed in this section. The optical de-tection methods are attractive as they can help quantify non-labeled agglu-tination in two-phase microfluidics. Labels (latex, magnetic, and fluorescenttags) enhance the signals for detection. The setup for agglutination couldFigure 2.17: a) Schematic of the continuous high-throughput microfluidicsystem: open oil reservoir with silicone tube (2m length) to the syringepump b) Top view of the PDMS block, with plug traveling through theviewing window c) Side view diagram and an image of an assay, indicatingrecirculation patterns and microbead aggregate behavior[40] CC BY-NC D.Castro (2018)322.4. DROPLET MICROFLUIDICSTable 2.3: Summary of Optical Detection of Agglutination in MicrofluidicsOpticalProbe Detection Method LOD RefPMMA Beadswith CRP antibody Fluorescence 0.2 ng/mL [34]Magnetic Beadswith CRP antibody Nephelometry 0.2 µg/mL [33]Latex Beadswith CRP antibody Nephelometry 0.5 mg/mL [17]Hemagglutination Optical Density Positive/Negative [32]Blood Typing Image Analysis Positive/Negative [36]Hemagglutination Image Analysis Positive/Negative [37], [38]Latex Beadswith CRP antibody Image Analysis 50 ng/mL [40]be as simple as a light source and a photodiode or as complex as coils(togenerate magnetic fields) [49], laser through optical fiber and the hardwareto focus the laser. Image processing can capture labeled (e.g. fluorescent)or non-labeled (e.g. latex) agglutination immunoassays as in the study byMengxing Ouyang (Fig. 2.9). Moreover, automating the concentration eval-uation using image processing is also relatively straightforward.For this study, non-labeled agglutination immunoassays will be used andimage processing (using a commercial camera Canon 5D) will be the mostsuitable choice for quantifying the concentration of a biomarker.2.4 Droplet MicrofluidicsDroplet microfluidics is a branch of microfluidics where one fluid is suspendedwithin another immiscible fluid in microchannels. Droplet microfluidicsleverages the unique capabilities of droplets in dual-phase systems as micro-reactors for a variety of applications, from chemical synthesis to biochemicalassays[15, 50–54]. Droplets (dispersed phase) in dual-phase microfluidicsare isolated from each other and the tubing walls because of the immisci-bility and encapsulation of the droplet with a thin film of the continuousphase[15]. The generation of a droplet of an analyte with the required com-position and volume is the first step in droplet microfluidics[55], followed bydroplet manipulation and then quantification. Generally, the droplet gener-332.4. DROPLET MICROFLUIDICSation is designed for continuous operation yielding high throughput achievedthrough structures like a T junction[56–58], co-flow[59–61], or flow focusing[62–64] channels operating in squeezing or dripping in configurations.Compared to a continuous operation Droplet-on-Demand (DoD) systems uti-lize external forces or mechanisms to offer precise control over the number,size, and timing of droplet generation. In DoD systems, volumes of two con-secutive droplets can be varied with a rest period in between; both are notpossible in continuous systems. The DoD systems minimize reagent wasteas they produce a droplet only when needed. The DoD systems can be fab-ricated using lithography (micro-fabrication), 3D-printed, or macrosystem.Traditional DoD systems in microfluidics often involve micro-fabricated de-vices and techniques like lithography to create SU-8 molds to create PDMSchips[65, 66], offering tight control over droplet size and easy integrationof the control mechanism. However, a cleanroom is required for photo-lithography, which significantly raises expenses, making it cost-prohibitivefor the researchers lacking cleanroom facilities.The advent of 3D printing technology has allowed an alternative method tofabricate droplet generation DoD systems. 3D printing is predominantly usedfor crafting PDMS molds [67] or for direct fabrication of microfluidic devices[68]. Projection micro stereo-lithography 3D printing technology allows afeature size of a few microns, e.g. Junyi Chen et al.[69] who developed a3D printed modular microfluidic chips with a feature size of 10 µm. Theresulting microfluidic system, with droplet generation, is versatile but theequipment required incurs a high cost (approx.$180,000 USD).2.4.1 Droplet on demand in Microfluidic ChipsThe prevalent method for generating droplets in Droplet on Demand (DoD)systems typically involves using a valve to control the disperse phase, con-trolled pneumatically[70] or electrically. A valve usually consists of a controllayer, a fluid layer, and a thin membrane between them. These systems aredesigned to create droplets with nanoliter or picoliter volume precisely[71],yet they depend on complex and costly setups, such as specialized flowcontrollers[72]. When a valve is used with a T junction, it can act as adroplet generator on demand [73]. Fig. 2.18, shows a valve-based dropleton-demand microfluidic chip developed by Feng Guo et al. at Wuhan Uni-versity.Other droplet-on-demand designs in microfluidics involve active actuation to342.4. DROPLET MICROFLUIDICSFigure 2.18: a) Schematic diagram of the droplet on demand b) fabricateddevice c) fusion channel (scale bar 400 µm)[73] ©F. Guo (2010) used withpermissioncontrol the release of the dispersed phase into the continuous stream. Actu-ation can be a pressure pulse [74], an AC pulse [75] or microwave signal [76].There’s less emphasis on generating droplets through electrical actuation inliterature[77], e.g. using alternating current (AC) electrical field[78, 79] ormicrowave-induced heating or AC fields[80].Several companies offer microfluidic kits for easy setup of droplet generationsystems, such as the Elveflow© starter pack[81]. Designed for straightfor-ward use, these kits include essential components like pressure controllersbut require users to familiarize themselves with the accompanying customsoftware.Discussion on a droplet on demand in Microfluidic chipsActive actuation or valve-based designs add complexity to the fabrication ofthe microfluidic device. Complex fabrication increases chip-to-chip variations[82], which in turn will increase intra-assays coefficients of variability. It alsohinders mass production. Microfabricated chips’ production requires highlytrained individuals and their use requires a technically trained individual.2.4.2 XYZ platformMicrofabrication is expensive, requires specialized equipment, and has multi-ple steps in fabrication. An alternative to microfabrication is the XYZ plat-352.4. DROPLET MICROFLUIDICSform, where the inlet is moved between vials or reagents (dispersed phase)and the oil (continuous phase). Interest in the development of microfluidicssystems with the capability of movements in three dimensions (X-Y-Z) hasgrown lately. In these systems, the input or the control mechanism movesin Cartesian coordinates to generate and manipulate droplets[83]. An exam-ple of systems that moves the controlling mechanism is shown in Fig.2.19,the latest version of DropLab by Xuyang Hu et. al.[84], where the systemcontrols magnets in magnetic digital microfluidic (MDM) technology.Alternatively, the system can move the inlet of the microfluidic system andgenerate droplets from a variety of analytes, they can be referred to as au-tomated dynamic inlet microfluidic systems (ADIM systems). Researchidentifies two primary categories of Automated Dynamic Inlet Microfluidic(ADIM) systems: those that modify existing liquid handling systems[85, 86]and those creating new platforms. Wen-Bin Du et. al. designed DropLab,shown in 2.20, an automated microfluidic platform for droplet-based reac-tions and screenings at nanoliter scales, achieving picoliter precision[87]. Itcan mix components to create composite droplets and conduct assays indroplet arrays, minimizing sample and reagent use. The system, devoid ofcomplex microchannels, can assemble droplets varying in size, composition,and sequence, with volumes as small as ≈20 pL. Droplet array is stored in aFigure 2.19: a) 4 individual units designed in the chip and a magnifiedimage of the thermoformed microwells and microchannels.b) The DropLabchip used in ELISA testing with 4 units for four parallel reactions. c) Thecalibration curve of the image-based optical detection module that correlatesOD with the droplet intensity ©X. Hu (2023) used with permission362.4. DROPLET MICROFLUIDICSFigure 2.20: a) DropLab with slotted-vial array. b) DropLab with multiwellplate. c) Droplet arrays sealed with wax for long-time storage. d) 2D-dropletarray formed by depositing the generated droplets in the capillary into anoil-immersed nanowell plate. The arrows represent the flow directions ofliquids in the capillary channels.©W.B. Du (2010) used with permissionglass capillary using sealing wax at the ends as shown in Fig. 2.20c.Helena Zec et. al. devised a microfluidic setup using a serial sampling loading(SSL) mechanism, integrating an automated vertical (Z) axis with manuallycontrolled horizontal (X-Y) axes[88], as shown in Fig. 2.21. A custom cap-illary adapter in the SSL system interfaces between a microcapillary and amulti-well plate. The adapter seals a well on the multi-well plate, generatinga temporary pressure chamber inside the sample well. The application ofpositive pressure forces the fluid into the microcapillary. This design facili-tates the creation of extensive sample libraries linked to microfluidic devicesand is capable of generating 150 nL droplets. The droplet is formed by mov-ing the inlet from its starting position above the analyte (in the continuousphase), lowering it into the liquid (dispersed phase), and then pulling it backup. As proof of concept, a linear combination array of sample plugs and fourdifferent reagents was created. Its key strength lies in its scalability, per-372.4. DROPLET MICROFLUIDICSFigure 2.21: a) The ‘Serial Sample Loading’ (SSL) system a) Schematic: Acustom SSL system was designed that employs positive pressure to inject asample plug into a microcapillary from a standard multi-well plate. b) Pho-tograph of a prototype device. The microfluidic device has a multichannelarchitecture [88] ©H. Zec (2012) used with permissionmitting analysis of numerous samples against multiple reagents (N samplesagainst M reagents (NxM)) without complicating the system’s design.Fabrice Gielen et. al. developed a platform tailored for nanoliter-scaleassays[89], as shown in Fig. 2.22a. The system uses a hook to make aninverted liquid handling system, which requires only 20 minutes to calibratebefore starting unsupervised operation. Standard deviations of the dropletlengths are typically less than 10% while using an average of 30 nL of en-382.4. DROPLET MICROFLUIDICSFigure 2.22: a) Schematic of the compartment-on-demand platform. Sam-ples (in bottomless tubes) are supplied by rotating an oil-filled carousel.Samples are withdrawn when a sample tube aligns to the PTFE tubing lo-cated underneath. b) Sequential operation of the COD platform. During allsteps of operation, the tubing is aspirating liquid at a constant rate[89]. c)Dolomite Mitos Dropix device ©F. Gielen (2013) used with permission392.5. RESEARCH GAP AND OBJECTIVESzyme per data point. The system utilizes PTFE tubing (0.2 mm ID, 0.4mm OD) and can hold 15 samples at a time. This system evolved intothe Mitos Dropix sampler through collaboration with Dolomite Microflu-idic [90], shown in Fig. 2.22b. This device enables digital programmingfor generating sequences from up to 24 distinct reagents, and its compati-bility with Dolomite’s microfluidic chips enhances functionality for dropletmanipulation. Mitos Dropix is an example of a commercial device, and theresearchers have used this device to develop an ancillary microfluidic system[91]. It has a price tag of £ 10,660, equivalent to CAD 19,0695 for the baseunit.Discussion on XYZ platforms as Droplet on DemandA product like Mitos Dropix has high utility as it can be paired with microflu-idic chips to perform additional functions such as droplet fusion, splitting,sorting, and storage. Although it requires minimal training to operate, thehigh price tag reduces its use in research. DropLab is an alternative XYZplatform, however, recreating is complex and requires high expertise.XYZ platforms are an alternative to microfabricated microfluidics chips asdroplet-on-demand generators. The advantages include a simple mechanismfor droplet generation and the convenient inclusion of multiple reagents. Af-ter developing the XYZ platform as a droplet-on-demand generator, it can beoperated by anyone with minimal training. One of the disadvantages of XYZplatforms is that they require more world-to-chip connections which mightintroduce issues with the flow. However, this can be seen as an opportunityto make the system more modular.An open-source XYZ platform that is easy and quick to implement to gen-erate droplets and perform immunoassays would allow for broader adoptionin the scientific community to make microfluidic research more accessible2.5 Research Gap and ObjectivesThe last section discussed the utility of XYZ platforms in droplet microflu-idics and their preference over microfabricated microfluidic chips. At the mo-ment the options available are either expensive devices (i.e. Mitos Dropix) orcustom setups (i.e. DropLab) restricted to a few laboratories. This study is5Exchange rate as of 17th September 2024402.5. RESEARCH GAP AND OBJECTIVESthe first step in designing a modular, cost-effective microfluidic system thatis easy to replicate and operates with the required accuracy.The overall objective of this study will be to develop a high throughputlow-cost microfluidic lab-in-tubing system capable of:1. generating droplet libraries from multiple analytes (minimum 2) witha volume coefficient of variance < 5%2. storing the droplet libraries3. detecting agglutination of biotinylated Bovine Serum Albumin (BSA)in 0.0128 µg/mL to 5000 µg/mL concentration range with StreptavidinbeadsTo fully understand the system’s capabilities, a parametric study of thedifferent variables involved will be carried out related to various modules.41Chapter 3Automated Dynamic InletMicrofluidics (ADIM) System:Version A3.1 OverviewThis study modifies a Prusa MINI+ 3D printer by replacing the print headwith a 3D-printed nozzle replacement module. The overall bill of materials,including the vacuum pump, 3D printer, and all chemicals, comes to approx-imately ≈ $ 2500 USD. The comparison of other ADIM systems in literatureand the proposed system is summarized in Table 3.1. This study imple-ments droplets in a plug flow configuration where droplets are encapsulatedby the tubing’s walls, facilitating the formation of internal vortices within thedroplets. This feature proves beneficial for the system’s diagnostic-orientedapplications.In recent years, 3D printers have become more affordable and widely avail-able. Furthermore, a 3D printer is equipped with motion control infras-tructure and pre-installed electronics, enabling an easy setup that requiresminimal technical knowledge. Consequently, by modifying a 3D printer, amicrofluidic droplet library generation system can be set up quickly. Thesystem generates monodisperse droplets from multiple analytes (96 differentanalytes in theory). Storage of the generated droplets in the droplet libraryis also shown.This chapter outlines the development of a high-throughput microfluidic lab-in-tubing system to complete the first research objective. This chapter hasbeen published as an article titled, “Automated dynamic inlet microflu-idics system: 3D printer adaptation for cost-effective, low volume,on-demand multi-analyte droplet generator", in the Lab on a Chipjournal, with me as the first author. [1]. Supplementary material to the423.2. THE DEVELOPED ADIM SYSTEMarticle can be found in Appendix B. Justin Farrell and I worked on themathematical modeling, designed, and performed the experiments together.I did the data analysis, and visualization, while Justin Farrell and Dr. IanG. Foulds proofread the manuscript. All images and tables in this chapterhave been reproduced from Ref. [1] with permission from the Royal Societyof Chemistry.3.2 The developed automated dynamic inletmicrofluidic systemThis section presents an overview of our innovative ADIM system, under-pinned by a key modification: a 3D-printed nozzle replacement module.Consequently, we detail the system’s operational setup, including the dropletgeneration mechanism and the integration of novel connectors for creating adroplet cartridge. We also highlight the dual operational modes enabled byMATLAB, which cater to different requirements in droplet generation.The main modification in the proposed system involves a 3D-printed nozzlereplacement module. There are several versions of the 3D-printed nozzlereplacement module. The basic replacement module is designed to replaceonly the 3D printer’s nozzle rather than the complete print head. This min-imalist design was used to generate a train of droplets in an earlier iterationof the current system [6]. Although this design utilized the existing printhead, this constrained the use of specific tubing sizes and prevented the im-plementation of measurement and feedback sensors near the generation site.Therefore, the current replacement module replaces the complete print headfrom the X-axis carriage. This approach allows much more freedom in de-signing a replacement module with electrical probes, sensors, and differenttubing sizes. The 3D-printed nozzle was printed using a Prusa S1S printerwith Prusa’s signature orange resin (settings: 0.1 mm layer height, exposuretime 2.6 seconds, and an initial exposure time of 30 seconds). The STL filesof the replacement module can be found in the supplementary materials† .The electrical connections from the print head to the 3D printer’s electronicboard are left connected. Removing these electrical connections prevents theoperation of the 3D printer, as the firmware restricts user access under theseconditions. Changing the firmware would increase the technical expertiserequired for the system’s setup.Fig. 3.1 shows the schematic of the system, integrating the 3D-printed433.2.THEDEVELOPEDADIMSYSTEMTable 3.1: Comparison between this study and other worksMitos Dropix ®[89] Helena Zec[88] Elveflow© This StudySmallest Droplet 1 nL 3.8 nL 0.5 µL 1 µLCoefficient of Variance 4% Unknown < 3% 3%Number of Analytes 24 96 2 96+Microfluidic chips use Yes No Yes YesSetup Difficulty Low High Medium MediumClean room Required No Yes No NoDroplet Library Storage Yes No No YesCost (USD) ≈$15000 Unknown ≈$9000 ≈2500Figure 3.1: System schematic a) Setup of the system with modified Prusa Mini (3D printer) illustrating the use ofdroplet cartridge in the droplet generation system b) The tip of the 3D-printed nozzle replacement for extruder ofthe Prusa Mini with electrical electrodes and pipette tip. (Created with BioRender.com)443.2. THE DEVELOPED ADIM SYSTEMnozzle into the overall setup. The system utilizes the 3D printer’s Z-axismovement to transition the tubing tip from the light mineral oil (continuousphase) in the well to the analyte (disperse phase), and then back to the oil,thereby generating a droplet. The tip remains in the oil reservoir betweendroplet generation. The printer’s X-Y axis movement is used to choose thedesired well for droplet generation. A standard 96 well plate is submergedin an oil reservoir, constructed from 3 mm PMMA sheets. The oil level ismaintained higher than the well plate height to allow continuous dropletgeneration when switching between analytes in different wells of the wellplate, as seen in Fig. 3.1 a. The oil reservoir is designed to accommodatethe entire well plate, ensuring a consistent environment for droplet formationand analyte processing.The pipette tip is inserted at the end of the tubing which is then placedinside the nozzle as shown in Fig. 3.1 b. The shape and size (smallerdiameter) of the pipette tip compared to tubing or syringe needle reduces thegeneration of satellite droplets. The nozzle module has slots through whichdisposable acupuncture needles (250 µm by 40 mm) are passed through toact as electrical probes. When the probes touch the analyte, the circuitis completed and the resistance change allows the user to detect the levelof the analyte in the well. The electrical probes have been tested withdyed deionized water (current study) and Streptavidin beads (DynabeadsM-270 Streptavidin, Invitrogen and Vector Laboratories 10 mg VECTB2007biotinylated bovine serum albumin (BSA)) in previous work[4]. Fig. 3.1 bshows a difference between the end of the probes and the pipette tip, ∆h.The conduction level (CL) is determined by lowering the tip until the probesbegin conducting. Subsequently, the vacuum pump is turned on to start theoil flowing in the tubing. This is followed by progressively lowering the tipin increments of 0.1mm until the analyte starts to flow into the tubing. Theheight difference, ∆h, is the number of 0.1 mm steps required for the analyteto start flowing into the tube after the conduction level (CL).The tubing passes from the 3D-printed nozzle to the viewing window whichin this case is the Tygon ® tubing encased in transparent flexible resinfrom Prusa. The resin reduces the visibility of the