Novel Capacitive Sensors for Chemical and Physical Monitoring in Microfluidic Devices

A dissertation presented to

the faculty of

the Russ College of Engineering and Technology of Ohio University

In partial fulfillment

of the requirements for the degree

Doctor of Philosophy

Parthiban Rajan

May 2019

This dissertation titled

Novel Capacitive Sensors for Chemical and Physical Monitoring in Microfluidic Devices

PARTHIBAN RAJAN

has been approved for

the School of Electrical Engineering and Computer Science

and the Russ College of Engineering and Technology by

Savas Kaya

Professor of Electrical Engineering and Computer Science

Dennis Irwin

Dean, Russ College of Engineering and Technology 3

Abstract RAJAN, PARTHIBAN, Ph.D., May 2019, Electrical Engineering Novel Capacitive Sensors for Chemical and Physical Monitoring in Microfluidic Devices Director of Dissertation: Savas Kaya Lab-on-a-Chip (LoC) devices integrate the elements of advanced electronics and microfluidic technology to create robust, cost-effective, state-of-the-art chemical, environmental and biomedical analysis platforms to be used for wearable health monitors, analytical monitoring and portable point-of-care diagnostics solutions. Demand in these applications are expected to grow exponentially in next decade and efficient, low-cost capable microfluidics platforms can define the success of this on-going revolution along with the printed electronics (PE) that enhance the capability, affordability and scalability of LoC systems. Key to design of such compact and low-cost LoC systems is the variety, size and capabilities of novel nanosensors. Accordingly, this dissertation aims at fusing the PE and microfluidic technology in creating application- specific novel LOC devices. In particular, the vast prospects of capacitive sensing technology have been comprehensively explored. By altering the printed capacitive design elements, microfluidic design and flow properties, a number of novel nanosensors categorized into capacitance based physical and chemical capacitive sensors have been developed. In this dissertation, the concept of capacitive sensing, with a distinct focus on planar printed interdigitated capacitors (IDC) integrated into microfluidic devices, has been investigated. Initial focus has been on process development for efficient activation of printing surfaces for IDC fabrication and microfluidic integration. Force-spectroscopy via an atomic-force microscopy was used to guide this development work, which has not been explored previously. Together with accompanying software development and 3D printed molds, an efficient platform for sensor enriched microfluidic devices is developed. It is shown that low-cost and scalable capacitors that can be printed on flexible media and glass can be adapted to detect multiple physical and chemical parameters. Starting from an analytical approximation of printed IDC and fully utilizing the dielectric loading effect, novel sensors were designed and developed for a variety of sensing 4 modalities including proximity and motion, temperature, humidity, electro-kinetic flow, ionic concentrations (proton and divalent metal ions such as Zn, Cu, Ni) in unique microfluidic designs. Based on these distinct sensing approaches, novel device arrangements that include cross-shaped IDCs on flexible paper surfaces, or nanogap capacitors with ~10nm electrode gaps have been adapted as effective and suitable sensing elements in future applications. Thus, this research work provides both principle and practical basis for development of highly capable and flexible capacitive sensing platforms in an upcoming era where effective use of computational resources and CMOS compatibility may become the key enabler for environmental, chemical and biomedical monitoring systems. 5

Dedication

To Dad, Mom and Bavya.

Acknowledgments I would like to thank Dr. Savas Kaya, my mentor, for always backing me up on my journey through this PhD. His suggestions, motivation, training and guidance in both academic and personal life, for the past 8 years has been one of the primary reasons this work has developed into what it is now. I would like to thank Dr. Wojciech Jadwisienczak for being another important part of this PhD journey. Dr.J has been one of the best professors I have worked with. He has been a solid support and inspiration. I would like to thank Dr. Craig Nunemaker, who has been a strong support in making this collaborative work possible. He has always explained patiently about the biological part of this study, constantly motivated me and pushed me towards achieving goals in this research. I would like to thank my committee members, Dr. Avinash Karanth, Dr. Monica Burdick and Dr. David Tees for their support, time and feedback. I am grateful to them for being a part of my PhD committee. This PhD would not have been possible without the love and support from my family. My dad, my hero, Dr. Rajan, my mom Dr. Amaravathy Rajan and sister Dr. Bavya Rajan. I am out of words to express my love and gratitude to them. Next, I would love to thank my brother Shankar Narayanan. The one person who has seen me grow and been with me, supporting me, holding me and pushing me to success my entire life. I would like to thank him for all his guidance and advice in helping me develop all the software tools in this work by training me on the software development part (Thama, thank you da). I would like to thank Akanksha Rohit, for coming into my life, for travelling with me through this journey and being there for me every minute of the day. She has been one of the reasons which helped me stay focused and strong at all stages of my PhD. I would like to thank my brothers Thoshy Felix, Aby Abraham, Jayakrishnan Muraleedharan Nair and Sai Goutham Koya. Thank you for just being there and supporting me every single time and through all sorts of phases in life, all these years. You guys are the best. Next, I would like to thank Jason Wright, my friend, my roommate and one of the most inspiring person I have ever met in life. Thank you, buddy. For all the late-night research talks and support you have given me all these years. I would like to thank Patrick Hanlon, Tianyi Cai, Nicholas Whitticar and Akanksha Rohit for all their help and contributions in this research. Their help definitely made me more productive in achieving results on time.

Table of Contents Page Abstract ...... 3 Dedication ...... 5 Acknowledgments...... 6 List of Tables ...... 11 List of Figures ...... 12 List of Abbreviations ...... 20 Chapter 1: Introduction ...... 21 1.1 Sensor Integrated Microfluidics – Lab-on-a-Chip ...... 21 1.2 Motivation ...... 23 1.3 Sensor Fusion Era...... 24 1.4 Research Goals & Accomplishments ...... 25 1.4.1 Research Goals ...... 25 1.4.2 Accomplishments ...... 26 1.5 Dissertation Characteristics & Organization ...... 27 Chapter 2: Background ...... 29 2.1 Nanoscience and Surface Study ...... 29 2.1.1 Activated Surfaces ...... 31 2.2 Lab-on-a-chip (LoC) – Microfluidic Devices ...... 33 2.2.1 Evolution of Microfluidic Devices ...... 34 2.2.2 Types of Microfluidic Devices ...... 36 2.2.2.1 Traditional Microfluidic Devices ...... 37 2.2.2.2 Paper Microfluidics ...... 38 2.2.3 Fluid Mechanics in a Microfluidic Channel ...... 40 2.3 Printed Electronics ...... 42 2.3.1 Types of Printing ...... 43 2.4 Bio & Chemical Sensors ...... 45 2.4.1 Types of Biosensors...... 47 2.5 Capacitive Chemical Sensors ...... 50 2.5.1 Capacitance – An overview ...... 50 2.5.2 Types of Printed Capacitors ...... 52 2.5.3 Capacitive Sensors – State of Art ...... 53 8

Chapter 3: Integrated LoC Capacitive-Sensor – Process Development ...... 55 3.1 Surface Study ...... 55 3.1.1 Introduction ...... 55 3.1.2 Materials & Methods ...... 56 3.1.3 Surface Activation ...... 56 3.1.4 Surface Characterization ...... 57 3.1.5 Verification of Surface Activation ...... 57 3.1.6 Optimization of Surface Activation ...... 61 3.1.7 Implications for Printing ...... 64 3.2 Microfluidics ...... 66 3.2.1 Fabrication ...... 66 3.2.1.1 PDMS Microfluidics ...... 66 3.2.1.2 Paper Microfluidics ...... 71 3.2.2 Flow Integration – Syringe Pump ...... 72 3.2.3 Microfluidics Summary ...... 74 3.3 Printed Capacitors ...... 74 3.3.1 Introduction ...... 75 3.3.2 Sensing Platform...... 78 3.3.2.1 Types of CDC ...... 79 3.3.2.2 Noise Elimination ...... 80 3.4 Chapter Summary ...... 82 Chapter 4: Capacitive Physical Sensors ...... 84 4.1 Capacitive – Flow Sensor ...... 84 4.1.1 Electro-osmosis – Integral boundary layer analysis ...... 84 4.1.1.1 Surface driven flow properties ...... 84 4.1.1.2 Integral Boundary Analysis ...... 86 4.1.2 Hagen-Poiseuille Law...... 90 4.1.3 Hydraulic Capacitance – Compliance ...... 92 4.1.4 EDL & Debye Length...... 94 4.1.5 Comprehensive background behind capacitive-flowrate sensor ...... 95 4.1.6 Design and development ...... 98 4.1.7 Results ...... 100 9

4.1.7.1 Pure polar fluids ...... 100 4.1.7.2 Ionic Polar Fluids ...... 104 4.1.7.3 Capacitive-pH sensor ...... 110 4.1.8 Summary ...... 112 4.2 Capacitive-Thermometer (CapT) ...... 113 4.2.1 Design and Development for CapT ...... 115 4.2.2 Results ...... 118 4.2.2.1. Proof of concept ...... 118 4.2.2.2 Printed IDC Thermometer ...... 120 4.2.2.3 Enhancing Capacitive-Thermometer Response ...... 121 4.2.2.4 Flexible Paper CapT ...... 125 4.2.2.5 IDC Humidity Response ...... 127 4.2.3 CapT Summary ...... 130 4.3 Motion (Counting) Sensor ...... 131 4.3.1 Design and Development...... 131 4.3.2 Results ...... 133 4.3.2.1 Sonoplot printed capacitive-motion sensor ...... 133 4.4 Nanogap Capacitors ...... 138 4.5 Chapter Summary ...... 140 5.1 Introduction ...... 142 5.2 Capacitive-Chemical Sensors ...... 143 5.2.1 Magadiite ...... 143 5.2.2 Ion Exchange Resins (IER) ...... 144 5.3 Design and Development ...... 146 5.4 Results ...... 147 5.4.1 Proof of Concept ...... 147 5.4.2 Integration of Magadiite Zn Sensor ...... 154 5.4.3 Ion Exchange Resin Based Capacitive Heavy Metal Sensors ...... 156 5.4.4 Three Chamber Design ...... 160 5.4.5 Software Development – CionXR ...... 165 5.5 Chapter Summary ...... 168 Chapter 6: Conclusions and Future Work ...... 170 10

6.1 Conclusions ...... 170 6.2 Future Work ...... 174 References ...... 177 Appendix A ...... 203 Appendix B ...... 205 Appendix C ...... 206 Appendix D ...... 207 Appendix E ...... 209 Appendix F...... 210 Appendix G ...... 219 Appendix H ...... 220 Appendix I ...... 222 Appendix J ...... 226

List of Tables Page Table 2.1 Product-Technology impact analysis for LoC (L-Low, M- Medium & H- High)...... 34 Table 2.2 Properties of PDMS ...... 38 Table 2.3 Calculation of Reynolds Number for a typical microfluidic channel...... 41 Table 2.4 Evolution of Bio and Chemical Sensors [115]...... 46 Table 3.1 Qualitative Comparison of major techniques used for surface activation...... 56 Table 3.2 Contact angle measurements for different activation conditions and plasma gases...... 58 Table 3.3 Comparison of master mold fabrication techniques...... 68 Table 3.4 Comparison of Sonoplot Microplotter and Dimatix Printer...... 76 Table 3.5 Modelled capacitance vs Measured capacitance...... 77 Table 3.6 Comparison of CDC's used for sensor characterization...... 80 Table 4.1 Comparison of Ohm’s law and Hagen-Poiseuille’s Law...... 92 Table 4.2 Comparison of capacitance in a fluidic and electrical systems...... 93 Table 4.3 Magnitude and slope of flow rate responses shown in Fig. 4.14...... 103 Table 4. 4 Magnitude and slope of flow rate responses shown in Fig. 4.17...... 106 Table 4.5 Magnitude and slope of flow rate responses shown in Fig. 4.18...... 109 Table 4.6 Comparison of sensor characteristics of Capacitive-flowrate sensor vs Commercial flowrate sensor ...... 113 Table 4.7 Results of Bend test on CapT Quad sensor...... 126 Table 4.8 Comparison of sensor characteristics of CapT vs Commercial thermocouple...... 129 Table 4.9 Capacitor dimensions of Fig. 4.43...... 133 Table 4.10 Applications and future work of capacitive-flowrate sensors...... 141 Table 5.1 Design Parameters used for the printed capacitive sensors...... 147