tube walls improving theconditions for image capture via microscope. The next section of the tubingis connected to the cartridge connectors designed to create a droplet cartridgewhere a train of droplets can be stored and used separately. Consequently,tubing is connected to a vacuum pump via a flow sensor. An Arduino readsflow rate values from the flow sensor and relays them to the PC, whereMATLAB script uses these values. The vacuum pump, attached at the end453.2. THE DEVELOPED ADIM SYSTEMof the tube, maintains a constant negative pressure, pulling both the carrierand disperse phase into the tube. The system can be configured withouta droplet cartridge while maintaining the functionality of other sections ofthe system. Moreover, the cartridge containing the droplets can be detachedfrom the system and used separately or for storage. The tube, housing thegenerated train of droplets – referred to as the droplet cartridge can beconnected to a microfluidic chip using conventional methods. The ability togenerate and store a library of droplets for later use significantly increasesthe utility of a microfluidic system [89].Simple connectors are designed to create cartridges of tubing that can beplugged into the system and removed easily. These connectors can be 3D-printed and used with different types of tubing (Tygon® and PTFE). Theconnectors, shown in Fig. 3.2 are designed in pairs of male and female luerlocks to ensure alignment when assembling modules. The tubing is threadedthrough the orifices in the connectors and seamlessly aligned and pressedfirmly against each other. Thus, neither the carrier fluid nor the dropletcomes in contact with the 3D-printed material. There is a groove aroundthe orifice of male and female connectors, which house a silicone O-ring(purchased from amazon.ca ) with an inner diameter of 3 mm and a thicknessof 2mm embedded in it. When the connectors are assembled, the tubing iscompressed against each other and the O-ring is compressed tightly to sealthe gap to avoid the leakage of the fluids. Furthermore, Teflon tape is usedon the grooves of the male luer lock to improve the connector’s functionality.The orifice of connectors can be changed to match any tubing size (The STLfile of the cartridge connectors is available in the supplementary materials† .).The user can operate the system using MATLAB in two modes. In mode1, the MATLAB script directly runs the 3D printer, while in mode 2, theMATLAB script generates a G-code file, which can then be run on the printervia USB disk or any 3D printing host software such as Printrun[92]. Inmode 1, the MATLAB script can take instantaneous values of flow rate froma flow sensor attached in line before the vacuum pump. This allows thescript to incorporate real-time fluctuations in the flow to adjust the printer’smovement, which results in less variation in droplet target volume withina train of droplets. In the case of a stable calibrated pump (vacuum orsyringe ), the system can be operated in mode 2 with a single flow ratevalue, without the instantaneous values from the flow sensor. The generateddroplet train will have a variance higher than the fluctuation of the flow rate.The analyte leveling is executed manually before the G-code is executed forthe generation of droplet trains.463.2. THE DEVELOPED ADIM SYSTEMFigure 3.2: Schematics of droplet cartridge connectors (male and female).a) CAD Model with O-ring b) 3D-printed (on Prusa SL1S) cartridge con-nectors.3.2.1 CalibrationOnce the setup of the system is complete, the next step is to calibrate thesystem to generate droplets of the desired volume accurately. This sectioninitially outlines the approach for calibrating each component of the system,followed by the introduction of a mathematical model that incorporates thegeometry of a standard 96-well plate. Subsequently, a detailed explanation ofthe calibration methodology follows, which includes adjustments for varioussystem parameters and real-time flow rate measurements to ensure precisedroplet generation.The system that moves the syringe tip down into the analyte and retracts itback to oil will generate a droplet with the volume as expressed in Equation3.1, where Q is the flow rate and the tanalyte is the time the tip is in theanalyte. Equation 3.2 shows a further division of the tanalyte, where tup andtdown are the time the tip is moving within the analyte. tdelay is the timethe tip is at rest in the analyte. The tip’s movement is constrained by thespecification of the 3D printer’s model. The maximum Z-axis speed limitfor the Prusa Mini+ model is 720 mm/min (12 mm/s), set by the G1 F720command.Vdroplet = tanalyte ×Q (3.1)473.2. THE DEVELOPED ADIM SYSTEMttotal = tdown + tdelay + tup (3.2)The G4 command in the G-code of the 3D printer enables the user to setdelays in milliseconds or seconds. For example, entering ‘G4 P100’ intro-duces a delay of 100 milliseconds, while ‘G4 S100’ results in a delay of 100seconds. However, the 3D printer (Prusa Mini+) has a built-in delay whentwo consecutive commands are executed. Therefore, if the user commandsthe printer to move down into the analyte and retracts in the next command,the resulting droplet has a larger volume than expected indicating the delaythe 3D printer incorporated between the two consecutive commands. Thetip’s movement for different programmed delays was captured in a video at50 frames/s (repeated three times) and analyzed by a MATLAB script. Theaverage results from the video analysis of the tip’s movement are presentedin Table 3.2. Comparison between different programmed delays and theactual delay executed by the 3D printer shows a difference between the de-lay programmed and the actual delay executed, which is dependent on the3D printer. Due to this fact, the size of the droplet generated can not becalculated without incorporating this difference in the calculations. In thetable 3.2, the command G4 P0 is symbolic as when the delay of 0ms is re-quired, there is no delay command between the two consecutive commandsin G-code.Due to the negative pressure, there is a difference between the point at whichthe analyte initially contacts the descending tip and the point of last contactduring the ascent of the tip. The tip’s and the analyte’s surface movement isobserved to be similar to the Wilhelmy plate method for measuring contactangle hysteresis, where a plate is moved into a liquid or pulled out of theTable 3.2: Results of 3D printer movement analysis for different programmeddelaysG-code Command Programmed Delay (ms) Actual Delay(ms)G4 P0 0 20.0G4 P1 1 106.6G4 P10 10 113.4G4 P50 50 153.4G4 P100 100 213.4G4 P150 150 260.0G4 P200 250 300.0G4 P500 500 608.0483.2. THE DEVELOPED ADIM SYSTEMliquid to measure the contact angle [93]. This wetting hysteresis effect isenhanced because of the applied negative pressure. Additional volume addedto the droplet due to this effect is not measured but incorporated in theempirically derived calibration method.After the time in the analyte, the flow rate is the second factor that deter-mines the volume of the droplet. The flow rate (Q) is dependent on theHydraulic resistance and pressure difference according to Hagen-Poiseullelaw[94]. Hydraulic resistance is dependent on factors such as length, di-ameter, and the material of tubing[95]. Moreover, connecting additionalcomponents ( e.g. flow sensor ) inline to the tubing can also affect the hy-draulic resistance. Therefore, a mathematical model of the system shouldincorporate the dynamics of the 3D printer, the geometry of the well plate,and factors related to the system configuration( tubing size, material, length,wetting hysteresis, inline components, etc.)A general application of the developed microfluidic system will be restricted ifthe mathematical model is derived for a specific 3D printer with fixed systemparameters. However, a calibration method derived empirically would allowthis method to be applicable to any 3D printer or similar microfluidic setup.Calibration Mathematical ModelThe calibration method empirically derives an Equation to provide a delayto be programmed in the 3D printer, which is required to generate a dropletwith a desired volume at the measured flow rate. However, the geometryof the well plate has to be incorporated into the model to track the level ofthe analyte in the well plate. Moreover, The model allows us to predict thechange in height of the analyte level after the desired volume of the dropletis removed. A standard 96 well plate (Axygen 96 Well PCR Microplate) issubmerged in a reservoir of mineral oil (Sigma M5904) for this study.∆h =VdesiredπR2(3.3)Ri =RH × (ALinitial +Himag)H(3.4)∆h = ALinitial − 3√[AL3initial −2∆VdesiredAL2initialπR2i](3.5)To generate a geometry-based model, the well is divided into two segments;the cylindrical portion and the conical portion. The top of the well plate493.2. THE DEVELOPED ADIM SYSTEMFigure 3.3: Illustration of the mathematical model for the geometry of thewell in a standard 96 well plate.has a cylindrical shape, therefore Equation 3.3 gives the height change afterdesired volume is removed. The bottom of the well plate is more conic inshape thus the conical portion of the well plate is modeled as a circularfrustum (partial cone). Slicing a whole cone we get a frustum and a smallercone. As the bottom of the well plate is modelled as a frustum, the smallercone is an imaginary shape that completes the frustum into a cone withthe radius of the well in the well plate. Fig. 3.3 shows the modeling ofthe conical section of the well, where the frustum is shown in red and theimaginary smaller cone with height Himg in yellow.The value of initial radius at the analyte level (Ri) is calculated throughthe principle of similar triangles, as outlined in Equation 3.4. This approachinvolves comparing two triangular configurations: one comprising the entireconical section of the well in addition to an imaginary smaller cone and theother consisting of the initial analyte level with the imaginary smaller cone.In the model, some quantities are measured and remain fixed e.g. radius(RH=2.65 mm) and height (H = 19 mm ) of the whole cone; and the Himag(8 mm). On the other hand, the initial analyte level (ALinitial) is sensedusing resistance test with two electrical probes on either side of the pipettetip. Height change in the conical portion of the well plate can be calculatedthrough Equation 3.5, using the initial analyte level (ALinitial) and initial503.2. THE DEVELOPED ADIM SYSTEMradius (Ri). ALfinal is the level of analyte after the∆Vdesired is removed fromthe well. The analyte level (AL) is updated after each droplet generation.Calibration MethodologyThe first step is to set up the system by deciding the factors related to thetubing (length, diameter, material), inline components (e.g. flow sensor),and droplet detection system, as shown in Fig. 3.1 a. The initial analytelevel is detected using the resistance test and programmed into MATLABas ALinitial. Total time ( ttotal ), Equation 3.6, is calculated using a targetdroplet volume (µL) and instantaneous value of flow rate (µL/min) from theflow sensor. This is the time the syringe tip has to be inside the analyte inorder to generate the desired droplet.Using Equation 3.5, ∆h is calculated and substituted into Equation 3.7.Subtracting ∆h from ALinitial provides us the point into the analyte thetip should stop (ALfinal). A single droplet is then generated starting aboveanalyte (e.g. 3 mm above ALinitial), stopping at ALfinal, with a delay ofttotal ms, and then retracting back to the starting position. The size of thegenerated droplet is characterized through the viewing window, which willhave a volume larger than the target volume. Equation 3.8 gives the timethe tip actually spends in the analyte (tactual) using the measured flow rateand the volume generated. Equation 3.9 defines tsystem as the differencebetween the tactual and the tdelay. The parameter tsystem encompasses allthe dynamics of the system, such as tin, tdown, and the build-in delay of the3D printer. Consequently, the Equation 3.10 finds the difference of ttotal andtsystem to give us the delay the 3D printer has to be programmed (tdelay) toachieve the tactual which results in the droplet of the target volume.ttotal =Target Droplet VolumeFlow rate(3.6)ALfinal = ALinitial −∆h (3.7)tactual =Volume Generatedflow rate(3.8)tsystem = tactual − ttotal (3.9)tdelay = ttotal − tsystem (3.10)513.3. RESULTS AND DISCUSSIONThis process is repeated for multiple flow rates in the operable range of thesystem, which for our case is 50 µL/min to 150 µL/min. Tsystem vs flowrate (µL/min) is plotted to find an Equation which relates measured flowrate to Tsystem. For each droplet in the train, flow rate is measured whereasALfinal, Tsystem is calculated to give the tip’s stopping point in the analyteand the delay to be programmed.3.3 Results and discussion3.3.1 CalibrationThe system described in the previous section is set up with Tygon® tubing(150 cm, 0.508 mm I.D.), a flow sensor (Sensirion SLF3S-0600F) at the end ofthe line, and a viewing window at 50 cm, 80 cm, and 110 cm. The calibrationtests have to be carried out at the maximum, minimum, and middle of flowrate’s operating range. A vacuum pump (Gemini 2060 Dry Pump, Welch) isused to run the system with a negative pressure with a resulting flow rate inthe range of 50 µL/min to 150 µL/min. Once the tests are carried out for thenominal values of 50 µL/min, 100 µL/min, and 150 µL/min in the conicalFigure 3.4: Results of calibration of three versions of the system for Tsystemas a function of flow rate: Case 1 (black) and Case 2(red) are for the versionof the system with 150cm of tubing, whereas the case 3 (blue) is for 100cmof tubing (50 cm of Tygon Tubing and 50 cm of PTFE droplet cartridge).523.3. RESULTS AND DISCUSSIONsection of the well, Tsystem is calculated for each test and plotted in Fig. 3.4.It is recommended to run at least four calibration tests, e.g. case 1 in Fig.3.4, to achieve accurate results. However, more tests can be carried out, e.g.cases 2 and 3 in 3.4. The flow rates deviated from the targeted nominalvalues, clustering in the proximity of the intended flow rates because of thefluctuations in the negative pressure. The best-fit line gives the relationshipbetween the Tsystem and the flow rate. A linear relationship gives resultswith adequate accuracy while saving on computing power as compared toa higher-order Equation. This will facilitate the process of converting thissystem to a standalone device in the future.Fig. 3.4 shows three cases where case 1 (red) and case 2 (black) illustratethat two system setups with the Tygon tubing of 150 cm with the sameparameters can still change the relationship between Tsystem and flow rate.Case 3(blue) shows a system where the length of the tubing is changed to100 cm comprising of 50cm of Tygon tubing and 50 cm of PTFE dropletcartridge. The advantage of an empirically driven relationship is that thesame calibration method works with any changes to the system.[This paragraph has been added for the Dissertation] The vacuumpump required stabilization for which a waste reservoir and pressure stabi-lizer were integrated into the system as shown in Fig. B.2 (Appendix B).The waste reservoir collects excess liquid and provides a buffer volume, whichprevents abrupt fluctuations in pressure. Additionally, an empty plastic bot-tle serves as a pressure stabilizer, effectively acting as a dampening chamber.This setup smoothens out pressure oscillations caused by the vacuum pump,ensuring steady airflow and flow rate through the tubing. However, this in-troduces sources of error that affect the calibration of the system. Fig. 3.4shows a large variations for the same length of Tygon tubing (case 1 and 2)due to non-ideal seals, nonlinear pressure stabilization and dynamic changesin the system. Moreover, other factors such as kink in tubing, tubing diam-eter inconsistencies, or improper connections in the system also contributeto variations in the calibration curve for Tsystem.The generated droplet trains will be evaluated using the coefficient of varia-tion (CV%), defined as the ratio between the standard deviation in dropletvolume and the average droplet volume of the train, as a measure of themonodispersity.533.3. RESULTS AND DISCUSSION3.3.2 Single Train of Droplets with Constant VolumeThe system is setup with 150 cm of Tygon tubing, without a droplet car-tridge section, and operated in mode 1 (MATLAB directly controlling the 3Dprinter). Initial analyte level is detected using electrical probes after pipet-ting 30 µL of analyte (dyed water) into one of the wells of the 96 well plate.MATLAB takes instantaneous flow rate readings using a Sensirion flow sen-sor, the analyte’s initial level (ALinitial), and the relationship between theTsystem and flow rate to drive the 3D printer to produce a train of dropletswith constant volume. The size of droplets in a train is characterized usingthe Droplet Morphometry and Velocimetry (DMV) MATLAB plugin [96].The left y axis of the Fig. 3.5 a-c shows the results of the trains of dropletsgenerated at different flow rates for various target volumes in the conicalportion of the well using 30 µL. The left y-axis of Fig. 3.6 a shows variationwithin each train of droplets at different flow rates for different target vol-umes. As mentioned in section 2.1 (the system), the offsets of the electricalprobes and the tubing introduce an offset into the volume of the generateddroplets. The fluctuations in the flow rate due to the vacuum pump areanother cause for variations in the volume within a train of droplets. Fig.3.6 a (right axis) shows the percentage difference in volume from the meanvolume is less than 5% which indicates that the train of droplets generatedis monodisperse.The system demonstrates the required level of precision in multiple measure-ments (n=3), as evidenced by the minimum achievable standard deviation of0.009 µL for a droplet train of a single analyte with constant volume. Thissignifies a tight clustering of data points around the mean, highlighting thesystem’s ability to consistently produce droplets with minimal variability involume. Additionally, a maximum CV% of 2.93% for the volume of dropletsin a train further emphasizes the system’s capacity for producing dropletswith a high degree of reliability and reproducibility. These values highlightthe system’s capability to yield consistently accurate measurements, crucialfor droplet microfluidics.Fig. 3.6 b shows that the mean percentage difference of droplets in all thetrains is less than 8%. Overall, the system’s accuracy in achieving targetvolumes appears to be dependent on the interaction between the flow rateand droplet volume, with some combinations yielding more consistent resultsthan others.Interestingly, the data shows that the train of droplets has lessvariation from the target volume at a higher flow rate (i.e. 150 µL/min) as543.3. RESULTS AND DISCUSSIONFigure 3.5: Plots of droplet trains generated at different flow rate with differ-ent target volume a) generation at 150 µL/min b) generation at 100 µL/minc) generation at 50 µL/min. The error bars in the volume data are too smallto be seen on the plot. 553.3. RESULTS AND DISCUSSIONFigure 3.6: a) box plot to illustrate the variation within a train of dropletsat different flow rates and the percentage difference of droplet size from themean size of the droplet train.b) Box plot to illustrate variation observed inthe difference of droplets from their target volume in the train of droplets inFig. 