List of Figures Page Figure 1.1 (a) Comparison of traditional lab analysis vs LoC analysis (b) An illustration of LoC blood test kit [1], [2]...... 22 Figure 1.2 Illustration of LoC concept [3]...... 24 Figure 1.3 Illustration of concept of sensor-data fusion for effective health monitoring [7]...... 25 Figure 2.1 Comparison of methods to create nanomaterials (Top-down vs Bottom- up approach) [25]...... 30 Figure 2.2 Representation of Silinol Bonds on Glass substrate[32]...... 32 Figure 2.3 Building active sensors on activated surfaces [34]...... 32 Figure 2.4 Illustration of LoC concept [35]...... 33 Figure 2.5 Illustration of a microfluidic LoC device [50]...... 35 Figure 2.6 Traditional microfluidic device fabrication process flow[66]...... 37 Figure 2. 7 Chemical Structure of PDMS [71]...... 37 Figure 2.8 A paper based microfluidic device example and its main advantages [76]. ... 39 Figure 2.9 Paper based MEMS sensor with strain response [92]...... 40 Figure 2.10 Navier-Stokes equation indicating the forces acting on a fluid in a microchannel...... 41 Figure 2.11 Future Flexible devices along with IDTechEx Market Research displaying the impact of PE on future technology [98], [99]...... 43 Figure 2.12 Printing methods to create PE devices [101]...... 44 Figure 2.13 Printed capacitor on (a) PET and (b) Cannon Extra Glossy Paper...... 45 Figure 2.14 Venn-diagram demonstrating the significance of biochemical sensors...... 46 Figure 2.15 Wearable health monitors will lead to a boom in bio-medical sensor market as anticipated by CCC Insight market research [116]...... 47 Figure 2.16 Architecture of sensor - from analyte to end-user display...... 48 Figure 2.17 Difference between electrode functionalization (common) and dielectric functionalization (this dissertation)...... 49 Figure 2.18 Structure and working of a traditional parallel plate capacitor[178]...... 50 Figure 2.19 Structural comparison of a) Parallel Plate b) IDC[179]...... 50 Figure 2.20 a) Layout and b) Cross-section of an IDC [180]...... 51 13

Figure 2.21 Equivalent circuit of a 6-finger IDC [180]...... 52 Figure 2.22 Structure and design of a) IDC b) Serpentine c) Spiral and d) Meander - capacitive sensors [181]...... 52 Figure 2.23 Capacitive signaling using antibody-antigen binding induced charge transfer or redistribution [184]...... 53 Figure 2.24 Dissertation sensor design & development flowchart...... 54 Figure 3.1 March instruments CS-1701 RIE system...... 56 Figure 3.2 Contact mode AFM force spectroscopy process on an activated surface...... 57 Figure 3.3 AFM spectroscopy F-d curves comparing activated and unactivated surfaces using colloidal tips...... 59 Figure 3.4 XPS study of activated surfaces displaying variation in O1s and Si2p spectra...... 60 Figure 3.5 AFM topographical scans of a) Si control sample and plasma activated samples at power levels of b) 45 W, c) 100W and d) 250 W...... 61 Figure 3.6 Surface energy plots obtained from F-d curve averages for varying activation process parameters: time, pressure and flow rate...... 63 Figure 3.7 Correlation between contact angle and F-d curve averaging of surface energy for varying plasma power levels...... 63 Figure 3.8 Temporal study of surface stiction energy for two different Si surfaces activated at 100 W and 250 W. Inset gives the same data in linear y-scale...... 64

Figure 3.9 Optical image (30x) showing the impact of O2 plasma activation on PDMS surfaces used as substrates for inkjet printing...... 65 Figure 3.10 Improvement of features on activated glass substrates in Capacitors printed using Dimatix printer...... 65 Figure 3.11 Master mold and microchannel fabrication process flow...... 68 Figure 3.12 300µm channels bonded to glass substrate after surface activation...... 69 Figure 3.13 Warner Instruments 22mm diameter culture/imaging chamber...... 70 Figure 3.14 a) Bottom chamber and b) Top chamber of concentrated insulin 2-layer microfluidic device...... 70 Figure 3.15 a) Xerox was printer used to create the b) printed and cured paper microfluidic channels...... 71 14

Figure 3.16 Paper microfluidic capacitive-quad sensor showing the was printed microchannel side and printed sensor side...... 72 Figure 3.17 Arrangement of Syringe pump fluidic setup for microfluidic channel flow. 72 Figure 3.18 Screenshot of the fluid control interface and programmable flow control software...... 73 Figure 3.19 a) Ca2+ fluorescence response of 9 islets to glucose stimulation induced by forced oscillations. b) Average of overall response given in Fig 3.19a [202]...... 74 Figure 3.20 Sonoplot Microplotter used to print printed passive sensing elements...... 75 Figure 3.21 Fujifilm Dimatix Material Printer (DMP-2831)...... 76 Figure 3.22 Capacitive sensor integrated microfluidic device...... 78 Figure 3.23 Test setup used for sensor integrated microfluidic device characterization. . 79 Figure 3.24 Sensing Solution EVM GUI control panel and real-time data interface...... 80 Figure 3.25 Printed shield around the capacitor to eliminate environmental noises...... 81 Figure 3.26 Demonstration of improvement in signal quality with shield implemented in the design...... 81 Figure 3.27 Interface of "Data Cleanser" GUI developed using C#...... 82 Figure 3. 28 Spin-speed vs Thickness curves for Su-8 Negative tone photoresist ...... 203 Figure 3. 29 Process Flow of master mold creation process using soft-lithography...... 204 Figure 4.1 Glass surface with a drop of electrolyte...... 85 Figure 4.2 Silinol bonds present on a glass surface...... 85 Figure 4.3 Boundary conditions in an infinitesimal volume from wall of a microfluidic channel...... 86 Figure 4.4 Types of field in the microfluidic channel...... 87 Figure 4. 5 Optical view of the surface driven flow in a microfluidic channel...... 90 Figure 4.6 Cylindrical channel with a pressure driven flow...... 91 Figure 4.7 Hagen-Poiseuille’s Law - Pressure driven volumetric flux in a microchannel...... 91 Figure 4.8 EDL formation inside a microfluidic channel...... 94 Figure 4.9 Pressure driven flow in a microfluidic channel...... 96

Figure 4.10 Intercept of ρE and U distribution inside the Debye length...... 97 Figure 4.11 Dimatix printed capacitive-flowrate sensor...... 99 15

Figure 4.12 Capacitive-flowrate sensor test setup...... 100 Figure 4.13 Comprehensive response of varying flow rate test using DI water...... 101 Figure 4.14 Capacitive flow rate responses of DI flow with magnified images showing unique increasing area with flow rates (yellow – 0.5 mL/min, red – 1 mL/min & green – 2mL/min)...... 102 Figure 4.15 Comprehensive Capacitive-flowrate sensor response for varying ethanol flow rates...... 103 Figure 4.16 Comprehensive Capacitive-flowrate sensor response for varying 1% w/v Nickel Acetate in DI flow rates...... 104 Figure 4.17 Capacitive flow rate responses of Nickel Acetate flow with magnified images showing unique increasing area with flow rates...... 105 Figure 4.18 Comprehensive Capacitive-flowrate sensor response for varying 10% w/v Nickel Acetate in DI water flow rates...... 106 Figure 4.19 Overlay of applied pressure gradient pulse compared to the capacitive response for a 2 mL/min flow-rate...... 107 Figure 4.20 Capacitive flow rate responses of 10% w/v Nickel Acetate in DI flow with magnified images showing unique increasing area with flow rates...... 108 Figure 4.21 Capacitive-flowrate sensor response to flow of 0.1% w/v Nickel Acetate in Ethanol...... 109 Figure 4.22 Capacitive flow-rate response at fixed flow rate (2mL/min) for pH = 11 solution...... 110 Figure 4.23 Capacitive flow-rate response at fixed flow rate (2 mL/min) for pH = 3 solution with magnified view. The large lob is the result of initial transient and most likely caused by an air bubble that got stuck and released, which drops the capacitor...... 111 Figure 4.24 3D representation of LoC created with microfluidic channel capped over the printed passive electrodes ...... 114 Figure 4.25 Thermal response of a standard ceramic capacitor (C = 10nF)...... 115 Figure 4.26 a) Capacitance dependence on the frequency sweep for printed IDC’s used for comparison and control b) Printed IDCs on 4 different substrates...... 117 16

Figure 4.27 Experimental Setup: A – Printed Interdigitated Capacitor (IDC), B – Teca Hot/Cold plate, C – Computer control/monitor station and D – Capacitance Sensing Chip...... 118 Figure 4.28 Parallel plate capacitors - test setup...... 119 Figure 4.29 Parallel plate capacitive response to temperature sweep at different humidity levels...... 119 Figure 4.30 Humidity sweep at a fixed temperature (35ºC) parallel plate capacitors. As expected parallel plate shields the capacitors from any significant variations ...... 120 Figure 4.31 Filtered data showing the condensation effect observed on printed capacitors on glass substrate. Humidity level of 50% was higher than standard measurements...... 121 Figure 4.32 Capacitance vs temperature plots (polynomial fits) of printed capacitors on glass substrate with control air (no filler) and different polymeric dielectric fillers atop. Humidity level of 50% was higher than standard measurements...... 122 Figure 4.33 Capacitance vs temperature plots (polynomial fits) of printed capacitors on PA and PI substrates. Humidity level was standard 35%...... 123 Figure 4.34 Comparison of effects of different polymer fillers on the capacitance and thermal response...... 124

Figure 4.35 BaTiO3 filler thermal response enhancements: 44.7% results from

BaTiO3/PVDF nanocomposite filler (left) vs 37.3% from BaTiO3/PVDF thin film substrate...... 124 Figure 4.36 Thermal response of flexible IDC sensors printed on a) PET and b) PHD X98 paper substrate at humidity levels test1=35% and test2=50%...... 125 Figure 4.37 a) IDC thermometer quad sensor printed on PHD X98 glossy paper and b) its bending response...... 126 Figure 4.38 Response of a IDC thermometer on glass substrate under different humidity conditions...... 127 Figure 4.39 Inkjet printed - paper substrate capacitive response to temperature sweep at different humidity levels...... 128 Figure 4. 40 CapT plot used for comparative analysis...... 129 Figure 4.41 3D printed Microfluidic mold used to create the capacitive-count sensor. . 131 17

Figure 4.42 Significance of channel height design in a capacitive-count sensor...... 132 Figure 4.43 Capacitive-motion sensors Sonoplot printed IDCs are used in this work. .. 133 Figure 4.44 Proximity Sensitivity of Sonoplot capacitive-motion sensor to human finger that approach to the IDC in four intervals...... 134 Figure 4.45 Capacitive change between (a) DI (b) Ethanol & (c) IPA in comparison to control air capacitance. Pulses shows the sensitivity of the microfluidic system to finger taps to the top of the PDMS chamber...... 135 Figure 4.46 Capacitive-motion sensor detecting air pockets (1mm±0.4mm diameter) in a steady ethanol flow (100µL/min)...... 136 Figure 4.47 Capacitive-motion sensor detecting air pockets (4mm±1mm diameter) in a steady ethanol flow (100µL/min)...... 136 Figure 4.48 Capacitive drift in baseline capacitance caused due to flow dynamics...... 137 Figure 4. 49 a)Nanogap capacitors with Al-Au electrodes and 10nm gap in between the electrodes b) SEM image showing the 10nm gap between the electrodes [240]...... 138 Figure 4.50 Nanogap capacitors - capacitive response & temperature response comparison at different humidity levels...... 139 Figure 4. 51 Humidity sweep at a fixed temperature (35ºC) showing capacitive response of nanogap and paper capacitors (Inset - humidity sweep applied)...... 139 Figure 5.1 Depiction of dielectric loading method employed in capacitive-chemical sensor...... 143 Figure 5.2 Difference in adsorption vs adsorption (used in this study) process...... 144 Figure 5.3 Chemical structure of Sodium Silicate chain...... 144 Figure 5.4 Structure of Dowex G26 IER beads...... 145 Figure 5.5 Optical view (50x magnification) of DG26 H-form IER with chemical- termination representation...... 146