3.5.compared to a low flow rate (i.e. 50 µL/min). This can be attributed to thereduced effect of wetting hysteresis of the tip discussed in section chapter3.2.1 at a higher flow rate.The frequency of the droplet generation is dependent on the speed of the Z-axis movement of the 3D printer, and the distance the tip has to move above563.3. RESULTS AND DISCUSSION(ALinitial) to avoid wetting hysteresis. The Prusa Mini has a maximum speedof 720 mm/min (12 mm/s) for the Z-axis and the tip has to move at least3mm above the (ALinitial) to break the wetting hysteresis. As such the tiphas to move approximately 6mm between each droplet, limiting the frequencyto 2 Hz. At lower flow rates the droplets can be packed together denselywhere the time between the droplets remains approximately 0.5 seconds;however, the volume of the carrier fluid between the droplets will be small.Table 3.1 lists the smallest droplet generated by different systems. Thesmallest droplet generated for this study is 0.75 µL, however, smaller dropletscan be generated with tubing with a finer diameter at a lower flow rate. Toillustrate the system’s capability to generate a longer train of droplets, a trainof 100 droplets of 0.75 µL from 100 µL of analyte is generated at around 100µL/min with CV% of 2.7%., shown in Fig. B.1 [Appendix B].The system utilizing calibration case 1, shown in Fig. 3.4, was used to gener-ate 20 droplets from 25 µL analyte (Vector Laboratories 10 mg VECTB2007biotinylated bovine serum albumin (BSA) and Dynabeads M-270 Strepta-vidin, Invitrogen) and evaluated agglutination in previously published work[4].Single train of droplets with constant volume - Cylindrical sectionIn section 3.2.1, two Equations are presented for the calculation of ∆h. Inthe previous section, Equation 3.5 is used as the volume of analyte dispensedin the well is 30 µL, thus the operating in the conical section of the well inthe well plate. However, if large volume (e.g. 150 µL ) is dispensed then thesystem would be operating in the cylindrical section of the well, and Equation3.3 would be used in operation. The right y axis of Fig. 3.5 a-c shows theresults (from multiple measurements: n=3) of droplet trains generated inthe cylindrical section with the same parameters as section 3.3.2.The maximum CV% achieved for volume of droplets in a train within thecylindrical section of the well is 2.51% (flow rate: 150 µL/min, target dropletvolume: 1.0 µL), which is better than the accuracy achieved in the conicalsection of the well. The improvement is due to the fact that the Equation3.3 relates ∆h to ∆V olume more accurately than the modeled Equation3.5. This proves that the system can produce a train of droplets with lowvariance in volume throughout the well. Moreover, it also substantiates theclaim that the system can work for wells/ vials with different geometries,as long as the user can relate ∆h to ∆V olume. For the rest of the study,trains of droplets will be generated in the conical section of the well using a573.3. RESULTS AND DISCUSSIONsmall volume of 30 µL. Table 1 in the supplementary material provides thecoefficient of variance for each train of droplets in Fig. 3.5 a-c for conicaland cylindrical sections.3.3.3 Single Train of Droplets with Variable VolumeThe system can be programmed to generate droplets of different volumes ina sequence of the same analyte. In literature, this capability is particularlyuseful in generating merged droplets of varying concentrations by combiningmultiple trains of analytes with different volumes [97]. Fig. 3.7 a shows atrain of droplets where the sequence of 1.0 µL, 1.5 µL, and 2.0 µL dropletsis repeated four (4) times. The mean CV% (from multiple measurements:n=3) across different droplet sizes indicates a trend in the system’s precisionrelative to a target volume. For the 2.0 µL target volume, the mean CV% is0.81%, denoting high precision with minimal variability in droplet size. Themean CV% increases for 1.5 µL droplets and reaches its highest for 1.0 µLat 3.25 %, which still indicates good precision, though with a slight increasein variability. The consistent pattern and minimal error bars (too small tobe seen in Fig. 3.7) suggest that the system can reliably generate varyingvolumes of droplets in a sequence, critical for applications requiring preciseFigure 3.7: A droplet train of variable target volume sequence repeated four(4) times a) Sequence of 1.0 µL, 1.50 µL, and 2.0 µL repeated b) images ofthe actual droplets generated of variable target volume.583.3. RESULTS AND DISCUSSIONvolume control.3.3.4 Multi-Analyte droplet trainsThe versatility of the 3D printer is in its ability to have programmable move-ments in the X-Y and Z axis. The designed system uses the movement inthe Z-axis to generate a train of droplets from a well with either constantvolume or variable volumes. Conversely, it utilizes the X-Y axes movementto choose a well to generate a train of droplets from the 96 well plate. In astandard 96 well plate, the center of each well is 9 mm apart. Currently, asingle well plate is immersed in the oil reservoir. However, the system hasthe potential to accommodate multiple well plates in a larger oil reservoir,depending on the size of the 3D printer’s print bed.Maintaining the initial analyte level is crucial for generating droplets fromdifferent analytes in various wells. The initial analyte level (ALinitial) isrecorded for each well with an analyte and used by the computer to generatea train of multi-analyte droplets. The system is demonstrated to generate atrain of droplets from 3 different analytes; however, theoretically, the systemcan produce a train of droplets containing 96 distinct analytes of varyingvolumes. Variations in the initial analyte’s level (ALinitial) between the wellsarise due to dispensing errors and the leveling of the well plate. The levelingof the 3D printer’s print bed precedes the placement of the oil reservoircontaining the well plate, aiming to minimize errors in the initial analytelevel (ALinitial). Additionally, prolonged immersion of the well plate in theoil reservoir leads to deformation, resulting in an imbalance of the plate.When generating a train of different analytes, the frequency of droplet gen-eration decreases due to the need for the tip to move between the wells ofthe well plate as shown in Fig.3.8. The tip moves to a point (Zhigh) abovethe well plate within the oil reservoir and then moves in either the X orY-axis. Once positioned over the desired well, the tip then lowers to theALfinal point (e.g. ZB), calculated as mentioned in section 3.2.1 calibrationmethodology.Fig. 3.9a shows a train of droplets consisting of three (3) analytes, with five(5) droplets from each. Error bars represent standard deviations for multiplemeasurements (n = 5). The mean CV% across all droplet measurements isless than 5 %, signifying that the generated droplets are monodisperse, whichis a measure of the system’s precision. Meanwhile, the average standard de-viation is about 0.048 µL, which provides an absolute measure of variability593.3. RESULTS AND DISCUSSIONFigure 3.8: 3D Printer movements to generate multi analyte droplet train.in droplet sizes. These statistics suggest that while there is some fluctuationin the volume of droplets generated, generally the system maintains a consis-tent volume, demonstrating its capability to generate droplets of the targetsize across different wells. The repeating pattern does not display significantdrift in volume over the sequence of wells, which implies that the generationsystem maintains its performance consistently across multiple wells.The ALfinal value decreases as more droplets are generated from the analytethus increasing the distance between the Zhigh and ALfinal, thus the intervalbetween the droplets generated increases progressively. This aspect can beadjusted in the MATLAB script.Fig. 3.9b shows a train of different analytes, each with varying volumes, gen-erated with the same level of accuracy. A sequence of 1.0 µL from analyteA, 1.5 µL from analyte B, and 2.0 µL from analyte C is repeated ten (10)times to generate a train of 30 droplets. The trend of a decrease in CV% asthe volume increases suggests that the system is slightly more precise withlarger droplet volumes for a constant 100 µL/min flow rate. The highestCV% is observed for the droplet volume of 1.0 µL, which is 3.61 %. All an-alytes demonstrate stable performance throughout the sequence, indicatingthat the system can reliably generate droplets with variable volumes with-out drift over successive cycles. This suggests that the system is calibratedwell and can accurately manage different volumes for various analytes in ahigh-throughput manner.603.3. RESULTS AND DISCUSSIONFigure 3.9: Droplet trains consisting of multiple analytes in different wellsof the 96 well plate a) droplet train generated at 100 µL/min flow rate withtarget volume of 1 µL consisting of three different analytes (A,B, and C)b) droplet train generated at 100 µL/min flow rate with a sequence of 1.0µL from analyte A, 1.50 µL from analyte B, and 2.0 µL from analyte C isrepeated ten (10) times 613.3. RESULTS AND DISCUSSIONHigh-throughput of the systemIn comparison to existing platforms, our developed system showcases a greatercapacity for generating and handling droplet libraries, particularly in termsof the variety of analytes it can accommodate. For instance, while DropLabdeveloped by Wen-Bin Du et. al.[87] is designed around a 384-well plate,Prusa Mini+ modified in our system has room for two such plates simultane-ously, effectively doubling the potential throughput. Similarly, although theDropix system[91], evolved from Fabrice Gielen and team’s initial design[89],offers up to 24 loading slots, our setup’s capability to create a library from96 distinct analytes marks a significantly higher throughput. Moreover, thesystem devised by Teste et al.4, although capable of working with both 96and 384 well plates, is limited by its capacity to house only one plate at atime, a limitation our system has the theoretical capacity to overcomes byaccommodating two plates on the print bed.This enhanced throughput, in terms of analyte diversity and the ability toprocess multiple microtiter plates, sets our system apart from its prede-cessors. Additionally, the developed system’s capacity for storing dropletlibraries for future use further amplifies its practical utility. This featurereduces the emphasis on droplet generation frequency from a single source,allowing for more flexible experimentation at a higher throughput of samples.3.3.5 Droplet libraryThe system is set up with 50 cm Tygon ® tubing (0.508 mm I.D.) with50 cm of PTFE tubing (0.56 mm I.D.) cartridge before the flow sensor (asshown in Fig. 3.1 ). It is calibrated to determine the relationship betweenTsystem and flow rate. This relationship is expressed in Equation 3.11, asshown in Fig. 3.4 (Case 3). The resulting train of droplets from a sin-gle analyte is visible in Fig. 3.10. The system demonstrates versatility byaccommodating any configuration or sequence of different components andtubing types. PTFE tubing is used due to its resilient properties, and itssurface exhibits long-term resistance to fouling, unlike Tygon ® tubing. InPTFE tubing, the spacing between droplets remains constant over severaldays. Application such as bacteria susceptibility to antibiotics involves stor-ing droplets in a tubing and imaging throughout the incubation period totrack progress. Boedicker et. al. used wax to seal the Teflon tubing, forantibiotic screening experiments, before placing it in a petri dish for incuba-tion and imaging[98]. In contrast, in Tygon® tubing without surfactant indyed deionized water[99], the droplet breaks apart within 24 hours. Long-623.3. RESULTS AND DISCUSSIONFigure 3.10: Image of a droplet train from a single analyte stored in a car-tridge using custom connectors.term storage in Tygon® tubing leads to degradation of the inner surface,which causes the analyte to adhere to the tubing wall. Therefore, a frequentreuse of the microfluidic structure is thus prevented.Tsys = (−4.310)× (flowrate) + 1028 (3.11)Cartridge connectors, printed in clear tough resin, allow visual inspection ofdroplets traveling through it. Fig. 3.10 shows the cartridge closed onto itself;however, blank male and female connectors without a channel are printed,so that it closes the cartridge at either end.Figure 3.11: Schematic of how to use the droplet cartridges. (Created withBioRender.com).633.3. RESULTS AND DISCUSSIONThe droplet cartridge can also be used separately, as shown in Fig.3.11, withadditional tubing. Although the schematic shows the droplets in the car-tridge leading to a viewing window, it can also be connected to a microfluidicchip in any traditional manner. The primary issue is that connecting anddisconnecting the cartridge introduces air bubbles at either end. To avoidthis, the cartridge can be disconnected and connected while the connectorssubmerged in an oil reservoir.In our diagnostic-focused application, the necessity for a large library is lesscritical compared to comprehensive testing scenarios such as dose-responseevaluations. While our developed system has the capability to generateextensive libraries, the practicality of storing such large volumes of librariesposes a significant challenge.3.3.6 Applications of Developed SystemThe droplet library generators facilitate a shift towards more efficient, cost-effective, and precise experimental setups in various research contexts ofdroplet microfluidics. In a study by Zhang et al., a library of 192 uniquesequences is manually pipetting directly from a well plate to quantify as-says for oligonucleotide amplification[100]. A droplet library generator willincrease the throughput of their system multi-fold showcasing the utility ofdroplet library generators.Furthermore, the use of the droplet library generator is illustrated in the de-velopment of DropLab by Wen-Bin Du et al.[87]. Application of the DropLabis demonstrated in the inhibition of β -galactosidase assay. This assay gener-ated 2 nL droplets containing varying concentrations of enzyme, substrate,and inhibitor to derive an inhibition curve and calculate the IC50 value forthe inhibitor. The same system screens and optimizes crystallization condi-tions for lysozyme, a model protein, using a series of 50, 12 nL droplets. Thedeveloped system showcases higher throughput than DropLab at a similarlevel of accuracy, therefore such a system can be used in enzymes or proteinassays.The utility of the developed system is further validated in the context ofevaluating Biotinylated Streptavidin bead agglutination assays quantified inour previously published research[4]. The results of this study demonstratethe application of this system for quantification of agglutination assays.Collectively, these applications demonstrate the significant potential of dropletlibrary generators in microfluidics research. The ability of the developed sys-643.3. RESULTS AND DISCUSSIONtem to efficiently produce droplets of precise volumes for a variety of assays,coupled with its cost-effectiveness, positions it as a viable alternative to tra-ditional automated liquid handling systems. The droplet library generator’sprecision and adaptability make it a valuable tool in microfluidics, offeringresearchers a means to conduct intricate experiments with reduced resourceconsumption and enhanced accuracy.3.3.7 Analysis of Random and Systematic Errors[Following discussion has been added for the Dissertation] The usershould be aware of the random and systemic errors in the developed system.Alignment of the electrical probes is important but hard to ensure for ADIMsystem version A. The conduction level will be measured when the higherprobe of the two touches the analyte, and as the conduction level is measuredby lowering the tip in 0.1mm steps, there will be a slight error in detectingthe analyte level. This error can accumulate as the droplets are generatedand the analyte level lowers to the bottom of the well.On the other hand, the user needs to align the tip to the top of the meniscusof the analyte at the center, and any offset in this will lead to an error in thevolume of the generated droplets. A wetting hysteresis causes the analytelevel to extend above the final analyte level when the tip exits the analyte,due to the negative pressure through the tip and wetting of the tip. Theflow rate or the viscosity of oil and analyte leads to the wetting hysteresisbehaving differently, leading to an error in droplet generation.Prolonged immersion in the oil reservoir causes deformation of the 96-wellplate, leading to variations in the initial analyte level (ALinitial). Until thedeformation is visible, it will continue to affect the ALinitial. Calibration forADIM system A uses a mathematical model of the well plate. The trainsof droplets in the cylindrical section of the well plate have better CV% ascompared to the conical section in Fig. 3.5. The improvement is becausethe Equation 3.3 relates ∆h to Vdesired more accurately than the modeledEquation 3.5.There are errors that can propagate through the system and worsen thedroplet generation with time. ADIM system version A measures the initialanalyte level (ALinitial) before generating the first droplet. For the seconddroplet, the ALinitial is calculated by subtracting the volume of the dropletgenerated from the ALinitial of the first droplet. Fluctuation in the flowrate can cause the system to create droplets with a greater or lesser volume653.4. SUMMARYthan the target volume. This causes an error in ALinitial for the next dropletcalculation, which can compound with subsequent droplet generation. More-over, errors in executing G-code for one droplet can lead to a similar effect.In multi-analyte systems, accumulated variations further exacerbate errorpropagation. When droplets are generated sequentially from multiple wells,errors in leveling the well plate and measuring analyte height can propagateacross the sequence, causing cumulative errors in droplet volume or spacing.Over successive wells, even small initial inaccuracies can grow into signifi-cant deviations. Therefore, precise leveling and consistent monitoring duringoperation are vital.3.4 SummaryThe research presented in this chapter fulfills the need for a cost-effectivemodular microfluidic droplet generation system with the capability to storemulti-analyte droplets. The developed automated dynamic inlet microfluidicdroplet generation system displays the capability to reliably produce dropletsconsisting of arbitrary size and composition limited primarily by the size ofthe bed and speed of the 3d printer used. Our system offers the advantage ofautomation and equipment standards that accompany a 3D printer comparedto classical microfluidics. The setup of the proposed system can be completedwithin a working day.All custom modules of the system, e.g. 3D printed nozzle, can be 3D printedon the printer before the modification. Moreover, the 3D printer can berestored by re-attaching the print head to the X-axis carriage. The technicalexpertise required to set up the proposed system is minimal compared tomost systems in the literature. It requires no clean room for the fabricationof droplet-generating microfluidic chips or the operation of valves, which isthe most common droplet-on-demand generation mechanism.The proposed system is 3.6 times cheaper than the commercially availabledroplet generation starter pack from Elveflow©, and 6 times cheaper thanMitos Dropix. Laboratories in the initial stages of droplet microfluidics re-search would prefer to use the proposed system to develop their expertisebefore making large financial investments. Mitos Dropix allows for 24 ana-lytes, while the proposed system accommodates a 96 well plate. However,the number of well plates that can be placed depends on the size of theFused Deposition Modeling (FDM) 3D printer’s print bed; larger beds canaccommodate multiple well plates.663.4. SUMMARYThis article fulfills the following objective of the research to develop a highthroughput low-cost microfluidic lab-in-tubing system capable:1. generating droplet libraries from multiple analytes (minimum 2) witha volume coefficient of variance < 5%2. of storing the droplet libraries67Chapter 4Integrating Image Analysis andLow-Cost OpticalTransmittance:An Application of ADIMSystem Version A4.1 OverviewThis paper presents a dual-phase Lab-in-Tubing microfluidic platform thatintegrates image analysis with low-cost optical transmittance measurementsto quantify agglutination across a broad concentration range. This workhighlights the potential of combining microfluidic technologies with opti-cal detection methods for more accurate, efficient, and accessible diagnostictools.The droplet trains of the model immunoassay of streptavidin-biotin weregenerated using the ADIM system version A, outlined in the previous chap-ter. This chapter focuses on the application of the ADIM system versionA.This chapter has been submitted to the Sensors and Actuator A: Physi-cal journal as an article titled “Integrating Image Analysis and Low-Cost Optical Transmittance for Quantification of Agglutination ina Dual-Phase Automated Dynamic Inlet Microfluidics System".Supplementary material for the article can be found in Appendix C. JustinFarrell collaborated in data collection, contributed to the discussions on anal-ysis, and reviewed the manuscript. This research uses the data analysis ofthe low-cost optical transmittance measurements but the details are not pre-684.2. MATERIALS AND METHODSsented in this research.The detailed analysis for the low-cost transmittance detection of agglutina-tion has been published in IEEE Biosensors Conference 2023 where I am thesecond author. The paper is titled “Low-Cost Optical TransmittanceMeasurements for the Detection and Quantification of Agglutina-tion in Dual-Phase Lab-in-Tubing Microfluidics" [4]. I worked on theanalysis with Justin Farrell and produced the figures for the manuscript.4.2 Materials and methodsThe system diagram in Fig. 4.1, illustrates the droplet generation and detec-tion mechanisms. A standard 96 well plate is submerged in an oil well filledwith light mineral oil, and premixed analytes, consisting of Dynabeads M-270 Streptavidin (Invitrogen) and 10 mg of VECTB2007 biotinylated bovineserum albumin (BSA) (Vector Laboratories), are pipetted into a well of theplate. The PTFE tubing (0.556 mm I.D.) is passed through a 3D-printednozzle attached to a modified Prusa Mini + 3D printer. The movement inthe Z-axis of the nozzle moves the tip from its rest position in the oil (con-tinuous phase) into the analyte (dispersed phase) and then back into the oilto generate a droplet of analyte of the desired size under a constant negativepressure from a vacuum pump (Gemini 2060 Dry Pump, Welch). A moredetailed description of the droplet generation system can be found in ourpreviously published research [1][Chapter 3]. The generated droplet train isthen passed under a Zeiss