Figure 5.6 Response of uncured Na2SiO3 (liquid form) to varying Zn(CH3COO)2 concentrations...... 148 2 Figure 5.7 Response of cured Na2SiO3 to Zn + ions – Changing the ratio between DI water and 15% Zinc Acetate solution...... 149

Figure 5. 8 Concentration vs Capacitance relationship for Na2SiO3 capping layer for Zn2+ ion detection...... 150 18

Figure 5.9 Sensitivity of Na2CO3 capping dielectric to 15% Zn(CH3COO)2 drop test. . 151

Figure 5.10 Response of Na2SiO3+ Na2CO3 cured capping layer for 15%

Zn(CH3COO)2 drop test...... 152

Figure 5. 11 Concentration vs Capacitance relationship for Na2SiO3+Na2CO3 composite capping layer for Zn2+ ion detection...... 152 Figure 5.12 Zinc adsorbed capacitive ramp caused by a pressure driven pulse inside the microfluidic channel...... 154

Figure 5.13 Optical Images (10x magnification) of different Na2SiO3 capping layers cured at identical conditions...... 155 Figure 5.14 Bottom view of the IER’s used as a capping layer on the printed IDC...... 156 Figure 5.15 Two-layer microchannel integration with a) top chamber for fluid flow and b) bottom chamber for resin holder...... 157 Figure 5.16 Cross sectional sketch depicting the assemble of IER on printed sensor. ... 158 Figure 5.17 a) Concentration vs Relative Dielectric constant for ionic solutions [263] b) Adsorption timeline for the cationic IER [264], [265] ...... 158 Figure 5.18 Capacitive response of IER to Cu adsorption with the solvent being the dielectric...... 159 Figure 5.19 Three Chamber Design concept for metal ion detection using IER...... 160 Figure 5.20 Capacitive response of three-chamber measurement setup with inset1 (right) showing the ion exchange process setup and inset2 (left) showing the 2+ discolored/swollen IER upon adsorption of 1% w/v NiCl2 in DI water carrying Ni ions @ 50µL/min flow rate...... 162 Figure 5.21 Capacitive response of three-chamber measurement setup for 1% w/v of 2+ ZnCl2 in DI water carrying Zn ions @ 50µL/min flow rate...... 163 Figure 5.22 Capacitive response of three-chamber measurement setup for 1% w/v of 2+ CuCl2 in DI water carrying Cu ions @ 20µL/min flow rate. Note that the measurement for Cap1 occurs after the chamber1 was full, therefore remains on average constant...... 163 Figure 5.23 DG26 IER – Optical image (50x) of the three stages of IER – activated, ion exchanged, and regenerated...... 164 Figure 5.24 Screenshot of the Application developed (CIonXR)...... 166 19

Figure 5.25 Prediction of ionic concentration of solution using CionXR...... 167

List of Abbreviations pF – Pico Farad fF – Femto Farad nm – Nanometer mm – Millimeter µm – Micrometer cm – Centimeter nL – Nanoliter pL – Picoliter mL – Milliliter µL – Microliter mM – Milli Molar µM – Micro Molar fM – Femto Molar hrs – Hours min – Minutes s – Seconds M – Molar mM – Milli Molar ºC – Degree Celsius rpm – Rotations Per Minute mTorr – Milli Torr sccm – Standard Cubic Centimeter per Minute w/v – Weight per Volume v/v – Volume per Volume

Chapter 1: Introduction 1.1 Sensor Integrated Microfluidics – Lab-on-a-Chip With increasing capabilities and rapid innovations in the area of nanoelectronics, lab-on-a-chip (LoC) systems have become one of the core area of study for researchers to pursue compact solutions for biomedical problems and enhance the analytical capabilities of integrated electronics interfacing liquid samples, an example of which is provided in Fig.1. Microfluidics is the key platform in bringing the concept of LoC into existence that had major impact on bio-chemical research, drug-development and point-of-care health care systems, especially in the last two decades. Along with the strides in microfluidic devices, the field of sensing has been continuously improving in all aspects including accuracy, sensitivity, size, cost and user friendliness. In particular, printed low-cost electronics has established its own prominence and paved the way for novel electrical, optical, magnetic, electro-chemical sensing devices built affordably on non-conventional surfaces such as glass, polymers and paper. Thus, today we are presented with a wide range of sensors of varying types and capabilities that can and are being applied to a multitude of biomedical and biochemical studies. However, since the original justification of these products were affordability and high-level integration, it is imperative to identify and exemplify low-cost, scalable, low-power and CMOS- compatible sensing solutions that can be utilized on equally low-cost and flexible substrates. This dissertation identifies capacitive sensing as this ‘unique’ sensing platform that can deliver in all fronts (low-cost, scalable, low-power, digital-CMOS compatible) of performance and offers novel capacitive solutions on a wide range of physical (temperature, humidity, flow, motion) and chemical (pH, Zn, and heavy-metal) sensing vectors using only printed capacitive elements. Besides many exciting sensors that printed electronics can offer, capacitive sensing is especially suitable for biomedical applications where the changes in chemical and physical observables such as solvent chemistry, concentration, flow, temperature or particle/cell velocity, structure and behavior are often the main interest. 22

Figure 1.1 (a) Comparison of traditional lab analysis vs LoC analysis (b) An illustration of LoC blood test kit [1], [2].