microscope with a Canon 5D Mark IV camera forvideo capture for the image analysis and quantification of the agglutination.Subsequently, the tubing passes through two standard optical line break sen-sor (LBS) units (RPI-121, Rohm Semiconductor), positioned 5.0 mm apart,controlled by a Teensy 4.1 microcontroller, which provides a 4x amplificationby a 16-bit differential ADC module (ADS1115, Adafruit).For this study, an analyte concentration range of 0.0128 µg/mL to 5000µg/mL of biotinylated BSA is chosen to be quantified by both, the imageanalysis, and LBS waveform analysis, working independently. Control is Dul-becco’s Phosphate Buffered Saline (sigma-aldrich) with 1% non-bio–tinylatedBSA (sigma-aldrich) and 1% Tween (Sigma-Aldrich). The non-biotinylatedBSA in the control concentration assures that the detected signal is not dueto non-specific binding.694.2.MATERIALSANDMETHODSFigure 4.1: System diagram for droplet generation and detection system. Droplets of premix beads and analyte aregenerated before passing two independent detection mechanisms: 1) Canon 5D Mark IV mounted on an invertedmicroscope for video capture and 2) low-cost optical LBS. a) An image of a droplet is split into bins in vertical andhorizontal orientations. An illustration of an image of a droplet of 1.6 µg/mL concentration split into vertical andhorizontal bins b) Agglutination signal waveform captured by the LBS for droplet train of 1.6 µg/mL concentration.The red and blue dash line shows the minimum and maximum, whereas the black solid line is the mean waveformfor all the droplets (Created with BioRender.com).704.2. MATERIALS AND METHODS4.2.1 Line Break Sensor Analysis MethodologyThe LBS signals for each droplet are analyzed to quantify agglutination inour previous published research [4]. This paper focuses on the image analysisof the droplet trains and quantification of agglutination and its comparisonwith the LBS results previously published. A brief description of the analysismethodology is presented below for convenience supported by Fig. 4.1b.Fig. 4.1b shows the maximum, minimum, and mean waveforms for thedroplet train (offset-compensated) for the 1.6 µg/mL dataset. The variationin the waveform is for different droplets in the same train of the same con-centration. These waveforms exhibit consistent shapes setting them apartfrom the control group, which contained a PBS buffer and nonbiotinylatedBSA. The peaks in the waveform signal align with the agglomerates in thedroplets. The mean waveform of the control is calculated in a similar manner,which illustrates the optical properties of a control droplet. The mean con-trol waveform is subtracted from each droplet’s waveform and the resultantwaveforms are analyzed for the position and height of peaks.4.2.2 Image Analysis MethodologyFor each concentration, a train of 25 droplets is captured in the video(1080x1920p at 25 frames/second) using a Canon 5DMark IV camera mountedon a Zeiss Axio Zoom.V16 in a bright field setup. MATLAB script extractsvideo frames with a droplet from the video, for each concentration. The im-ages are further processed to identify the droplet in the frame and crop theimage around the droplet, as shown in Fig. C.1 [Appendix C]. Each imagetitle is renamed to a specific format, TestXX Droplet YY ZZ.png, where XXis the test number corresponding to a concentration, YY is the droplet num-ber of that test and ZZ is the number of images of the YY droplet capturedwithin the viewing window.In each image, pixel data is organized into two distinct arrangements knownas horizontal and vertical bins. A vertical bin encompasses a specified num-ber of adjacent pixel columns, spanning the entire height of these columns.Conversely, a horizontal bin is a selection of rows, extending across the fullwidth of these rows. Fig. 4.1a, illustrates how a cropped image of a dropletat 1.6 µg/mL (Test07 Droplet 10 10.png) is split into vertical and horizon-tal bins. For understanding, vertical bin C1 includes all the rows (R1 toR5), whereas horizontal bin R1 includes all the columns (C1 to C10). Forinstance, a large agglomerate can be seen in the C8 vertical bin, while other714.2. MATERIALS AND METHODSsmaller agglomerates are spread throughout the droplet. The number of binsdepends on the size of the image which varies due to a difference in the lengthof the droplet. A bin size of 1 pixel is selected for this study. For each bin,the mean and standard deviation of the gray values are calculated.The vertical mean of a bin is determined by averaging the grayscale intensityvalues of all pixels within each column that falls into the vertical bin. Tofind the mean intensity value of the kth vertical bin, the sum of the intensityvalues I(i, j) for all the pixels in the rows and columns of the bin is calculatedand divided by the total number of pixels in the bin. I(i, j) is the intensityvalue of the pixel in the ith row and the jth column. This process is repeatedfor each vertical bin across the image width. The horizontal mean followsa similar process but is oriented across the horizontal bins. For the lthhorizontal bin, the intensity values of all pixels in the rows that make up thehorizontal bin are summed and then divided by the total number of pixelsin the bin to obtain the average intensity for that bin.The standard deviation for each vertical bin measures the spread of thegrayscale intensity values around the vertical mean. It is computed by takingthe square root of the variance, which is the average of the squared differencesbetween each pixel’s intensity value and the mean of the vertical bin. Thiscalculation is done for each vertical bin and it indicates the variability ofpixel intensities within each vertical bin. Similarly, the horizontal standarddeviation is calculated for each horizontal bin. It assesses the variability ofpixel intensities within the horizontal bin by computing the square root ofthe average of the squared differences between each pixel’s intensity and thehorizontal mean. More details can be found in the supplementary document,section 3.Fig. 4.2 a and b show the graphs of the mean and standard deviation of thegrayscale intensity values for horizontal and vertical bins. The initial andterminal peaks of the signal can be attributed to droplet boundaries. Forthe horizontal bins, Fig. 4.2a, the shape illustrates Taylor dispersion flowwithin a droplet [101], with larger agglutinates aligned along the center ofthe droplet. Ignoring the initial and terminal peaks in Fig. 4.2b, the graphdistinctly highlights the presence of aggregates, marked by two pronouncedpeaks, amidst an otherwise irregular signal pattern throughout the droplet’sextent. Fig. C.2 and Fig. C.3 [Appendix C] show the mean of gray valuesfor every image of all the droplets for the whole concentration set for verticaland horizontal bins respectively. The pattern observed herein closely mirrorsthe arbitrary units (A.U.) from LBS shown in Fig. 4.1b for 1.6 µg/mL724.2. MATERIALS AND METHODSFigure 4.2: Image of a droplet is split into bins of 1 pixel each in both verticaland horizontal orientation. a) mean and standard deviation of gray valuesof horizontal bins b) mean and standard deviation of gray values for verticalbinsconcentration. It shows a peak after sample number 50, similar to the peakin the vertical bins’ mean and standard deviation.The similarities between the LBS and vertical bin signal allow for imple-menting the same signal processing algorithm to both, as one strategy to734.3. RESULTSanalyze the image dataset. Additional analytical methods are also consid-ered because an image provides more information than the LBS waveformsignal.For this study, a train of 25 droplets for each concentration (0.0128 µg/mLto 5000 µg/mL) will be analyzed by different strategies. The first strategywill calculate various metrics for vertical bins, including the position, height,frequency, and the Full Width at Half Maximum (FWHM) of peaks. The sec-ond strategy will entail the calculation of the mean and standard deviation ofthe grayscale intensity values for vertical bins in each image, followed by theapplication of the signal processing technique previously employed for ana-lyzing the waveforms from LBS, as detailed in our preceding publications andoutlined in section 4.2.1. The third strategy will extend to a comprehensiveanalysis of the entire image. For each strategy, the objective is to determinethreshold values that effectively differentiate between agglutinated and non-agglutinated droplets. Additionally, we will explore categorizing dropletsbased on varying concentrations using these thresholds.4.3 ResultsThe main goal is to differentiate between agglutinated droplets and non-agglutinated droplets. For this, all the concentrations (0.0128 µg/mL to5000 µg/mL) should be above the limit of detection (LOD), which is calcu-lated from the limit of blank (LOB) [102] using the (4.1) and (4.2). More-over, distinguishing between different concentrations would allow differentbiomarkers, normally tested via agglutination, to be quantified.LOB = ¯xblank + 1.645(σblank) (4.1)LOD = LOB + 1.645(σlowestconcentration) (4.2)4.3.1 Peak AnalysisFor each image, a histogram is calculated of the vertical means data. Thenumber of bins in the histogram is determined based on a specified bin size(a bin size of 1 was selected for this study). Various histogram metrics: peakpositions, peak heights, number of peaks, and peak width at half maximum.Mingru Zhou et al. used Full Width at Half Maximum (FWHM) to measurethe particle size [103]. FWHM finds the width of a peak at half the maximumvalue (peak value).744.3. RESULTSAll the FWHM values of different images for each droplet are averaged andplotted for different concentrations in Fig. 4.3. When FWHM values of allimages for every droplet are considered, the spread is too wide to be usefulin the quantification of agglutination. However, if we average FWHM valuesfor all the images of droplets for each concentration then we see the dose-response curve we expect, but inverted. Fig. 4.3b shows the inverted meanFWHM value per concentration (multiplied by -1). The LOD value of -165allows us to detect concentrations from 1.6 µg/mL to 40 µg/mL.Miller et al. [104] quantified small molecule enzyme inhibitors in 140pLdroplets but required 10,000 data points (each data point refers to a droplet)to get a reliable binding curve. Fabrice Gielen et al. [89] achieved thesame level of reliability with 500 times fewer data points but at the cost ofincreasing the droplet volume by 5000-fold (720 nL instead of 140 pL). Oursystem operates with 1000 nL droplets with 25 droplets per concentrationwith approximately 13 to 15 frames captured per droplet. Therefore, foreach concentration, we have approximately 360 data points. Miller’s systemis useful in minimizing reagent consumption. However, when analyzing singlespecies, such as cells, where droplet count dictates consumption, and whentime-dependent data is required, a system like the current one or Gielen’ssystem is preferred. Our system offers accuracy comparable to Gielen’s indroplet generation while being more cost-effective.Fig.C.4 and FlowWithinDroplet.gif [Appendix C] show significant movementwithin a single droplet between different frames. When viewing all thedroplets for a single concentration, the variation in FWHM values is mag-nified, leading to a boxplot with a wider spread in Fig. 4.3a. Motion bluroccurs as agglomerates undergo a three-dimensional (3D) motion under theinfluence of forward-directed streamlines and sedimentation motion [15, 105].Fig. 4.3b shows that averaging all images for each droplet gives results thatcan help distinguish between agglutinated and non-agglutinated droplets forsome concentrations.However, droplets of the same concentration exhibit variations in size, andimages of individual droplets differ slightly from one another in size (by a fewpixels). Therefore, resizing each image to a standardized size is necessary;however, the potential loss of detail and distortion associated with imageresizing could compromise the integrity and accuracy of the data. Therefore,it is advisable to avoid resizing images in the analysis to preserve the integrityand accuracy of the data. Alternatively, all the analysis from this pointforward will be carried out on one image per droplet to avoid variations754.3. RESULTSFigure 4.3: Histogram analysis a) Full Width at Half Maximum (FWHM) forevery droplet for different concentrations b) Mean FWHM of all the dropletimages for different concentrations764.3. RESULTSbetween frames. Once metrics are developed for agglutination quantificationusing one image per droplet, they will be applied across all frames for alldroplets and evaluated. Threshold values for each analysis will be determinedusing one image per droplet (1PerDroplet), and any algorithm developed forthe quantification of agglutination will also be tested on all the images forall droplets (AllPerDroplet) to observe its accuracy.σ¯Vertical =byyb∑k=1σVertical,k (4.3)The variation in vertical bins increases as the agglomerates’ size increaseswith concentration. An average value of the vertical bin’s standard devia-tion is calculated for one image per droplet for the complete concentrationrange as described in Equation (4.3). Fig. 4.4a shows the results where it ispossible to distinguish between non-agglutinated droplets (control) and ag-glutinated droplets above the threshold value 10. However, it is not possibleto differentiate between agglutinated droplets.4.3.2 Adapting Optical Transmittance Methodology forImage Data AnalysisThe waveform files from the LBS are compensated for the drift over time.The average control waveform is calculated to provide the optical charac-teristics of a standard droplet and then subtracted this waveform from theindividual droplet’s waveform. This process is analogous to background sub-traction in the image processing. For the images of a droplet, this processcan be repeated for either the vertical bin’s mean and standard deviationor the mean and standard deviation of grayscale values along a line throughthe middle of the droplet.Fig. S4 and the FlowWithinDroplet.gif (Supplementary Material in Ap-pendix C) show that large agglomerates are aligned along the midline ofthe droplet’s width due to streamlines within droplets. Therefore, the meanand standard deviation of grayscale values along the midline of the droplet’swidth capture variation due to agglomerates. Fig. 4.4b, shows the result forthe concentration range when the standard deviation along the midline of thedroplet’s width is measured. This is done for one image per droplet for allconcentrations. Concentrations above 1.6 µg/mL are detectable whereas the200 µg/mL median is almost at the limit of detection. Two different bandsof concentrations 1.6 µg/mL to 40 µg/mL and 200 µg/mL and 5000 µg/mL774.3. RESULTSFigure 4.4: Three metrics for quantification of agglutination: a) Av-erage of image’s vertical bins standard deviation for one image perdroplet(1PerDroplet) b)The standard deviation along a trace through themidline of the droplet’s width for one image per Droplet (1PerDroplet) c)Mean Standard deviation calculated of one image per droplet for all dropletsof each concentration after image normalization (AllPerDroplet)784.3. RESULTSare distinguishable in Fig. 4.4b. Droplets above the threshold value of 8.24will belong in the band 2 of concentrations (1.6 µg/mL to 40 µg/mL). Onthe other hand droplets below the limit of detection (≈4.75) belong to eitherthe control or the concentration band 1 (0.0128 µg/mL to 0.32 µg/mL)4.3.3 Image Normalization and Variability AnalysisApproaches outlined in the previous sections deal with a section of the imagerather than the whole image. When analyzing the whole image, the image isconverted to a grayscale image and then normalized between 0-1 by dividingby 255, before finding the mean standard deviation of the image. Fig. 4.4cshows the results of the standard deviation of a normalized gray image. Allagglutinated droplets can be distinguished from non-agglutinated as theyare above the LOD line. However, agglutination can only be identified intwo bands 0.0128 µg/mL to 0.32 µg/mL as band 1 and band 2 from 1.6µg/mL onwards. All droplets in the first band are below the threshold valueof 0.055.4.3.4 Multi-Criteria Algorithm for QuantificationAs established in previous sections, a single parameter is insufficient for ac-curately quantifying agglutination levels. Tsung-Feng Wu et al. achievedmaximum accuracy in quantifying agglutination patterns at different bac-terial concentrations by utilizing five identified agglutination features [106].Therefore, the proposed algorithm incorporates various criteria to quantifythe level of agglutination across three bands within a dose curve. The bandsare defined as follows: Band 1 ranges from 0.0128 µg/mL to 0.3 µg/mL,located in the postzone; Band 2 spans 1.6 µg/mL to 40 µg/mL, denotingthe zone of equivalence; and Band 3, representing the prezone, extends from200 µg/mL to 5000 µg/mL. The analysis of the LBS waveforms presented inpreviously published work defines concentration bands similarly [4].In Algorithm 1, each droplet is classified into one of three bands based on thefollowing parameters: the mean standard deviation of vertical bins (Section4.3.1), the standard deviation along the trace line that bisects the dropletimage (Section 4.3.2), and the standard deviation of the normalized image(Section 4.3.3). A threshold value for each parameter is selected, TA, TB, andTC respectively. As previously discussed, the motion causes the data pointsto be more spread out as agglomerates move around within the droplet.Therefore, the algorithm’s success rate in categorizing a droplet into a correctband is carried out on two sets of images. The first set contains only the794.3. RESULTSAlgorithm 1 Analysis of Agglutination1: Calculate: mean of Vertical Bins’ σ(see Section 3.1)2: if σvertical bins > 10 then3: agglutination ← 14: else5: agglutination ← 06: BandOutput ← 07: end if8: Calculate: σ along trace line(see Section 3.2)9: Calculate: mean σ of normalized image(see Section 3.3)10: if agglutination == 1 then11: if σnormalized image > 0.052 andσtrace line > 8.24 then12: BandOutput ← 213: else14: if σnormalized image < 0.055 andσtrace line < 4.75 then15: BandOutput ← 116: else17: BandOutput ← 318: end if19: end if20: end ifinitial image of each droplet (1PerDroplet), whereas the second set includesall the frames captured for each of the droplets (AllPerDroplet).Initial threshold values for the three parameters mentioned are selected basedon Fig. 4.4a to Fig. 4.4c in previous sections. The threshold value for TA ischosen to be 10, while the values for TB and TC are listed in Equation (4.4),where the subscript indicates which band it is used for.Using threshold values in Equation 4.4, algorithm 1 resulted in a successrate of 96.45% when applied to the 1PerDroplet image set. When the samealgorithm is applied to the AllPerDroplet image set, the success rate dropsto 91.59%, most likely due to variations in metrics’ values caused by the804.4. DISCUSSIONmovements of agglomerates within the droplet.[TB1 TC1TB2 TC2]=[4.75 0.0558.24 0.052](4.4)The movement of the agglomerate causes variation in values leading to se-lecting only 1 image per droplet (the first frame captured) in the previoussections. However, each frame captured provides more data for that droplet.A Majority-Based Band Selection (MBBS) Algorithm is employed, wherethe band for a droplet is selected by categorizing each frame into a bandaccording to algorithm 1 and choosing the most frequent band. MBBS al-gorithm achieves 97.50% accuracy for the AllPerDroplet image set over theentire concentration range.4.4 DiscussionThe LBS waveforms were analyzed in previously published work [4] to givea 97.7% success rate in the classification of droplets into two bands (band1: 0.0128 µg/mL to 0.3 µg/mL, band 2: 1.6 µg/mL to 40 µg/mL) usingalgorithm 2, adapted from Fig. 2c of [4]. Although the success rate in theclassification of droplets is higher for LBS waveform analysis than imageanalysis, it is applicable for the lower two bands.On the other hand, the MBBS algorithm is applicable across the whole rangeAlgorithm 2 LBS Waveform Analysis1: Import Waveform2: if Standard Deviation > 450 then3: if Peak Negative then4: if Absolute Value of Peak > 2000 then5: Output Band 1 (Postzone)6: else7: Output Band 3 (Prezone)8: end if9: else10: Output Band 2 (Equivalence)11: end if12: else13: Output Negative14: end if814.4. DISCUSSIONTable 4.1: Success rate comparison between different algorithmsAlgorithmNo. 1 No. 1 MBBS No.2Image Set: 1PerDroplet AllPerDroplet AllPerDroplet LBS [4]Overall 96.46% 91.59% 97.50% 97%Band 0 100.00% 95.59% 100% 100%Band 1 93.33% 86.97% 98.33% 100%Band 2 96.67% 96.53% 96.67% 95%Band 3 98.33% 90.35% 95.00% –of concentrations. Table 4.1 provides a deeper look into each band’s allo-cation for different algorithms (algorithm 1 (for 1PerDroplet and AllPer-Droplet), MBBS, and algorithm 2 for LBS waveforms). LBS (algorithm 2)is better at lower concentrations while the MBBS algorithm is better at thehigher concentration. Therefore, combining both algorithms will provide thebest results over a wide range of concentrations. LBS analysis for lowerconcentrations (Band 0 and 1) and image analysis for higher concentrations(Band 2 and 3) leads to a combined accuracy of 97.9%.The image analysis methodology described in this paper demonstrates thatby developing multiple criteria and employing an algorithm that utilizes thesecriteria, it is possible to broaden the range of concentrations distinguishablefor agglutination. Moreover, by using multiple optical detection methods,such as image analysis and light scattering (LBS), the accuracy can also beincreased.4.4.1 Implication and future worksIntegrating LBS with image analysis broadens the applications of the de-veloped algorithms across a wider concentration range. Table S1 (Table C1in Appendix C) shows a summary of selected biomarkers (C-Reactive Pro-tein (CRP)[107],[108], Myoglobin [109], α -Fetoprotein[110], and ProstateSpecific Antigen [111]) with their respective ranges for normal concentrationand the cutoff values for diagnostic purposes. Table C2 shows a summaryof selected biomarkers in Table S1 but in nmol/L, and despite the clinicalranges in Table C2 being on the lower end of the tested spectrum, additionalresearch into specific biomarkers is essential. Changes in the bead-to-analyteratio can modify the detection range. It is important to acknowledge that the824.5. SUMMARYvariations in binding affinities and the impact of spatial hindrance requireadjustments to the assay parameters. Moreover, Fig. S4 and FlowWithin-Droplet.gif (Supplementary Material in C demonstrate agglomerate move-ments within a droplet, warranting a detailed analysis to understand theflow dynamics.Currently, the droplet trains are generated from a premixture of analytes,which starts the agglutination reaction, in the well plate. As the agglomerategrows over time, depending on the concentration, it settles more quickly inthe well. Consequently, the initial droplets generated would not containthe same amount of agglomerate as those produced later. This could be thereason for a spread in different analyses within a train of droplets for a singleconcentration. A droplet merger chip in line with the system, will allow forthe exact timing of the start of the reaction and eliminate this as the sourceof error.The emergence of AI enables machine learning algorithms to classify dropletimages by concentration, potentially offering more precise outcomes thanconcentration bands.4.5 SummaryThis chapter focuses on leveraging the ADIM system (Version A) combinedwith image analysis and low-cost optical transmittance measurements for de-tecting and quantifying agglutination. Combining image analysis with low-cost light transmittance sensor readings widens the detectable concentrationrange for agglutination. The methodologies developed in this study demon-strate the ability to differentiate between agglutinated and non-agglutinateddroplets with 100% confidence. The use of histogram analysis, optical trans-mittance methodology adaptation for image data, and image normalizationhas shown promising results in distinguishing between various concentrationbands of agglutination.Through quantification of a model streptavidin-biotin assay, this article ful-fills the following objective of the research:3. to develop a high throughput low-cost microfluidic lab-in-tubing sys-tem capable of detecting agglutination of biotinylated Bovine SerumAlbumin (BSA) in 0.0128 µg/mL to 5000 µg/mL concentration rangewith Streptavidin beads83Chapter 5Automated Dynamic InletMicrofluidics (ADIM) System:Version B5.1 OverviewIn response to the need for an accessible, cost-effective microfluidic system,a Prusa MINI+ 3D printer is modified with a 3D printed nozzle replace-ment module, converting it into an ADIM system, Version A in the previouschapters. This study achieves the same goal but with a different mechanism,a bottom-up approach versus the previous top-down approach, and a min-imum achievable droplet size of 12 nL compared to 0.75 µL for version A.Moreover, the overall bill of materials is reduced from $2500 for version Ato $1700 USD for version B. Similar to the DropLab and Mitos Dropix, ourplatform also creates droplets by moving the inlet from a position below theanalyte upward and then back down, but it does so more affordably whilemaintaining a similar level of accuracy in droplet formation.In our previous iteration of the ADIM droplet generation platform [1], thePrusa Mini+ is modified to generate droplets from a top-down mechanism,similar to the SSL system. However, the system required extensive math-ematical modeling and calibration procedures to generate mono-dispersedroplet libraries. The latest iteration of our developed system simplifiesthe setup and achieves higher accuracy at a lower cost. A comparison of allthe similar works is summarized in Table 5.1.This chapter has been published as an article titled, “Automated Dy-namic Inlet Microfluidics System: Cost-effective Biaxial NanoliterDroplet on Demand Generation Platform and Its Application inAgglutination Assays", in the Lab on a Chip journal, with me as the firstauthor. [3]. Supplementary material to the article is given in Appendix D.845.2. METHODS AND MATERIALS5.2 Methods and MaterialsThe developed system is shown in Fig. 5.1, with the modified Prusa Mini+,which has the nozzle head replaced with a 3D-printed nozzle with a J bendat the bottom to allow the tubing to be inserted through and connected tothe syringe needle tip (gauge 20Ga). A 0.7mm orifice is drilled at the bottomof the Eppendorf tube to move the needle tip from the oil into the tube. Theneedle tip is chosen to allow for smooth movement in and out of the tubewithout touching it. The print head is removed from the X-axis carriagebut remains electrically connected to the main circuit board of the printer.This allows the 3D printer to be used without requiring any changes to itsfirmware.The well is fabricated using 3D-printed base and acrylic sheets for walls.The well is filled with silicone oil (XIAMETER PMX-200 100 cSt, The DowChemical Company), and a 3D printed rack is placed on the top of the wellto house Eppendorf tubes (1.5 mL Eppendorf tube) with analytes. The 3Dprinter moves in the Z axis to move the needle tip from its resting positionin the oil (continuous phase) into the analyte inside the Eppendorf tube(disperse phase) and then back into the oil to create droplets.For the system to generate droplets from different analytes the 3D-printedrack houses multiple Eppendorf tubes. The number of different analytesavailable depends on the bed size of the printer and the oil well constructed.For the proof of concept in this study, an oil well measuring 85 mm by 55 mmand 64 mm in height is constructed, alongside a 3D-printed rack designed toaccommodate five distinct analytes.The tubing passes through the J bend of the 3D-printed nozzle, then througha viewing window under a microscope with a Canon 5D Mark IV camera forevaluation. After the viewing window, the tubing transfers the dropletsinto the syringe pump (New Era Systems NE-1000), which applies constantnegative pressure on the system. The droplets collected in the syringe aredisposed of after each run. The viewing window is created by casing thetubing in PDMS. The tubing type and length can vary depending on theapplication, while microfluidic chips and sensors can be connected in linewith the mentioned setup.The 3D printer can be operated in three modes, primary mode uses a G-codefile via USB disk to run the 3D printer and generate a train of droplets. TheG-code is generated using MATLAB script with the start and end point ofthe needle tip, and the flow rate as input parameters to calculate the delay855.2.METHODSANDMATERIALSTable 5.1: Comparison between this study and other worksMitos Dropix[89] Helena Zec[88] Elveflow Previous System[1] This StudySmallest Droplet 1 nL 3.8 nL 0.5 µL 1 µL 12nLCoefficient of Variance 4% Unknown < 3% 3% 2%Number of Analytes 24 96 2 96+ 15Throughput (per hour) 1000 6000 ∗ 3.2× 106 6900 6900Microfluidic chips use Yes No Yes Yes YesSetup Difficulty Low High Medium Medium LowClean room Required No Yes No No NoDroplet Library Storage Yes No No Yes YesCost (USD) ≈$15000 Unknown ≈$9000 ≈2500 ≈1700Figure 5.1: The system diagram: setup of the system with modified Prusa Mini (3D printer) and the modified3D-printed nozzle with J bend (Created with BioRender.com)865.2. METHODS AND MATERIALSrequired in the analyte to generate the desired volume of droplets and gen-erate the G-code for the 3D printer. The second mode to use the systemis through a host software such as Printrun to run the generated G-code.Thirdly, a MATLAB script runs the printer in real time by sending G-codecommands. This allows sensors, e.g. flow sensors, to be incorporated into thesystem, providing instantaneous flow rate values for the script to calculatethe required delay.5.2.1 CalibrationThe first step is to evaluate the 3D printer and its movements. The movementin the Z axis is the most important as it is responsible for droplet generation.The maximum feedrate for the three axes are X=Y=12000 mm/min, and Z= 720 mm/min. These speeds have to be tested. Moreover, the main timethe syringe needle spends in the analyte is the delay between moving up intothe analyte and moving down out of the analyte. The G4 command in theG-code allows the user to enter the delay in milliseconds (P) or seconds (S).For instance, G4 P250 gives a delay of 250ms between commands while G4S250 gives a delay of 250 seconds. Both axes’ movements and the delay canbe evaluated by analyzing the movement of the syringe needle tip capturedin a video.Once the system is set up like in Fig. 5.1, the system should be calibratedand a few important set-points values have to be evaluated before the systemis used. The first step is to align the syringe needle to the 0.7 mm orificeof the Eppendorf tube. Fig. 5.2 shows all the important set-points to beevaluated for single-analyte and multi-analyte schemes. In the single analytescheme, an initial point is selected to be the origin point for the X, Y, andZ axis (X00, Y00, Z00). ZAnalyte is the point at which the syringe needle tiptouches the analyte in the Eppendorf tube. The difference between the originand ZAnalyte is denoted Updistance. It can be as small as 1mm but for thisstudy Updistance is set at 2.5mm. The Updistance will affect the throughputof the system. The needle must move beyond ZAnalyte and transverse intothe analyte for better droplet generation. Distances between 0.5 mm to 2mm were tested and we found that the best practice is to have ZA−high at1.5 mm. The MATLAB script moves the syringe needle tip from oil (origin)up into the analyte to ZA−high taking tup ms, wait for the calculated delay(tdelay) and then take tdown ms to bring the tip back down into the oil (origin)to generate a droplet. Due to the negative pressure, the syringe needle pullsthe oil and analyte interface down when descending from the analyte into875.2. METHODS AND MATERIALSthe oil. This means ZAnalyte differs for the needle ascending and descending.If this interface hysteresis is extensive, it can lead to analyte leakage fromthe orifice.VDroplet = (tup + tdelay + tdown + tsystem)× Flowrate (5.1)Equation 5.1 presents the formula for determining the volume of the pro-duced droplet. The difference between ZAnalyte and ZA−high remains fixedfor both ascending and descending motions (1.5 mm respectively) withinthe analyte. This duration is derivable through the Z-axis feedrate (720mm/min), resulting in a calculated time of 250 ms (tup + tdown). The pa-rameter tsystem encompasses all the dynamics of the system; such as thebuilt-in delay of the 3D printer or interface hysteresis. Two other variablesare tdelay and flow rate. Given a fixed flow rate, the MATLAB script can eas-ily calculate the required tdelay to generate the droplet of the desired volumeand generate G-code.In a multi-analyte scheme, the movements of the 3D printer are a bit morecomplicated than a single-analyte scheme. A low point in the Z axis isselected for each Eppendorf tube, ZA−Low and ZB−Low, which is zero. TheFigure 5.2: Schematic to show movements of the 3D printer to gen-erate droplets a) one analyte scheme with setpoints (Z00, ZAnalyte,ZA−High) b) Multi analyte scheme movement with set points(X00, ZA−Low,X11, ZB−Low, ZA−High, ZB−High) for the 3D-printed nozzle (Created withBioRender.com)885.2. METHODS AND MATERIALS3D printed rack is designed to house each tube 11.5 mm apart. After thesyringe needle is aligned with the first Eppendorf tube and X00, ZA−low isset, the next Eppendorf is at X = X00 + 11.5mm. Due to variations inthe manufacturing of the tubes and the placement of the drill hole at thebottom, the user manually aligns each tube, but it is usually a few 0.1 mmsteps off. Once the coordinates for all the tubes are aligned and recorded inthe program, the assay can be run multiple times.The high-point of each Eppendorf tube has to be selected 1.5mm abovethe ZAnalyte. The leveling of the printer’s bed, the well, and the rack isvery important. Any imbalance might result in the high points of the twoEppendorf tubes, (ZA−High and ZB−High), being different. As long as thehigh point is 1.5 mm above the ZAnalyte, the size of droplets will remain thesame, however, the gap between the two consecutive droplets in a train willdiffer slightly.The 3D printer moves the syringe needle tip from X00, ZA−Low to X00,ZA−High,rests for the calculated delay, and then comes back to X00, ZA−Low, generat-ing a droplet of analyte A, before moving to X11,ZB−Low. Then the printermoves up into the analyte B, to ZB−High, rests, and returns to ZB−Low.Hence a droplet of analyte A is generated followed by a droplet from ana-lyte B. The delay between the movement from X00, ZA−Low to X11,ZB−Lowdetermines the gap between the droplets.Use of Equation 5.1, without factoring in tsystem, a target of 1 µL at aflow rate of 35 µL/min leads to droplets averaging around 0.7 µL, althoughuniformly sized. To achieve the target volume, tdelay can be adjusted easily.Moreover, our previous droplet generation system’s calibration methodologyallows for an empirical relationship between tsystem and flow rate, whichrequires generating calibration droplets at different flow rates for systemdynamics analysis[1]. For systems integrated with microfluidic componentsor sensors, in line with the system in Fig. 5.1, calculating tsystem is crucialfor precision.The syringe pump exhibits a lag in reaching the designated flow rate, and thelead screw introduces further fluctuations. For shorter tubing length (≈1m)and short droplet train, this can be managed by turning the pump on 5minutes before conducting experiments to ensure a stable flow. For longeror narrower tubing, the flow rate will differ from the syringe pump settings.The addition of microfluidic chips and sensors in line with the system inFig. 5.1 will also affect the flow rate. For such cases, the methodology forcalibration presented in section 2.2 of the previous iteration[1] of the system895.3. RESULTShas to be followed to give results with high accuracy.5.2.2 MethodologyThe developed system is used to generate trains of droplets in different con-figurations with dyed water to show the functionality and capabilities of thesystem as a proof of concept. The uniformity of the droplet series producedis assessed by calculating the coefficient of variation (CV%), which is thestandard deviation of droplet volumes divided by their mean volume. Thisserves as an indicator of their uniformity. Consequently, a model proteinassay will be implemented using the developed system to show its utility inimmunoassays.Agglutination is a biological process where particles clump together, oftenin response to a probe-target reaction (e.g. antigen-antibody). In medicaldiagnostics, this method is used to detect the presence of target molecules inbody fluids by observing the visible aggregation of particles, such as red bloodcells or bacteria, when specific probes like antibodies are present. A commonmethod in biochemical experiments to study protein interactions utilizes thestrong affinity between biotin and Streptavidin to capture, detect, quantify,or purify specific proteins[112].The developed system is used to generate 10 droplets from 30 µl analyte(Vector Laboratories 10 mg VECTB2007 biotinylated bovine serum albu-min (BSA) and Dynabeads M-270 Streptavidin, Invitrogen) in the con-centration range of 2.5 µg/mL to 40 µg/mL. The control consists of Dul-becco’s Phosphate Buffered Saline (Sigma-Aldrich) supplemented with 1%non-biotinylated BSA (Sigma-Aldrich) and 1% Tween (Sigma-Aldrich). Thenon-biotinylated BSA at this concentration ensures that the detected signalis not attributed to non-specific binding. The evaluation of agglutination willdemonstrate the developed platform’s capability to generate droplet librariesfor immunoassay applications.5.3 Results5.3.1 Calibration3D printer’s movements are recorded in a video (1280x720p, 120 fps) to beanalyzed for movements in X, Y, and Z axis by focusing on the syringeneedle tip. The delay commands are scripted for incremental delays fromno delay to 500ms. The results of the analysis are presented in Table 5.2.905.3. RESULTSThe delay commands are passed to the 3D printer through a G-Code filevia USB or Pronterface. In our previous work, MATLAB communicatedthe delay commands to the printer. Compared to previously reported dif-ferences between programmed and actual delays, the actual delay is longerin both cases (G-code file and Pronterface). The largest difference betweenprogrammed and actual delay can be seen for the Pronterface. However, theutility of Pronterface and G-code file is that it does not overload the cache ofthe printer which can happen with MATLAB. When the cache is overloadedsome commands fail to execute. On the other hand, MATLAB script canincorporate the instantaneous reading of sensors (e.g. flow sensor) thereforeit is more robust to changes in the system. The video analysis for the Z-axismovements showed that it moves with 720 mm/min speed.The Eppendorf tube is positioned only a few millimeters into the oil, whichcauses the oil to enter the Eppendorf tube via the bottom orifice. A minimumvolume of the analyte has to be maintained in the tube, depending on thetube’s position, to ensure that the analyte weight pushes the silicone oil out.For this study, the minimum analyte volume limit is 30 µL. If the analytevolume falls below this limit the oil starts to fill the Eppendorf tube movingthe interface and leading to an error in droplet volume, as shown in Fig. D.1[Appendix D].Table 5.2: Results of 3D printer movement analysis for different programmeddelay executed different methodsProgrammed Actual Actual Actual3D Printer Delay (ms) Delay (ms) Delay (ms) Delay (ms)G-Command [Chapter 3][1] Pronterface G-code fileNo Delay 0 20.0 8.3 8.3G4 P0 0 – 125.0 116.6G4 P1 1 106.6 108.0 116.6G4 P10 10 113.4 141.6 133.33G4 P50 50 153.4 158.3 166.67G4 P100 100 213.4 225.0 225.0G4 P150 150 260.0 275.0 266.67G4 P250 250 300.0 375.0 375.0G4 P500 500 608.0 616.67.0 625.0915.3. RESULTS5.3.2 Train of Droplets with Constant Volume of SingleAnalyteThe sizes of the droplets in a train are characterized using the Droplet Mor-phometry and Velocimetry (DMV) MATLAB plugin[96]. The system in Fig.5.1 is set up using 70 cm of Tygon tubing (0.508 mm I.D.) with a viewingwindow at around 30 cm from the point of droplet generation. The syringepump is used with a 3 mL B.D syringe. Fig. 5.3 shows the result of gener-ating 1 µL droplets at 35 µL/min flow rate in 3 separate runs with the errorbars representing absolute error from the mean. The mean CV% achievedfor trains of 20 droplets of 1 µL volume is 6.4%. The minimum volume pos-sible at 35 µL/min flow rate is 0.3 µL with CV% of 6.3% when the systemis calibrated as described in section 5.2.1, with no tdelay. Note that if ex-periments are conducted using the G4 P0 command it introduces a 125 msdelay between the up and down commands instead of 8.3 ms, as per Table5.2. The extra ≈100 ms leads the minimum volume achieved at 35 µL/minFigure 5.3: Train of 20 droplets generated at 35 µL/min with a target volumeof 1 µL and the minimum possible volume that can be generated at this flowrate925.3. RESULTSto be 0.4 µL rather than 0.3 µL, as shown in Fig. D.2 [Appendix D].The first droplet seems to vary in volume the most compared to the restof the train. In all the experiments in this study, the last droplet does notshow the variance like the first droplet. The reason for variation in the firstdroplet might be due to changes to hydrodynamic resistances as dropletsare added to the tubing. On the other hand, the oil-analyte interface isstable for the first droplet while subsequent droplets face less variation dueto insufficient time for the interface to fully stabilize between droplets. SSLsystem by Helen Zec [88] also shows variation in the first and last dropletdue to valve actuation sequence. The percentage difference in volume fromthe mean volume is just above 5% which would make the train of dropletsgenerated mono-disperse. Using this scheme a train of 100, 1 µL droplets isgenerated with a CV of 1.76%, and a mean volume of 0.98 µL. The resultantvolumes of the droplet train and flow rate are shown in Fig. D.3 [AppendixD], illustrating the high accuracy achievable with this platform.Low