Capacitive sensing has the potential to accurately and directly monitor such changes with relatively low power using the change in the electrical properties of the solutions, proteins, cells and tissues via the dielectric polarizability of the media and/or charge redistribution on biologically relevant surfaces and time scales. Hence the field of capacitive bio-sensing has become a hot-bed of interdisciplinary activity in recent years, where (electrical) engineers empower biomedical community with ever compact, capable and low-cost sensors that can continually enhance their capabilities and introduce entirely new class of tools or methods in research. Research efforts described in this dissertation target similar gains by using novel printed capacitive sensors integrated with microfluidic devices. More specifically, the dissertation introduces the toolset developed so far to build capacitive-sensor enriched micro-fluidic devices to monitor physical and chemical parameters. Such capacitive sensors integrated with compact microfluidic devices would benefit the field of biomedicine in numerous ways. Apart from the field of biomedicine, these sensors would impact a variety of other research fields such as wearable health monitors, smart bandages, portable point-of-care instrumentation. By tuning the type, sensitivity, and location of dielectric layers and electrode geometry in capacitive sensors, interfaced to powerful and compact digital electronic systems, it is envisaged that we can provide additional analytical means to design and build low-cost LoC tools for biomedical and biochemical analysis that can be accomplished with minimal manual intervention and high efficiency.

1.2 Motivation Even though conventional fabrication processes have ruled the world of electronics for decades now, the goal of fabricating sensors affordably at large scale necessitates a switch from the traditional methods used for fabricating active and passive electronic components and devices. Printing has evolved to be one of the major processing technique in creating such microelectronic devices and structures. Today’s advanced material/solution printing systems can be used to fabricate passive R, C, L, structures, RF antennas and waveguides, optical elements and active devices like a transistor that act as the main sensing element in diverse application. Thus, the main motivation behind this work is to explore the limits and expand the capabilities of one of simplest yet most capable of such printed biosensors, planar interdigitated electrode capacitors, for microfluidic platforms. This opens the door to analyze and create a low- cost platform to study innumerable biomedical and biochemical processes. Although the field of biomedical engineering and biological sciences are not lacking such basic sensors and devices, many of them require expensive optical/fluorescent detections tools, highly specialized surface functioning techniques, demand the use of electrochemical amplifiers or complex fabrication sequences that cannot be scaled up for data-fusion driven and wearable LoC systems era. In this new era what is required is to integrate multitude of sensors that can work together to build multi- dimensional complex vector space which can be effectively monitored for more reliable decisions using readily available low-cost and wireless computational power and can benefit from many mature machine-learning algorithms. This would lower the resources and reaction time in many applications, from wearable health monitors and point-of-care systems to LoC systems in analytical labs, that perform wide range functions including chemical and protein detection, cell culturing, counting, sorting and drug delivery, which are few of the major experiments performed in a biomedical lab on a single glass slide, as shown in Fig. 1.2. These devices would change the way in which the modern-day biological lab would work, reducing waste of expensive analytes, lowering overall space and cost for instrumentation, reducing analysis time, improving process control, speeding up analysis by tremendous parallelization (high throughput analysis), and creating a safer (sealed) platform for chemical and biological studies. 24

Figure 1.2 Illustration of LoC concept [3].

Even the simple sensor-less or single-sensor based microfluidic systems can offer many of the benefits listed above. However, the truly capable LoC systems of tomorrow require many sensors which must be low-cost and scalable to be globally and practically beneficial. By developing novel capacitive sensors and optimizing them for chemical and physical sensing problems on simple, efficient and accurate microfluidic platform, we not only can expand the capabilities of LoC systems and reduce their cost, we can also ride the new wave of sensor-fusion algorithms that shift the focus from pursuit of ‘a perfect sensor’ to study of ‘cooperative sensing’, where decisions are better than the sum of parts. 1.3 Sensor Fusion Era Sensor fusion deals with the concept of integrating and co-processing of data from multiple sensors on the same platform with a common sensor processing unit in contrast to using a single high-accuracy sensor. The aim of this technology is increasing the reliability of the sensor system by multiplexing data driven from many sensors [4]–[6]. Current efforts in many complex problems such as self-driven vehicles, complex life- style-driven conditions like diabetes and smart-city projects have shown that better decisions are possible when data from multiple sensors modalities are available and context for each is well understood, even though not all sensors provide the most accurate data all the time. The future of advanced sensing is focused towards this concept (Fig. 1.3). The immense potential of capacitive sensors is that they can be made in many size 25 scales and harbor multiple sensing modalities, all of which can be turned to simple capacitive measurement that CMOS electronics excel at. Although no specific algorithmic work is undertaken in this work for sensor fusion, the very premise of building and integrating multiple chemical and physical sensors all directly interfaced to CMOS electronics make capacitive sensing an ideal candidate to focus for this upcoming sensor-fusion era. Thus, a secondary motivation of this research work is the development of multiple cost-effective, simple, compact and flexible capacitive devices that can enable LoC systems to benefit from sensor fusion. It does implicitly offers capacitors as the building blocks of a scalable sensor fusion platform for LoC systems of tomorrow that are expected to define biomedical engineering in the 21st century.

Figure 1.3 Illustration of concept of sensor-data fusion for effective health monitoring [7].

1.4 Research Goals & Accomplishments 1.4.1 Research Goals Besides identifying the potential and utility in using capacitive sensors beyond touch/proximity sensing and indicating their unique position to realize sensor fusion 26 especially for the LoC systems, there are also practical goals and achievements associated with this research work. First and foremost, the present work aims to show that capacitive sensing is not fully explored at present and novel sensing solutions and vectors can be pursued using relatively simple printed capacitors. Thus, it explores several innovative ways that capacitors can be used to sense novel chemical and physical observables, by monitoring changes in the dielectric media or immediate coatings. In doing so it also has uncovered a number of other important and novel process and software developments. Thus, we can list the specific objectives of this PhD research in the following fashion.  Process development for novel capacitive sensors based on interdigitated electrodes printed using novel metallic nanoinks on both relevant inorganic (glass and silicon) and low-cost organic flexible (polymer and paper) substrates.  Novel sensing devices based on flexible capacitors, including quantitative determination of temperature, humidity, heavy metal and specific ion (Protons and Zn2+).  Demonstration and deployment of multiple capacitors integrated onto flexible microfluidic systems for low-cost and scalable applications. 1.4.2 Accomplishments Driven by above goals, this research has enabled us to achieve a wide range of practical accomplishments, which will be expounded in the dissertation in relevant sections. They include all aspects of sensor development, from surface science to printing and electrical characterization to modeling, which are grouped below for brevity and introductory purposes:  A novel surface energy characterization method for plasma activated surfaces using “AFM Force Spectroscopy” technique.  Optimization of the plasma activation process for PDMS microfluidic design and efficient printing of nanoink used by two advanced printing systems (Sonoplot Microplotter 2 capillary printer and Dimatix 2831 flatbed Inkjet writer)  Design and development of unique PDMS and paper microfluidic elements for capacitive monitoring via novel 3D printing tools. 27