Volume at Low Flow RateThe system in Fig. 5.1 is set up with 70 cm of PTFE tubing (0.254 mmI.D.) placing the viewing window at around 30 cm from the point of dropletgeneration. The syringe pump is set with a 1 mL syringe and calibrated togive below 10 µL/min flow rate. Fig. 5.4 shows the result of generating atrain of 20 droplets of 30 nL volume each at different flow rates. The meanCV% achieved for trains of 20 droplets of 30 nL volume is 5.1% for all flowrates. The highest CV% is observed for 4.5 µL/min which is 9.2%, whichis due to the first droplet, the CV% for the rest of the train of droplet fallsto 3.8%. The first droplet shows the greatest variation again. The lowestvolume attempted is 12 nL at 1.5 µL/min where the variation (CV% of 8%)is achieved in droplet volume and it correlates with the cyclic movements ofthe lead screw of the syringe pump, shown in Fig. D.4 [Appendix D]. Theavailable syringe pump does not provide stable flow at lower flow rates, thusonly nanoliter volumes are possible. However, if a pressure source providesa stable negative pressure, there is no reason to believe the system cannotachieve high consistency at picoliter droplet volumes.Tube FoulingPTFE, Tygon, and Silastic Tubing are the main tubing used in microfluidics.Repeated use of Tygon tubing showed signs of degradation causing the wa-ter droplets to adhere to the walls, leading to satellite droplets latching to935.3. RESULTSFigure 5.4: Train of 20 droplets generated with the target volume of 30 nLat different flowthe walls and droplet breakage. Therefore, the tubing has to be replacedperiodically. PTFE tubing does not show degradation after use, however,the inner vortices of the droplet are limited leading to limited mixing. Inagglutination assays in Lab-in-Tubing the mixing inside the droplet plays acrucial role in incubation. Limited mixing leads to longer incubation time.Fig. D.5 in the supplementary material [Appendix D] shows this and pro-vides more detail with an example. This means that Tygon tubing capturesmolecules from agglutination assays, causing cross-contamination like silas-tic tubing in previous studies[17]. Silastic tubing also degrades and causespressure drop after repeated use. However, both Tygon and silastic tubingwalls provide enough friction resulting in inner vortices within the droplet.The PTFE tubing was chosen to store droplet libraries in droplet cartridgesin our previous research due to its high resistance to fouling [1]. Dropletsstored in PTFE do not break over time. The trade-off is between degradationover time or limited mixing within when choosing between silastic, Tygon,and PTFE. The agglutination assays are carried out in ascending order of945.3. RESULTSconcentration to avoid contamination of the previous assay on the current.5.3.3 Train of Droplets with Variable Volume of SingleAnalyteThe utility of the modified 3D printer is its ability to be programmed. Thesystem can generate a train of droplets where each droplet in a single trainhas a different volume. This is useful in particular with concentration gradi-ent generation via droplet merging [56]. Fig. 5.5 shows the results for a trainof droplets at 35 µL/min where the sequence of 0.3 µL (minimum), 0.5 µL,0.7 µL, and 0.8 µL is repeated 5 times. Fig. 5.5 a shows the mean value ofeach target volume and the error bars represent the mean absolute error formultiple measurements (n = 3). A Mean CV% achieved is 5.75%. Fig. 5.5b shows the boxplot illustrating the variation for each target volume in thetrain of droplets. The highest CV% of 10.7% is observed for a target volumeof 0.3 µL. Due to the CV% achievable and the limitation of the 3D printerchosen, the minimum volume difference discernible is 0.1 µL with the delaychange of 100 ms. Fig. D.6 in section 4 of the supplementary provides moredetail [Appendix D].5.3.4 Train of Droplets with Constant Volume of MultipleAnalytesThe 3D printer’s adaptability stems from its programmable movements alongthe X-Y and Z axes. This system employs Z-axis motion to create sequencesof droplets from a well, with volumes that can be either uniform or varied.Meanwhile, the X-Y axis movement is used to select an Eppendorf tube froma 3D-printed rack atop the well to produce droplet sequences. Currently,the rack can house 5 Eppendorf tubes, with two being used as proof ofconcept in this study. The setup can be expanded to include more Eppendorftubes inline within a larger oil bath, depending on the print bed size of the3D printer. Each Eppendorf tube is set roughly 11 mm apart (from itscenter point) which means that Prusa Mini+ can hold around 15 Eppendorftubes with a bed size of 180 mm by 180 mm. A Fused deposition modeling(FDM) printer with a bigger print bed can hold more Eppendorf tubes. The3D printer movements required to generate droplets from multi-analytes aredescribed in section 5.2.1 and shown in Fig. 5.2.Fig. 5.6 shows a train of 20 droplets where the system alternates betweentwo Eppendorf tubes of the analyte. Fig. 5.6 b is a picture of the train ofdroplets from two (2) different analytes each with constant volume, where955.3. RESULTSFigure 5.5: a) A train of droplets with variable volume, sequence of 0.3 µL,0.5 µL, 0.6 µL, 0.7 µL, and 0.8 µL is repeated five (5) times b) Boxplotshows variation for each target volume in the train of dropletthe tubing is placed in a 3D printed holder. The inset droplet image in Fig.5.6 shows daughter droplets. At a higher flow rate, there is an increased shearforce on the analyte-oil interface leading to instability resulting in daughterdroplets. At a lower flow rate (e.g., 10µL/min) daughter droplets are notformed.965.3. RESULTSFigure 5.6: A train of droplets with constant volume from two (2) differentanalytes a) droplet train generated at 35 µL/min flow rate with a targetvolume of 0.5 µL consisting of two different analytes (A and B) b) image ofdroplet train with multiple analytes placed in a 3D-printed holderThe 3D printed Eppendorf tube places the Eppendorf tube 11 mm apartin the X axis. The distance from the low point to the high point is 4 mmfor each Eppendorf tube in the Z axis. The maximum speed of Prusa Mini+ is 12mm/s for the Z-axis, while for the X-axis, it is 200mm/s. Accord-ingly, this means it takes 1.33 seconds for the Z-axis and 0.055 secondsfor X-axis movements. The minimum time between the generation of two(2) droplets from adjacent Eppendorf tubes is 1.385 seconds. The intervalbetween droplet formations will extend if the Eppendorf tubes are spacedfurther apart. Nonetheless, a MATLAB script is capable of determiningthe interval for droplets originating from the most distant Eppendorf tubes975.3. RESULTSand can introduce delays for those nearer to each other, ensuring a uniformdroplet sequence.Droplet MergingThe timing between two consecutive droplets is very important. When gener-ating trains from two different analytes, it can be observed that the dropletswould merge if the interval between them is kept too short (the minimumtime of 1.38 seconds). Droplet merging is very useful in microfluidics, asthe start time of two analyte’s mixing is known exactly, therefore, the timefor incubation can be calculated with high accuracy. However, the dropletmerging in this system is unpredictable, making it more of a liability thanan asset. Fig. D.7 [Appendix D] shows multiple experiments when a train oftwenty (20) droplets of 0.3 µL merged into ten (10) droplets of 0.6 µL vol-ume with 100% accuracy. Moreover, DropletMerging.mp4 (supplementarymaterial) shows two droplets merging and two different dyes allowing us topeek at the inner flow patterns of the droplet.5.3.5 Straptividin Beads TestsThe single analyte scheme, section 5.2.1, is used to generate 10 dropletsof a premixed solution consisting of Streptavidin microbeads and biotiny-lated bovine serum albumin (bBSA). The study utilizes Dynabeads M-270polystyrene microbeads, supplied by Thermo Scientific Pierce, with eachbead measuring approximately 2.8 µm in diameter. For the experiments,these beads are suspended in a phosphate-buffered saline solution at varyingconcentrations, specifically at 2.5, 5, 10, 20, and 40 micrograms per milliliter(µg/mL). Non-biotinylated BSA in PBS buffer is used as the negative con-trol. Droplet trains are captured by Canon 5D Mark IV mounted on a Zeissmicroscope, at 120fps, 1280x720px in a bright field configuration.Agglutination tests usually yield semi-quantitative results, indicating whetheragglutination has occurred or not in a particular range of concentrations, akinto assessing C-reactive protein (CRP) levels where values above 3.0 mg/mLsuggest a higher risk for cardiovascular issues.Image analysis is a critical tool for quantifying agglutination processes invarious fields such as virology [113, 114], blood typing [32, 36–39], and mi-crobiology [115, 116]. Ana Ferraz et al. underscore the application of imageprocessing techniques for detecting agglutination reactions [47]. A dominantpart of the literature focuses on hemagglutination [37, 39] or fluorescently985.3. RESULTS[34, 35, 91] labeled biomarkers. Yi Luo et. al. used image processing toquantify C-reactive Protein (CRP) in a concentration range of 10 to 500µg/mL with the help of deep learning models[117]. Morphological analysisalso looked at the particle area of agglutination assay. Huet et. al. usedimage analysis to monitor and quantify hemagglutination using correlationand variance-based indicators for blood typing[37]. More details on imageanalysis can be found in our other research work [2] [Chapter 4 and forbackground see Chapter 2].Subsequent sections will describe the image processing steps taken beforequantifying the level of agglutination by two methods, the first is based onvariance of gray scale values and the second method measures particle sizewithin droplets.Image processingFig. 5.7 shows the steps taken to prepare the images of droplets (N=19,194)and their quantification. The first step is to isolate the video frame con-taining the droplet. This is done using MATLAB script which checks thefront and end region of the frame and it should be similar to the backgroundimage (the first frame). The middle region’s standard deviation must differfrom other regions (front and end). The script allows the user to select aregion of interest (ROI) for the droplet detection to reduce the amount ofcomputation. If the droplet is detected in a frame then the frame is sub-tracted from the background image and only the subtracted ROI is stored.A second MATLAB script carries out this function, binarizing each imageusing MATLAB’s auto-threshold function and applying the sharpness filterdescribed in Equation 5.2 to create sharpened images, Binarized and sharp-ened images are stored in separate folders. The binarized image is used forparticle counting (Fig. 5.7b), while the sharpened image is used to calculatevariance-based indicator for agglutination (Fig. 5.7c).Sharpness filter = 0 −1 0−1 9 −10 −1 0 (5.2)995.3.RESULTSFigure 5.7: a)Image processing steps: 1. frame is extracted from the video of droplet train 2. The backgroundis subtracted 3. The image is cropped around the droplet and converted to a greyscale 4. Greyscale image isbinarized by auto threshold function 5. Greyscale image is passed through sharpness filter b) Boxplot of meanparticle size for 2.5 µg/mL, 5 µg/mL, 10 µg/mL, 20 µg/mL, and 40 µg/mL using thresholded image c) VarianceBased Indicator (VBI) plotted for different concentrations based on the sharpened image. The VBI values arescaled to 1/100000 times1005.3. RESULTSParticle Size MeasurementIn agglutination, particles clump together which should be reflected in par-ticle size measurements. For this purpose, greyscale images of droplets arebinarized using an auto threshold function, and then the particle area is cal-culated using regionprops function of MATLAB. If the border of the dropletis not removed, it will be detected as the largest particle after the imageis through the regionprops function. It will be a few folds bigger than thesecond particle, it can be removed easily. However, it can be connected tothe agglomerates near the border of the droplet, which will lead to an errorwhen the largest particle is removed. Removing the border before particlesize calculation resolves this issue.The mean particle size of each droplet is calculated and plotted in Fig. 5.7b.The droplet of control has a uniform distribution of latex beads due to whicha major part of the droplet is detected as a large particle. The differencebetween agglutinated and non-agglutinated is distinct.Variance Based IndicatorThe clumping of beads together changes the pixels’ gray values, causingthe variance of some regions to increase compared to other regions. Thischange within a droplet will be minimal in the control as the beads areequally dispersed. Huet, Cubizolles, and Buhot developed a variance-basedagglutination indicator for blood typing in a passive microfluidic chip whichachieved a 100% success rate in discriminating between positive and nega-tive results[37]. The chip had no flow and the agglutination in the channel isstatic. This study quantifies agglutination in a dual-phase microfluidic setupwhere the droplet is traveling at a 35 µL/min flow rate (at 2.87 mm/s linearvelocity). Variance Based Indicator (VBI) is adopted and applied on sharp-ened images of droplets. The whole region of the droplet is split into a regionof interest (ROI) array (3 by 16). Equation 5.3 shows how the variance ofa small window within each ROI is calculated where the parameter L is thenumber of lines in the ROI taken together. After sorting Vj in descendingorder, the mean of the top X values for each ROI is taken according to equa-tion 5.4 to calculate the variance of the ROI. Once a variance is calculatedfor all ROIs, its mean value is calculated according to equation 5.5.[This paragraph has been added for Dissertation] The variance-basedindicator (VBI), the values for L and x are found using a systematicallyapproach to evaluate the system’s performance across all possible parameter1015.4. DISCUSSIONconfigurations within a predefined range. The results are chosen for thevalues of L and x that show the best results. For this study, the value of L= 7, and X= 10 is used.Vj =1Lk=0∑L−1(ROI(j + k, :) + µROIj )2 (5.3)¯VROI =1Xk=1∑XVjk (5.4)V BI =1Ni=1∑N¯VROIi (5.5)Fig. 5.7c shows the results of VBI for different concentrations. All imagesof each droplet for all concentrations are included in this evaluation. Thisis partly why there is so much variation in each concentration, as the ag-glomerate’s movements within the droplet are influenced by the droplet’sinner vortices. The limit of detection is calculated as per the standard[118].Control A is the train of droplets run before running any positive assay,consequently, assays of increasing concentration are generated. Control B isthe control assay run after the highest concentration (40 µg/mL). The fourconcentrations are clearly above the level of detection (LOD), allowing forqualitative results for agglutinated and non-agglutinated.5.4 DiscussionThe variance-based indicator and particle size measurement can distinguishbetween agglutinated and non-agglutinated droplets for the complete con-centration range. In our previous experiments[4, 5], the concentration rangeof 0.0128 µg/mL to 5000 µg/mL of biotinylated BSA was investigated us-ing an off-the-shelf line break sensor (LBS) and deep learning models. Bothshow that the zone of equivalence for Streptavidin and bBSA is between 0.32µg/mL and 200 µg/mL. It is difficult to distinguish between them since allthe concentrations fall within the zone of equivalence. On the other hand, theexperiments show the application of the developed system in agglutinationassays.1025.4. DISCUSSIONThis study is calibrated for Prusa Mini+ however the user can use any 3Dprinter to implement the developed ADIM system. If the user uses a different3D printer or generates droplet volumes beyond the volumes outlined inthe manuscript, Section 6.3.2 in the supplementary material [appendix D]outlines the methodology for this.5.4.1 Application of Droplet Generation PlatformDroplet library generators are advancing experimental methodologies in dropletmicrofluidics toward greater efficiency, affordability, and precision across di-verse research areas. Zhang et al.’s study demonstrates this by using manualpipetting from a well plate to carry out oligonucleotide amplification assayswith a 192-sequence library[100]. A droplet library generator can signif-icantly enhance the throughput of their system, showcasing the utility ofdroplet library generators.Moreover, Wen-Bin Du et al. showcase the droplet library generator’s ap-plication through the creation of DropLab[87]. The application of DropLabis highlighted in a β-galactosidase inhibition assay, producing 2 nL dropletswith varied concentrations of enzyme, substrate, and inhibitor to establishan inhibition curve and determine the IC50 value of the inhibitor. Addition-ally, the system is employed to assess and optimize crystallization conditionsfor lysozyme, a standard protein, using an array of 50, 12 nL droplets.In the previous iteration of the system, custom connectors were designed tocreate a droplet cartridge, a self-contained droplet library[1]. The connec-tors are crafted with complementary male and female luer locks, ensuringprecise alignment during the assembly of the modules. The tubing passesthrough the connector openings, where it is meticulously aligned and se-curely fastened together, preventing any direct contact between the fluidsand the 3D-printed surface. Both connector ends feature a groove encir-cling the opening, designed to accommodate a silicone O-ring (sourced fromamazon.ca) with a 3 mm inner diameter and a 2 mm thickness, ensuring asnug fit. Upon assembling the connectors, the tubing ends are pressed to-gether, compressing the O-ring to create a tight seal that precludes any fluidleakage. Additionally, Teflon tape is applied to the male luer lock’s groovesto enhance the sealing efficiency. The connector orifice can be adjusted toaccommodate various tubing sizes, offering versatility in system setup.The system developed can theoretically utilize connectors to form a dropletcartridge that houses a library of droplets, as illustrated in Fig. 5.8. In the1035.4. DISCUSSIONFigure 5.8: Schematic of how a droplet cartridge can be installed in thesystem to generate droplet libraries (Created with BioRender.com)previous iteration of the system, the syringe needle had to descend throughthe oil into the analyte from above in a 96-well plate, which interfered withthe refilling process. The current design facilitates movement from below theEppendorf tube up into the analyte, allowing the user to refill the Eppendorftube from its open top, thereby enabling extensive droplet trains.The developed system is versatile in its setup as sensors and microfluidicchips can be incorporated in place of the droplet cartridge in Fig. 5.8 toexpand the system’s application.5.4.2 Analyte SelectionIf the density of the analyte is comparable to or higher than the siliconeoil (0.97 g/cm3) used in this study, the analyte can leak from the orifice inthe Eppendorf tube and sink to the bottom of the well. The water densityis comparable to the density of the silicone oil but the surface tension andcapillary forces keep the water in the Eppendorf tube. The issues that canarise with different analyte selections are discussed in detail in section 7 ofthe supplementary material. If the analyte’s density is around or above 0.97g/cm3, then the user can select oil like Fluorinert™ FC-40 [85, 88, 89, 91, 118–120] from Sigma-Aldrich with a density of 1.870 g/cm3 or HFE-7500 oil (3M)[65, 72, 121–125] with 1.614g/cm3 density. Oil with higher density would1045.4. DISCUSSIONresult in the analyte in the Eppendorf tube not leaking through its orificeat the bottom. Both oils are extensively used in literature related to dropletmicrofluidics.5.4.3 Impact of Oil and Analyte Viscosity on SystemPerformance[Following discussion has been added for the Dissertation] The vis-cosity of the analyte or oil will have a significant effect on the operation ofthe system. A range of viscosities (1, 10, 50, and 100 cSt) for the siliconeoil was tested before selecting 100 cSt for this study. The syringe pump ex-hibits lag in reaching the designated flow rate, as mentioned in section 5.2.1Calibration. The syringe pump took longer to achieve a stable flow, and thestable flow rate was lower than the designated flow rate with a more viscousoil. In continuous droplet generation, the increase in viscosity of the contin-uous phase (oil) decreases the droplet size and the droplet generation ratefor fixed flow rates for continuous and dispersed phase [126]. However, this isnot the case for ADIM systems. The tubing material will influence the flowrate achieved under different oil viscosities. For instance, the Tygon tubingwill be more susceptible to flow rate pulsation than PTFE, as it is moreflexible. Moreover, the source of negative pressure will also matter because apressure-controlled pump can provide a more stable and pulseless flow thana syringe pump. The droplet volume generated should be calculated usingthe actual flow rate. The user should calibrate the syringe pump for moreviscous oil before generating the droplets.ADIM System version A, Chapter 3, the viscosity of oil and analyte will havean additional effect of worsening the wetting hysteresis caused by the pipettetip exiting the analyte and the negative pressure. However, the calibrationprocedure detailed in section 3.2.1 should allow the user to incorporate thiseffect into the variable Tsystem.5.4.4 Analysis of Random and Systematic Errors[Following discussion has been added for the Dissertation] The usershould be aware of the random and systemic errors in the developed systems.The random errors in the system arise due to fluctuations in the flow rate,electrical probe placement, and wetting hysteresis. The vacuum pump forADIM system version A or the syringe pump for ADIM system version Binduces variation in flow rates, directly affecting the droplet size consistency.1055.5. SUMMARYThis fluctuation results in a deviation from the target droplet volume, evenwith calibrated parameters.The systemic errors of the developed system are related to the 3D printerused, hydrodynamic resistance of the tubing, deformation of the well plate,calibration inaccuracies, and connectors in the system setup shown in Fig.3.1 and Fig. 5.1. The 3D printer’s firmware introduces a fixed delay betweenconsecutive commands, which impacts droplet volume calculations. Table3.2 and Table 5.2 show the analysis for different programmed delays and thedifference between programmed and actual delays. However, the frame rateof the video of the 3D printer movements limits the results. Table 3.2 showsan analysis with 50 frames/s while Table 5.2 shows the updated analysiswith 120 frames/s. Delay time cannot be differentiated higher than 8.3 ms(120 frames/s).Variations in tubing material, length, and internal diameter change the hy-drodynamic resistance and systematically affect the flow rate and the dropletvolume generated. Fig. 3.1 and Fig. 5.1 show the system setup with con-nections from the tubing to the pipette tip, flow rate sensor, or cartridge.The connections introduce errors to the system’s operations by changing thehydrodynamic resistance of the system.5.5 SummaryThis chapter also addresses the need for an affordable, low-cost microfluidicsystem by modifying an FDM 3D printer (Prusa MINI+) capable of gen-erating droplets from 15 different analytes. The newly developed system,an ADIM system (Version B), demonstrates the ability to consistently cre-ate monodisperse droplets of various sizes and compositions, with limitationsstemming from the printer’s speed and bed size to house the oil well with ana-lytes. This system also differentiates itself by leveraging the automation andstandard equipment of a 3D printer, diverging from traditional microfluidicapproaches. Setting up this system is straightforward, requiring less thana day’s work. All necessary custom components, such as the 3D-printednozzle with J bend, are designed to be fabricated using the same printer.Compared to existing methods documented in the literature, this system isuser-friendly, eliminating the need for a clean room to produce microfluidicchips for droplet generation and avoiding the complexities of valve-operateddroplet-on-demand mechanisms.Table 5.1 compares the developed system with other commercially available1065.5. SUMMARYdroplet generation systems. However, it does not contain price estimatesfor droplet generation platforms (ADIM systems) designed in research labsdiscussed in the literature review. Therefore, the updated table is presentedin Table 5.3 estimates prices of other ADIM systems based on bill of materialsand compares them with both versions of the developed ADIM system. It canbe observed that developed systems are at par or better than other ADIMsystems in the literature. Both developed ADIM systems have a higherthroughput and similar accuracy. However, the price to a lab is significantlylower for our systems as compared to DropLab [87] or the platform designedby Helena Zec [88].Quantification of the streptavidin-biotin assay in the zone of equivalence,determined in Chapter 4, shows the application of the developed system inperforming immunoassays. The dataset of the experiments can be foundonline[127]. This article fulfills the following objective of the research todevelop a high throughput low-cost microfluidic lab-in-tubing system capableof:1. generating droplet libraries from multiple analytes (minimum 2) witha volume coefficient of variance < 5%3. detecting agglutination of biotinylated Bovine Serum Albumin (BSA)in 0.0128 µg/mL to 5000 µg/mL concentration range with Streptavidinbeads1075.5.SUMMARYTable 5.3: Comparison between this study and other worksDroplab[87] Helena Zec[88] ADIM-A[1] ADIM-B [3]Smallest Droplet 20pL 3.8nL 1µl 12nLCoefficient of Variance 2.6% Unknown 3% 2%Number of Analytes 16tubes/ 384wellplate 96 96+ 15Throughput (per hour) 720 6000 ∗ 6900 6900Microfluidic chips use No No Yes YesSetup Difficulty Medium High Medium LowClean room Required No Yes No NoDroplet Library Storage Yes No Yes YesCost (USD) ≈ $ 9,305 ≈$6,862 ≈ $2500 ≈ $1700108Chapter 6ConclusionIn this chapter, the dissertation’s main findings and contributions are sum-marized, and then suggestions for future work are offered in the subsequentsection.6.1 SummaryUsing an XYZ movement platform (ADIM system) for droplet generationprovides several advantages over complex microfluidic chips. It offers greaterflexibility and reconfigurability since inlets or control mechanisms can be eas-ily repositioned without redesigning the microfabricated microchannels. Thisapproach simplifies the overall system and reduces costs by avoiding complexmicrofabrication. Additionally, XYZ platforms can handle various analytesand scale up more easily, making the system a versatile, cost-effective solu-tion for high-throughput droplet-based applications.The overall objective of this study was to develop a high throughput low-costmicrofluidic lab-in-tubing system capable:1. generating droplet libraries from multiple analytes (minimum 2) witha volume coefficient of variance < 5%2. storing the droplet libraries3. detecting agglutination of biotinylated Bovine Serum Albumin (BSA)in 0.0128 µg/mL to 5000 µg/mL concentration range with StreptavidinbeadsChapter 3 fulfills the first objective of this study by adapting an FDM 3Dprinter into an automated dynamic inlet (ADIM) system (Version A). Thedeveloped system is simple to set up, as all the modified parts can be printedon the printer before modification. Although the calibration methodologyis multistep, it allows the user to use a vial of any geometry. The end oftubing is moved from oil to analyte in a top-down approach, to generate1096.2. COMPARISON OF ADIM SYSTEM VERSION A AND Bdroplets. The generation of droplets from multiple analytes is shown to bemonodisperse (CV < 5%). The second objective is fulfilled by the developeddroplet cartridge connectors that allow the generated droplet libraries to bestored for later use.Chapter 4 uses the ADIM system (Version A) to generate a train of varyingconcentrations of premixed analyte (Streptavidin coated beads and biotiny-lated BSA) and quantify it using image analysis. Previous work [4] looksat the same set of droplet trains using an off-the-shelf light break sensor.This work combines the two optical detection methods to achieve a com-bined accuracy of 97.9%. The system can differentiate between agglutinatedand non-agglutinated droplets with 100% accuracy, across the entire rangeof concentrations (0.0128 µ g/mL to 5000 µg/mL). Moreover, the system canclassify the agglutination into three regions (Band 1: 0.0128 to 0.32 µg/mL,Band 2: 1.6 to 40 µ g/mL, and Band 3: 200 to 5000 µg/mL) with a 97.50%accuracy for image analysis. This chapter fulfills the third objective with asuccessful quantification of agglutination.Chapter 5 developed an alternative version of the ADIM system in Chapter3 into version B and used it to quantify agglutination in Band 2, the zone ofequivalence determined in Chapter 4. This system works via a “bottom-up”approach in which the multi-axis translation capability of the 3D printer re-peatedly meters analyte fluid from Eppendorf tubes with small orifices at thebottom. The performance of the developed system is characterized by quan-tifying the droplet size distributions, and the application for agglutinationassays incorporating image analysis capabilities has also been successfullydemonstrated. This chapter fulfills the first and the third objectives by de-veloping an ADIM system and showing its application.6.2 Comparison of ADIM System Version A and BThe main issue with ADIM System version A was the extensive calibrationrequired for the system setup, especially if the user’s application requiresonly a few analytes. Therefore, ADIM System version B has a bottom-up approach, leading to the analyte-oil interface not changing results in arelatively simple calibration process. However, ADIM System version B hasmovements only in the X and Z axes, therefore the number of analytes is low(≈ 15) as compared to ADIM System version A, which can move in X, Y,and Z axes and can house > 96 analytes. Table 6.1 lists the pros and consof both versions of ADIM Systems.1106.3. FUTURE WORKTable 6.1: Comparison of ADIM System A and ADIM System BADIM System - A ADIM System - BPros ProsNumber of Analytes > 96 Calibration ProcessMovement in XYZ axes Easier to refillUse of Cartridge possible Use of Cartridge possibleThroughput 6900 Throughput 6900Cons ConsExtensive Calibration Number of Analytes ≈ 15Need conduction level measurement Movement in XZ axes6.3 Future WorkAfter the development of two versions of the ADIM system, various researchdirections can be taken. Droplet library generation will increase the through-put of any research that was manually pipetting droplets, e.g. Zhang andteam [100] mentioned in section 5.4.1 or David’s methodology mentioned inthis work. Both versions of the ADIM system are capable of generating alarge data set consisting of images and LBS data for the selected immunoas-say.The next step could be to perform an immunoassay for a biomarker e.g. CRPfor a wide concentration range and explore additional methods of quantifi-cation. Another direction the research could take is the development of 3Dprinted microfluidic chips that can be installed in line with the system andperform tasks like merging, splitting, or sorting.6.3.1 Use of Droplet LibrariesFigure 6.1 shows how droplet cartridges of two different analytes can be usedin line with a droplet merging chip that combines the respective droplets andstarts the agglutination reaction. Once the reaction starts, the beads andanalyte need to mix and incubate which is carried out in a long spool oftubing. Errors due to unknown incubation time can be eliminated. Thebeads are mixed within droplets due to inner vortices caused by frictionof the tubing wall. Moreover, the tubing could be incubated at a specifictemperature by submerging the tubing in a water bath.Additionally, 3D printed microfluidic chips can enhance the implementation1116.3. FUTURE WORKFigure 6.1: Schematic of how droplet cartridges can be used with microfluidicchips to run experiments (Created with BioRender.com)of immunoassay. Mitos Dropix has compatible microfluidic chips that canperform operations such as droplet merging, or splitting. Similarly, if com-patible microfluidic chips can be 3D printed it would greatly enhance theutility of the developed ADIM systems.6.3.2 Machine LearningA dominant trend in the literature related to image analysis is the use ofmachine learning algorithms to quantify immunoassays. Another student inour lab used the data set generated in Chapter 4, to perform a compara-tive study of five different deep learning models and develop a hybrid deepneural network to quantify agglutination assays [5]. 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Accessed: 2024-08-10.→ pages 133129AppendicesAppendix A: Dimensionless Number and PlugSedimentation Calculation(a) Capillary NumberThe capillary number Ca in microfluidics is a dimensionless number thatcharacterizes the relative effect of viscous forces versus surface tension forcesand is defined as:Ca =µVσwhere:− µ is the dynamic viscosity of the continuous phase (mineral oil in thiscase),− V is the velocity of the continuous phase,− σ is the interfacial tension between the dispersed phase (PBS withStreptavidin beads and BSA) and the continuous phase (mineral oil).For light mineral oil = 0.03 mN/m For FC-40 = 0.052 mN/mTo calculate the velocity V from a flow rate given in microliters per minute(uL/min) to meters per second (m/s), we use the internal diameter of thetubing and the flow rate.The flow rate Q is given by:Q = 100 uL/minTo convert Q to m³/s, we use:130Q(in m3/s) =100× 10−6 L/min60Since 1L = 10−3m3, this becomes:Q(in m3/s) =100× 10−9m3/min60= 1.6667× 10−9m3/sThe velocity V is then calculated using the cross-sectional area A of thetubing:A = πr2r =0.508mm2r(in m) =0.508× 10−3m2A = π × 0.64516× 10−9 = 20.268× 10−6m2Then, the velocity V can be calculated as:V =QAV =1.6667× 10−9m3/s20.268× 10−6m2 = 0.8223 × 10−6msAfter calculating the velocity V , the capillary number isCa =µVσ=1× 10−3 × 0.8223 × 10−60.03mN/m= 2.74× 10−7131(b)Calculation of Wetting Film Thickness According toBretherton’s LawBretherton’s law provides an expression for the thickness δ of the wettingfilm left behind a moving gas bubble in a liquid-filled cylindrical tube. Thelaw states that the thickness of the wetting film is related to the capillarynumber (Ca) as follows:δ ≈ 1.34 ·R · Ca2/3where:− δ is the thickness of the wetting film,− R is the radius of the cylindrical tube,− Ca is the capillary number, which is a dimensionless number definedby the formula Ca = µUσ where µ is the viscosity of the liquid, U isthe velocity of the bubble, and σ is the surface tension between the gasand the liquid.δ ≈ 1.34 ·R ·Ca2/3 = 1.34×0.254×10−3×(2.74×10−7)2/3 = 0.1436×10−9mTo calculate the film thickness δ, one must know the capillary number Ca,which depends on the velocity of the bubble and the fluid properties (vis-cosity and surface tension). The velocity of the bubble can be measuredor calculated based on the volumetric flow rate and the tube dimensions.Once Ca is known, the film thickness can be calculated using Bretherton’srelation.(c) Density Calculation for M270 and C1 DynabeadsThe total density of the beads, which consist of ferrite and polystyrene, canbe calculated using the following weighted average formula:ρbead = fferrite · ρferrite + (1− fferrite) · ρpolystyrenewhere:− ρbead is the overall density of the bead,132− fferrite is the fraction of ferrite in the bead,− ρferrite is the density of ferrite, which is taken as 5 g/cm3,− ρpolystyrene is the density of polystyrene, given as 1.055 g/cm3 .1. M270 Dynabeads (14% ferrite)For M270 Dynabeads, fferrite = 0.14[129].ρM270 = 0.14 · 5 g/cm3 + (1− 0.14) · 1.055 g/cm3ρM270 = 0.7 g/cm3 + 0.9073 g/cm3ρM270 = 1.6073 g/cm32. C1/T1 Dynabeads (26% ferrite)For C1 and T1 Dynabeads, fferrite = 0.26[129].ρC1 = 0.26 · 5 g/cm3 + (1− 0.26) · 1.055 g/cm3ρC1 = 1.3 g/cm3 + 0.7807 g/cm3ρC1 = 2.0807 g/cm3Conclusion− The density of M270 Dynabeads is approximately 1.61 g/cm3.− The density of C1/T1 Dynabeads is approximately 2.08 g/cm3.133(d) Settling Velocity for M270 and C1 Dynabeads in PBSThe settling velocity Vs of M270 and C1 Dynabeads in Dulbecco’s Phosphate-Buffered Saline (PBS) can be calculated using Stokes’ Law, as described byAmar S. Basu. The formula is:Vs =29· r2(ρp − ρf )gηwhere:− Vs is the settling velocity,− r is the radius of the particle (in meters),− ρp is the density of the particle (in kg/m3),− ρf is the density of PBS (in kg/m3),− g is the acceleration due to gravity (9.81m/s2),− η is the dynamic viscosity of PBS, taken as 0.001Pa·s(or 0.001N.s.m−2).The density of PBS is taken as 1000 kg/m3.1. Settling Velocity for M270 DynabeadsThe density of M270 Dynabeads (ρM270) is 1.61 g/cm3, or 1610 kg/m3. Thediameter of the M270 bead is 2.8µm, so the radius is:rM270 =2.8µm2= 1.4× 10−6mUsing Stokes’ Law:Vs =29· (1.4× 10−6)2 · (1610− 1000) · 9.810.001Vs =29· 1.96× 10−12 · 610 · 9.810.001Vs =29· 1.1744× 10−80.001134Vs ≈ 2.61× 10−6m/s2. Settling Velocity for C1/T1 DynabeadsThe density of C1 Dynabeads (ρC1) is 2.08 g/cm3, or 2080 kg/m3. The di-ameter of the C1 bead is 1µm, so the radius is:rC1 =1µm2= 0.5× 10−6mUsing Stokes’ Law:Vs =29· (0.5× 10−6)2 · (2080− 1000) · 9.810.001Vs =29· 2.5× 10−13 · 1080 · 9.810.001Vs =29· 2.6505× 10−90.001Vs ≈ 0.589× 10−6m/sConclusion− The settling velocity of M270 Dynabeads in PBS is approximately2.61× 10−6m/s.− The settling velocity of C1 Dynabeads in PBS is approximately 0.589×10−6m/s.(e) Flow Rate Calculation for Dynabeads in PBSWe will calculate the flow rate Q for M270 and C1 Dynabeads in a tube witha diameter of 0.559mm. The settling velocity Vs is converted to cm/s, andthe flow rate is then calculated using the formula:Q = Vs ·Awhere:135− Q is the flow rate (in µL/min),− Vs is the settling velocity (in cm/s),− A is the cross-sectional area of the tube.The area A of the tube is given by:A = π · r2where r = d2 , with d = 0.0559 cm (since 0.559mm = 0.0559 cm).1. M270 DynabeadsThe settling velocity of M270 Dynabeads in m/s is 2.61 × 10−6m/s. Con-verting to cm/s:Vs = 2.61× 10−6m/s× 100 = 2.61× 10−4 cm/sNow, the cross-sectional area A of the tube is:A = π ·(0.05592)2= π · (0.02795)2 ≈ 2.454× 10−3 cm2Thus, the flow rate Q is:Qs = Vs ·A = 2.61× 10−6 · 2.454× 10−3 ≈ 6.4× 10−7 cm3/sTo convert this to µL/min:Qs = 6.4× 10−7 cm3/s× 1000× 60 = 38.43× 10−3 µL/min2. C1 DynabeadsThe settling velocity of C1 Dynabeads in m/s is 0.589×10−6m/s. Convertingto cm/s:Vs = 0.589× 10−6m/s× 100 = 58.9× 10−6 cm/s136The cross-sectional area A of the tube is the same as above:A = 2.454× 10−3 cm2Thus, the flow rate Q is:Qs = Vs ·A = 5.89× 10−7 · 2.454× 10−3 ≈ 1.445× 10−7 cm3/sTo convert this to µL/min:Qs = 1.445× 10−9 cm3/s× 1000× 60 = 8.67× 10−3 µL/minConclusion− The flow rate for M270 Dynabeads in PBS is approximately 38.43 ×10−3 µL/min.− The flow rate for C1 Dynabeads in PBS is approximately 8.67×10−3 µL/min.(f) Shield Parameter for Dynabeads in PBS for PTFE andTygon TubingThe Shields parameter θ is calculated using the formula:θ =9µpVpa2∆ρgwhere:− µp = 0.001Pa · s (dynamic viscosity of PBS),− µc = 1.3188Pa · s (dynamic viscosity of Light Mineral Oil (M5904)),ρc = 0.84 g/cm3− g = 9.81m/s2 (gravitational acceleration),− a = d2 is the particle radius,− ∆ρ = ρp − ρf is the density difference between the particle and thefluid,− Vp is the flow velocity (calculated from flow rate).137We calculate the Shields parameter for M270 and C1 beads at different flowrates (60µL/min, 90µL/min, 120µL/min) in two tubing types: PTFE andTygon.1. Calculation of ∆ρ (Density Difference)The density difference ∆ρ for each bead is calculated as follows:For M270:∆ρ = ρp − ρf = 1.61 g/cm3 − 1 g/cm3 = 0.61 g/cm3 = 610 kg/m3For C1:∆ρ = ρp − ρf = 2.08 g/cm3 − 1 g/cm3 = 1.08 g/cm3 = 1080 kg/m32. Calculation of Flow Rate to Linear VelocityThe flow rate Q is converted to flow velocity Vp by dividing the flow rate bythe cross-sectional area of the tube:Vp =QAwhere A is the cross-sectional area of the tubing. The cross-sectional areasfor the PTFE and Tygon tubes are calculated as:APTFE = π(0.559mm2)2= 2.454× 10−7m2ATygon = π(0.508mm2)2= 2.027× 10−7m2For each flow rate, the corresponding flow velocity is calculated as follows:For a flow rate of 60 µL/min:V PTFEp =60× 10−9m3/min2.454× 10−7m2 ×160= 4.073× 10−3m/sV Tygonp =60× 10−9m3/min2.027× 10−7m2 ×160= 4.933× 10−3m/s138For a flow rate of 90 µL/min:V PTFEp =90× 10−9m3/min2.454× 10−7m2 ×160= 6.112× 10−3m/sV Tygonp =90× 10−9m3/min2.027× 10−7m2 ×160= 7.400× 10−3m/sFor a flow rate of 120 µL/min:V PTFEp =120× 10−9m3/min2.454× 10−7m2 ×160= 8.150× 10−3m/sV Tygonp =120× 10−9m3/min2.027× 10−7m2 ×160= 9.867× 10−3m/s3. Shields Parameter CalculationUsing the flow velocities and the density differences, we calculate the Shieldsparameter for M270 and C1 beads for each flow rate and tubing type. Resultsare shown in table A.2.Table A.2: Shields Parameter for M270 and C1 Beads at Different FlowRates and Tubing TypesBead Type Flow Rate (µL/min) PTFE Tube θ Tygon Tube θM270 60 3125 3785M270 90 4690 5678M270 120 6254 7571C1 60 13840 16762C1 90 20768 25144C1 120 27693 33527(g) Circulation Zone for Dynabeads in PBS for PTFE andTygon Tubing"The length of the circulation zone denoted LC , can be found using thePoiseuille model. The time required for a particle to reverse direction isVS/(0.707Rp), where VS is the sedimentation velocity and RP is the plugradius. During this time, the particle travels a horizontal length LC , whichcan be found by integrating the velocity profile over this time period. Scaling139LC by RP gives the aspect ratio of the circulation zone, and the dimension-less form can be expressed as a function of the plug velocity or the Shieldsparameter:" (AmarSBasu)LcRp=5√2Vp24Vs=5√248θRemember− The settling velocity of M270 Dynabeads in PBS is approximately2.61× 10−8m/s.− The settling velocity of C1 Dynabeads in PBS is approximately 5.89×10−9m/s.− RP is radius of tubing for PTFE (0.2795 mm) and Tygon (0.254 mm)Concluding Remarks− Table A.3 gives the results for the circulation zone Lc for different typesof beads in each PTFE and Tygon.