 Development and deposition of novel dielectric coatings for effective sensing of physical (temperature, humidity and motion) as well as specific ionic charges such as H+ and Zn2+ ions.  Establishment of programmable fluid delivery system for microfluidic testing and portable low-cost capacitance to digital converter cards for LoC development  Code development of several GUIs for mathematical modelling to predict the interdigitated electrode capacitance for given electrode geometry and substrate and data post-processing and filtering.  Exploitation of electroosmotic effect to capacitively capture fluid flow and development of novel capacitive-flowrate sensor to detect flow rate of polar ionic fluids and its pH  Development of a novel three chamber microfluidic design to detect heavy metal cation sensors via ion exchange resins (IER), fluid-dynamics and capacitance measurement, along with the MATLAB GUI that aids its data analysis. 1.5 Dissertation Characteristics & Organization This dissertation has a number of unique characteristics that sets it apart from prior works. The majority of existing works [8]–[12] focus on only one type of capacitor design (parallel plate, interdigitated), substrate (silicon, glass, polymer) or application (ion/protein/aptamer sensing [13]–[16], motion detection [17]–[20], or humidity [21]– [23]), which severely underestimate the great potential capacitive sensing presents. Also, the majority of these works rely upon expensive CMOS/MEMS processes to produce high quality capacitors that cannot be scaled in area, type of dielectric used, or the substrate employed. Instead of focusing on one application and design, we look into a depth and breadth of capacitive sensor applications and present a compendium of capacitive sensors on interdigitated printed capacitors suitable for wide-scale and low- cost LOC applications as well as wearable monitors. The following chapters present this unique and broad account of capacitive sensing. In particular:  Chapter 2 details the fundamentals of surface study, microfluidics, printed electronics, LoC and biosensors. 28

 Chapter 3 explains the integration process to create a working sensor and provides an in-depth design and development procedure for surface study, microfluidics, printed sensing elements.  Chapter 4 details the list of capacitive-physical sensors which details the capacitive sensors developed to measure physical parameters like flow-rate, pH, temperature, humidity and motion.  Chapter 5 explains the design, development and testing of capacitive-chemical sensors which explains the capacitive sensors developed to detect chemical parameters like heavy metal cations.  Chapter 6 includes a brief summary of this research along with the potential future developments and applications.

Chapter 2: Background This chapter aims at introducing the basic devices, research tools. The evolution and history of each section of work is detailed along with state of art research and development in every field.  Section 2.1 intends the readers to get an idea of what surface activation and the importance of this process in other segments of this project which includes printed electronics, microfluidics and sensor integration.  Section 2.2 provides an overview of the concept of Lab-on-a-Chip (LoC) and microfluidic devices, its importance in field of sensors and different types of microfluidic devices with a brief explanation of fluid mechanics.  Section 2.3 introduces the process of printed electronics and its importance in biosensors along with a comparison of various printing techniques that exist.  Section 2.4 details the state of art of biosensors, providing the reader an insight of integration of LOC and printed electronics in creating the biosensors in existence.  Section 2.5 particularizes on the capacitive sensors used in this project, the basic working principle, analytical approximations and the current devices that use this sensing technique. 2.1 Nanoscience and Surface Study Since nano represents length dimension measurement unit at the scale of 10-9 m, nanoscience refers to the science of materials in this regime. The properties of matter at this scale is distinct from its bulk counterparts, knowledge of which allows us to pursue novel solutions to common problems from a fundamental understanding of building blocks of matter. Humans have always learnt from nature since they walked the earth and the most advanced technologies we have developed have their roots often in the most basic sciences on the materials. For example, the most advanced integrated circuits (IC’s) start their origin as sand converted to pure silicon wafers, which are further transformed into IC’s with complex technological processes. They are fabricated with top-down approach that starts from bulk materials and arrive at intricate small devices at lower scale. This is expected to reach its limits in the next decade. Giving way to the bottom-up approach, already revolutionizing the field of molecular electronics. Thus, going from elemental materials to complex devices is an upcoming theme also for modern 30 electronics. According to Eric Drexler, the best way to comprehend the existence of modern technology is the ability to assemble matter using this bottom-up approach (Fig. 2.1) which is known as molecular nanotechnology [24].

Figure 2.1 Comparison of methods to create nanomaterials (Top-down vs Bottom-up approach) [25].

Characterization of the surface plays a major role in a broad spectrum of research areas. It’s importance in development of any kind of LoC’s is also a separate stream of research. Engineering a surface involves a deep understanding its physical or chemical properties and typically is very application specific. Its significant impact and vast research can be seen from the study of surface modification in Si-Si surface bonding [26] in the field of engineering and cell adhesion studies [27] in the field of biology. Besides these applications relevant to our research work, there are innumerable number of other research fields impacted by surface modification, which includes optics, civil engineering, mechanical engineering in a variety of applications. Today , even solar cells and LED’s utilize surface modification as a core part of their experimental process [28]. Most surface characterization include altering the roughness or the bonding on the surfaces which are modifications done in a few nanometer scales. But as the expertise grew, the surface studies have advanced into altering individual atoms on a sub 31 nanometer scale (<1 nm) using self-assembled atomic monolayers or the scanning probe tools such as STM to create specific bonds on the surface [29]. 2.1.1 Activated Surfaces Activated surfaces refer to enhancing propensity of a surface toward a specific physical or chemical interaction. Specifically, in terms of printed electronics and microfluidics fabrication it refers to enhancing hydrophobic or hydrophilic properties of a given surface before a subsequent deposition, bonding or printing process. Plasma treatment is one of the preferred methods of altering the surface property of the material of use. This is because this process is less toxic and avoids the usage of corrosive and dangerous chemicals such as piranha solution [30]. However, there are a variety of plasma processing conditions for activation, it is essential to study optimal conditions for best activation levels. Numerous studies have been performed over the past two decades on changing the surface properties of such plasma activated surfaces and its applications in device fabrication. For instance, Alam and coworkers’ study on this lifetime via XPS data showed the creation and deterioration of specific bonds leading to the change in surface properties over a given period of time [31]. Thus, in addition to creating hydrophilicity by altering surface bonds, it is also crucial to know the lifetime of such bonds created on the surface under different processing and storage conditions. In other words, temporal changes needs to be a main part of surface study for any optimized plasma activation. Surface energy is one the main properties which needs to be analyzed and studied when there is a change of surface properties. Activation of a surface breaks certain bonds on the surface of the Si which has a few nm of native oxide. The chemical changes caused on the surface have been studied in the past and the major contribution towards hydrophilicity has been reported to be due to the formation of dangling Silinol (Si – O – Si) bonds (Fig. 2.2) on the surface post-activation.