− The length of 1 µL droplet is ≈ 4 mm in either PTFE or Tygon tubing,which is several magnitudes smaller than the circulation zone for M270,C1, or T1 beads. This means the beads should circulate the full lengthof the droplet.Table A.3: Shields Parameter and Circulation Zone for M270 and C1/T1Beads at Different Flow Rates and Tubing TypesBead Type Flow Rate PTFE Tygon PTFE TygonBead Type µL/min θ θ Lc (mm) Lc (mm)M270 60 3125 3785 129 142M270 90 4690 5678 193 212M270 120 6254 7571 258 283C1 60 13840 16762 571 627C1 90 20768 25144 857 941C1 120 27693 33527 1142 1255140Appendix B: Supplementary Material to Chapter 3Conical SectionTable B.1: Percentage of Coefficient of Variance for Conical (Figure 5 a-c)Flow Rate 150 µl/min 100 µl/min 50 µl/minVolume 1.0µl 1.5µl 2.0µl 1.0µl 1.5µl 2.0µl 1.0µl 1.5µl 2.0µlMin 0.84 0.99 0.55 1.02 2.43 0.71 1.37 1.64 0.81Median 0.91 1.40 0.77 1.23 2.54 1.24 1.98 2.09 0.82Max 1.71 1.81 0.89 2.43 2.66 1.26 2.65 2.93 0.97Cylindrical SectionTable B.2: Percentage of Coefficient of Variance for Cylindrical (Figure 5a-c)Flow Rate 150 µl/min 100 µl/min 50 µl/minVolume 1.0µl 1.5µl 2.0µl 1.0µl 1.5µl 2.0µl 1.0µl 1.5µl 2.0µlMin 1.40 1.26 0.78 1.96 1.38 2.13 2.00 1.26 1.27Median 1.95 1.46 0.90 2.02 1.89 2.32 2.08 1.76 1.60Max 2.51 1.65 1.02 2.08 2.41 2.51 2.17 2.26 1.93141Train of 100 dropletsFigure B.1: Train of 100 single analyte constant volume (0.75µL) dropletsfrom 100µLPressure stabilization for vacuum pumpFigure B.2: Pressure stabilization for vacuum pump in ADIM system versionA to maintain negative pressure142Appendix C: Supplementary Material to Chapter 4Image Preprocessing Steps before analysisThe first frame is treated as the background image. The MATLAB scriptasks the user to draw an ROI on the first image, which is chosen to bethe microfluidic channel in the viewing window. This option is useful ifmultiple microfluidic channels are in the viewing window. Then the scriptisolates a droplet in the frames of the captured videos and saves the ROIcropped original and the background subtracted image (processed Image)into different folders. The filenames of the files are chosen to be of a specificformat Test00Droplet XX YY.png, where 00 is the test number, XX is thedroplet number, and YY is the frame number of that droplet (1st frame =01, 2nd frame = 02. . . ).[The following discussion in this section has been added for theDissertation] The second MATLAB script looks at each processed imagesaved to identify the droplet and finds its size to create an overlay to cropthe image exactly around the droplet. This extracted cropped frame is usedfor the analysis in all the sections of the article. Image processing steps aredetailed below. It contains few parameters that needs to be adjusted bythe user: number of dilation operations, number of erosion operations, fudgefactor for edge detection, and number of extra pixels around the droplets tobe included in the crop. These factors are adjusted based on a single imagefrom different concentrations before the script run for all the images.Step 1: Read Image− Purpose: Load the droplet image into MATLAB, convert it to grayscale,and display it for initial visualization.− Action:– Use imread to read the image.– Convert the image to grayscale using im2gray.– Display the image with titles or annotations.Step 2: Detect Droplet− Purpose: Segment the droplet from the background by detectingedges based on contrast differences.143− Action:– Apply the Sobel edge detection operator using edge.– Adjust sensitivity with a threshold multiplied by a "fudge factor".This fudge factor has to be selected by the user.– Display the binary gradient mask.Step 3: Remove Connected Objects on Border− Purpose: Remove irrelevant objects connected to the image borders.− Action: Use imclearborder with a 4-connectivity option to excludeborder-connected objects and display the updated image.Step 4: Dilate the Image− Purpose: Close gaps in the segmented edges of the droplet for betterboundary detection.− Action:– Create vertical and horizontal structuring elements using strel.– Apply dilation using imdilate to fill gaps in the gradient mask.Number of times image is dilated is controlled by the user.– Display the dilated image.Step 5: Fill Interior Gaps− Purpose: Fill holes inside the segmented droplet region for a completebinary mask.− Action: Use imfill with the ’holes’ option to fill internal gaps anddisplay the result.Step 6: Smooth the Object− Purpose: Refine the boundaries of the segmented droplet to makethem smoother and more natural.− Action:– Create a diamond structuring element using strel.144– Apply erosion multiple times using imerode. Number of timesthe image is eroded is selected by the user.– Optionally remove small objects using bwareaopen.– Bounding box is defined. The user decide how many extra pixelsaround the droplet to crop, e.g. 15 pixels around the droplet wasselected for this study.Step 7: Visualize the Segmentation− Purpose: Overlay the segmented droplet mask on the original imageto visually verify segmentation.− Action:– Use labeloverlay to combine the binary mask with the originalimage.– Mark key features (centroid, bounding box corners) for visualiza-tion.Step 8: Outline and Mask− Purpose: Extract the region of interest (ROI) for the droplet by ap-plying the binary mask.− Action:– Use bwperim to create an outline of the droplet.– Apply the outline as a mask to retain only the droplet region andset other pixels to black.– Crop the ROI for further analysis.Mean of Bins for all droplets of each concentration145Figure C.1: Image processing stepsFigure C.2: Mean for Vertical Bins (Bin Size 4 pixels) for different concen-tration. Multiple lines are for each image captured of different droplets forthat concentration146Figure C.3: Mean for Horizontal Bins (Bin Size 4 pixels) for different con-centration. Multiple lines are for each image captured of different dropletsfor that concentrationFormulas for the mean and standard deviation of the grayvalues calculationThe formulas below calculate the mean intensity and standard deviation ofpixel values in vertical and horizontal bins within an image.Here is a breakdown of what each formula represents:1. Vertical Mean Calculation (Equation 1):Vertical Meank =1bxkb∑j=(k−1)b+1x∑i=1I(i, j) (6.1)− This formula calculates the mean intensity value for the kth verticalbin.− I(i, j) represents the intensity value of the pixel located at the ith rowand the jth column.− The sum is taken over all pixels in the kth vertical bin, where b is thebin size and x is the total number of rows.1472. Horizontal Mean Calculation (Equation 2):Horizontal Meanl =1bylb∑i=(l−1)b+1y∑j=1I(i, j) (6.2)− This formula calculates the mean intensity value for the lth horizontalbin.− Similar to the vertical mean calculation, the sum is taken over allpixels in the lth horizontal bin, where b is the bin size and y is the totalnumber of columns.3. Vertical Standard Deviation Calculation (Equation 3):σVertical,k =√√√√ 1bx− 1kb∑j=(k−1)b+1x∑i=1(I(i, j)−Vertical Meank)2 (6.3)− This formula calculates the standard deviation of intensity values forthe kth vertical bin.− It measures the spread of the intensity values around the vertical meanwithin the bin.4. Horizontal Standard Deviation Calculation (Equation 4):σHorizontal,l =√√√√ 1by − 1lb∑i=(l−1)b+1y∑j=1(I(i, j)−Horizontal Meanl)2 (6.4)− This formula calculates the standard deviation of intensity values forthe lth horizontal bin.− It measures the spread of the intensity values around the horizontalmean within the bin.Where:− x-axis along the length of the droplet, y-axis is along the height of thedroplet− I(i, j) is the intensity value of the pixel in the ith row and the jthcolumn.− Vertical Meank is the mean intensity of the kth vertical bin.148− Horizontal Meanl is the mean intensity of the lth horizontal bin.− σVertical,k is the standard deviation of the kth vertical bin.− σHorizontal,l is the standard deviation of the lth horizontal bin.− k ranges from 1 to yb for the vertical bin calculations.− l ranges from 1 to xb for the horizontal bin calculations.− b is the bin size.These formulas are essential for processing images in a structured way, allow-ing for detailed analysis of pixel intensity variations within specified regions(bins) of the image.Biomarkers and their normal and cutoff rangesTable B.1: Summary of some biomarkers with their normal range and cutoffvalue which indicate high riskBiomarker Normal (µg/mL) Cutoff (µg/mL) RefC-Reactive Protein Low: < 0.219 > 0.658 [34],0.219 < Average < 0.658 [35]Myoglobin < 0.072 > 0.999 [36]α-Fetoprotein < 0.040 > 0.400 [37]Prostate Specific Antigen < 3.996 > 9.989 [38]Table B.2: Added for Dissertation: Summary of some biomarkers withtheir normal range and cutoff value which indicate high riskBiomarker Normal (nmol/L) Cutoff (nmol/L) RefC-Reactive Protein 0.83− 83 > 83 [34],[35]Myoglobin 0.29− 4.07 > 4.07 [36]α-Fetoprotein < 0.593 > 5.93 [37]Prostate Specific Antigen < 0.14 > 0.36 [38]149Agglomerates Movement within the DropletFigure C.4 shows different captured frames of the same droplet. It can beobserved that the agglomerate highlighted by the red circle is rotating dueto the inner vortices of the droplet.Figure C.4: Droplet of 1.6 µg/mL captured at different times within the view-ing window. The red circle shows the largest agglomerate’s shape-changingas it tumbles150Appendix D: Supplementary Material to Chapter 5When the volume of analyte gets low in Eppendorf tubeAs the Eppendorf tube sits on top of the oil, the oil rises into the tube to acertain height. In this study, the Eppendorf tube is lowered a few millimetersinto the oil, which means the oil rises a few millimeters through a 0.7mmEppendorf tube. Due to this, the level of the analyte has to be maintainedabove a minimum amount, otherwise, oil seeps into the Eppendorf tubepushing the analyte up and leading to a shift in the oil and analyte interface.The change in the interface lead to droplet size reduction, as shown in Fig.D.1. After droplet number 16 there is a drastic decrease in the droplet’svolume, due to low analyte volume and interface shifting. For this study, theminimum analyte volume limit is 30µL.Minimum Droplet Volume at 35 µL/minThe MATLAB script moves the syringe needle tip from oil (origin) up intothe analyte taking tup ms, waiting for the calculated delay (tdelay), and thentaking tdown ms to bring the tip back down into the oil (origin) to generateFigure D.1: Train of 100 droplet of 1 µL target volume is generated at35µL/min.151Figure D.2: 5 droplet of generated at 35µL/min to show the minimumdroplet volume possible when tdelay is equal to zero compared to when G4P0 command is used for tdelay.a droplet. The G4 command is used to give a delay between tup and tdown.When no delay is required between tup and tdown, no command is written,however, if G4 P0 is written, it should give 0ms delay. Few experiments areconducted with G4 P0 commands which introduce a 125 ms delay betweenthe commands instead of 8.3 ms, as per Table 2. The extra ≈100 ms leadsthe minimum volume achieved at 35 µL/min to be 0.4 µL rather than 0.3µL, as shown in Fig. D.2.Long train of 100 droplets of 1 µLTo show the developed system’s capability to generate a long train of droplets,a train of 100 droplets is generated at 35µL/min, shown in Fig. D.3. Asthe Eppendorf tube is open from the top, it can be refilled continuously, andtherefore the length of the train can be extensively long.152Figure D.3: Train of 100 droplet of 1 µL target volume is generated at35µL/min.Lowest volume: train of 12nL dropletsThe lowest target volume tested for the developed system is 12nL at 1.5µL/minwith 1 µL syringe in the syringe pump. The variation is the volume of dropletgenerated is high, however, the cyclic fluctuations due to the lead screw ofsyringe pump can be seen in Fig. D.4.Tygon Tube FoulingRepeated use of Tygon tubing showed signs of degradation causing the waterdroplets to adhere to the walls, leading to satellite droplets latching to thewalls and droplet breakage. Therefore, the tubing has to be replaced period-ically. PTFE tubing does not show degradation after use, however, the innervortices of the droplet are limited leading to limited mixing. In agglutinationassays in Lab-in-Tubing the mixing inside the droplet plays a crucial role inincubation. Limited mixing leads to longer incubation time. Fig. D.5 isan example of this. This means that Tygon tubing captures molecules fromagglutination assays causing cross-contamination. The agglutination assays153Figure D.4: Train of 15 droplets of 12 nL target volume is generated at1.5µL/min. Repeated experiment three (3) timesare carried out in ascending order of concentration to avoid contaminationof the previous assay on the current.Figure D.5: Encircled parts show water droplets adhering to the Tygontubing wallIncrement of delays and consequent increase in volumePrusa Mini+ printer allows the user to enter delay using the G4 command inmilliseconds (P) and seconds (S). G4 P100 means a delay of 100ms, whereasG4 S100 means a delay of 100 seconds. Table 2 in the article shows that thereis a difference between programmed delay and actual delay executed by theprinter. Fig. D.6 shows a train of 20 droplets, with varying delays (100ms,125ms,150ms, and 200ms). Experiments are repeated multiple times. Thevolumes generated for 100ms and 150ms are not discernable from each other.However, the volume generated at 100ms and 200ms can be distinguishedclearly. Few droplets, e.g. first droplet of P200, show large variation between154repetition of experiments. This occurs as the descending syringe needle pullsthe analyte out of the orifice, generating a droplet with larger than theanticipated volume.Figure D.6: Train of 20 droplets 0f 30 nL with varying delay. Delays of100ms, 125ms,150ms, and 200ms are programmed.Droplet MergingThe timing between two consecutive droplets is very important. When gener-ating trains from two different analytes, it can be observed that the dropletswould merge if the interval between them is kept too short (the minimumtime of 1.38 seconds). Droplet merging is very useful in microfluidics, asthe start time of two analyte’s mixing is known exactly, therefore, the timefor incubation can be calculated with high accuracy. However, the dropletmerging in this system is unpredictable, making it more of a liability thanan asset.The ability of a microfluidic system to merge droplets passively is very usefulin applications such as concentration gradients, chemical and biological reac-tions. In protein, enzymes, and chemical reactions, the initialization moment155Figure D.7: Train of 10 droplets 0f 0.3 µL from two different analytes isgenerated with minimum timing between changing the analyte resulting indroplet merging. Results of three (3) experiments where 100% is achieved isshownwhich occurs at the time of droplet merging allows for a better incubationtime assessment. However, in the developed system, the droplet mergingoccurs sporadically, with a higher likelihood when there is no delay betweenswitching neighboring analyte Eppendorf tubes on the 3D printed rack atopthe well. Fig. D.7 shows the result of three (3) experiments where 100%of 10 droplets of 0.3 µL from two different analytes resulting in 10, 0.6 µLdroplets.Media: video file titled ’DropletMerging.mp4’The video titled DropletMerging.mp4, in the supplementary files, demon-strates an instance where two droplets merge. The different dyes allow forthe visualization of streamlines within the droplets.Analyte Selection and the related issuesThe analyte can leak from the orifice in the Eppendorf tube and sink to thebottom of the well if the density of the analyte is comparable to or higher156than the density of the silicone oil (0.97 g/cm3) used in this study. Thewater density is comparable to the density of the silicone oil but the surfacetension and capillary forces keep the water in the Eppendorf tube.Occasionally the piercing action of the syringe needle tip causes excessivewater to leak out, leading to larger first droplets mentioned in section 3.2.M270 Streptavidin Dynabead (2.7 µm diameter) also faces this issue, but itis not excessive. Whereas when C1 Streptavidin (1 µm diameter) beads areused the droplets have a large number of oil droplets emulsified inside thedroplet (Fig. D.8). The larger beads could induce more shear-thickeningunder the stress of the syringe needle piercing, effectively reducing the leak-age due to the suspension with smaller beads. Additionally, the use of apremixed solution containing Streptavidin Dynabead and biotinylated BSA,which initiates a reaction when combined, suggests that the agglomerationof the larger 2.8 µm beads might partially obstruct or diminish the orifice’seffective cross-sectional area more significantly than the smaller 1 µm beads,thereby decreasing the leakage.When a premixed solution consisting of C1 Streptavidin microbeads (1µm)and biotinylated bovine serum albumin (bBSA) is used in the developedsystem, it leads to analyte leakage through the Eppendorf tubes. The leakageleads to irregular droplet size, however, more importantly, there is a largeamount of oil droplet emulsification inside the analyte droplet as shown inFig. D.8. Images for these bead tests are taken in a dark-field setting forbetter visualization of emulsification.If the analyte’s density is around or above 0.97 g/cm3, then the user canselect oil like Fluorinert™ FC-40 from Sigma-Aldrich with a density of 1.870g/cm3 or HFE-7500 oil (3M) with 1.614 g/cm3 density. Oil with higherdensity would result in the analyte in the Eppendorf tube not leaking throughits orifice at the bottom. Both oils are extensively used in literature relatedto droplet microfluidics.Figure D.8: C1 Streptavidin Beads set is used with biotinylated BSA, re-sulting in leakage through 0.7 mm orifice in Eppendorf tube, resulting inirregular droplet size and oil droplets emulsification within each droplets157Adapting the developed ADIM systemIf the 3D printer is changed then the Z-axis feedrate will be different andthat has to be taken into account when calculating tup, tdown, and tdelay. Thefirst step should be to find the difference between the programmed delay andthe actual delay for the new 3D printer by video analysis, the methodologydiscussed in Section 2.1. All “axes’ movements and the delay can be evaluatedby analyzing the movement of the syringe needle tip captured in a video."The movements should be for different delay times listed in Table 2. Next,the user should set up the whole system, the tubing running from the syringeneedle tip to the syringe pump. If additional components are to be added inline with the system, they must be added at this stage, e.g. droplet cartridgeor flow rate sensor. The user has to find the tsystem after the system is setup, previous publication [1] discusses the methodology for this in detail. Ifthe syringe needle is programmed to travel x mm in the analyte when thefeed rate on the Z axis of the printer is y mm/min, tup, tdown can be easilycalculated. With all the parameters evaluated, the desired droplet volumecan be achieved using Equation 1.At a given flow rate, there is a limit to the minimum droplet volume due tothe feed rate of the 3D printer in the Z-axis. For instance, section 3.2 (Trainof Droplets with Constant Volume of Single Analyte) states: “The minimumvolume possible at 35 µ L/min flow rate is 0.3 µL with CV% of 6.3%...”.This is because in this study the syringe needle tip moves 1.5 mm into theanalyte, therefore the tup and tdown are calculated to be 250 ms combined.This leads to the minimum volume of 0.3 µL. The user should lower the flowrate, which in turn lowers the minimum droplet volume. A faster 3D printerwould also lower the minimum possible droplet volume, at a given flow rate.To generate droplets smaller than the volumes outlined in this manuscript,the user must change both the tubing and the source of negative pressure.With tubing with a smaller inner diameter and a stable low-flow rate syringepump, the mechanism developed here can generate much smaller dropletvolumes. The limiting factor in this study is the New Era Systems NE-1000syringe pump, which gives fluctuations at low flow rates.As the Eppendorf tube is open from the top, it can be refilled continuously,and therefore the length of the train can be extremely long. However, if theuser wants to generate droplets with large volumes, there is a limit on theanalyte volume the Eppendorf tube can hold before the analyte starts to leakfrom the orifice at the bottom. This is mainly because the density of silicon158oil and water is comparable, as discussed in Section 8 of this supplementary.For the Eppendorf tube, changing the oil to FC-40 would increase the volumeof analyte that can be stored in the tube before it leaks. The weight of theanalyte has to be less than the force required to break the oil-analyte interfaceat the 0.7 mm hole at the bottom of the Eppendorf tube. This principle willapply even if the user uses a larger analyte container e.g. 50 mL Falcontubes.159
Thesis/Dissertation
eng
Electrical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Foulds, Ian G.
Development of microfluidic platforms for low-cost and high-precision droplet generation and quantification of agglutination assays
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