Figure 2.2 Representation of Silinol Bonds on Glass substrate [32].

A detailed analysis and optimization of physical, chemical and mechanical properties of the activated surface is key in determining the effectiveness of the plasma processing for a given application. Factor such as surface roughness may also affect surface energy in plasma-modified Si and SiO2 substrates which have been under study on a large scale for wafer bonding process [33]. Hence surface modification using plasma activation, should also include a comparison of surface roughness after a plasma treatment. Plasma activated surfaces have been the building blocks for fabrication of various sensing and energy harvesting devices (Fig. 2.3). Surface modification and surface chemistry plays a significant role in microfluidics and sensor development. An extensive and novel approach in characterizing the activated surfaces to precisely control the surface energy during the surface activation process has been identified and studied-in depth in this dissertation.

Figure 2.3 Building active sensors on activated surfaces [34]. 33

2.2 Lab-on-a-chip (LoC) – Microfluidic Devices LoC devices have been a bridge between academic research and commercialization in recent years. As the name indicates, LoC aims at integrating several experiments and characterization techniques performed in a research laboratory on to a single compact handheld device of dimensions ranging from a few mm to a few cm as illustrated in Fig. 2.4.

Figure 2.4 Illustration of LoC concept [35].

LoC is a blend of microfluidics and sensing technologies (electrical, optical, mechanical and electromechanical) on a single device [36]. Advancements in MEMS has also helped to significant developments in search of low-cost, reduced analyte volume (down to pL), high throughput, fast, portable, sensitive and automated LoC devices [37]– [39]. Thus, LoC system development can be considered as the ultimate playground of heterogenous integration for analytical and sensing technologies. The application of LoC devices is multifold and ever-expanding with the essential importance given to miniaturization and improvement of point-of-care diagnostic devices useful for biological, chemical and environmental studies [40]. The existing LoC technologies along with the impact it has created on the product is detailed in Table 2.1 [41].

Table 2.1 Product-Technology impact analysis for LoC (L-Low, M- Medium & H-High). Product Feature Technology Analysis Sample Analysis Portable Easy to use Diseases time Volume cost Size detected Microfabrication L M H H M M MEMS M M H H M M Microfluidics M M M H M H Nanosensor M L L L M L DNA Technology M L L L H H Sample Prep M H M H H M

From table 2.1, it can be observed that the number of applications of microfluidic devices are comparatively larger than other alternate technologies and is on par with the DNA Technology. These technologies do not have to be in isolation. In fact in this dissertation, an attempt to integrate bio-nano sensors and microfluidics with aid of microfabrication has been attempted, so it can be possible to create novel LoC devices with capability to study physical and chemical properties analytes. In creation of successful LoC devices, microfluidics plays a vital role. Microfluidics have developed to be an active research area on its own, especially with the recent expansion of paper-based devices that can handle and wick fluids in low-cost applicatons. The advent, evolution, state-of-art and applications of microfluidics is discussed in the subsequent sections. 2.2.1 Evolution of Microfluidic Devices Microfluidics is a field of science which requires development of micron-scale channels capable of handling “micro” volume of solutions (nL to pL), which is an essential requirement in portable wearable health monitors and point-of-care applications (PoC) [42]. Many PoCs aid in biomedical and healthcare practices by speeding up the analysis of patient samples to detect disease specific biomarkers in blood or presence of certain molecular signatures [43]–[45]. In addition, they also aid in environmental safety by detecting airborne or waterborne microorganisms [46]–[48]. Furthermore, harmful chemicals in food and water supply can be efficiently sensed [49]. What is common to many of such devices is the presence of intricate microfluidic devices that enable their operation and handle fluid transfer and processing. 35

The concept of microfluidics originated in the early 1980’s. As the name indicates, the fluid volumes when handled in such small volumes exhibit unique behavior in comparison to a normal fluid. Manipulation and control of fluids at the nanoliter and femtoliter level leads to innovation which has been the main advantage of using the concept of microfluidics. A microfluidic chip (Fig. 2.5) consists of microchannels engraved or molded on to the surface and may involve separation, mixing, manipulation and driving of fluid in the channels to create high-throughput, cost-effective, portable LoC devices.

Figure 2.5 Illustration of a microfluidic LoC device [50].

The evolution of microfluidic devices has been very fast in the last two decades in terms of miniaturization and multiplexing, especially using multi-layer fluidic networks. Using pneumatic control lines and flexibility of PDMS layers, it has advanced to a such a level that today single-cell manipulation, PCR of DNA/RNA and protein crystallization can be accomplished. Other significant applications that exploit advancements in large- scale microfluidic integration include cell culturing, protein analysis and/or synthesis, cell sorting and ordering and biological screening [51]. A comprehensive review of developments in specific areas of application can be found in references [52]–[57].

2.2.2 Types of Microfluidic Devices The formation of microfluidic devices involved creation of a master mold which would be a reverse replica of the actual microfluidic channel designed. One of the well- known methods for creation of this mold is through soft-lithography techniques using a negative-tone photoresist. Since the late 1980’s, there have been multiple methods to create this master mold, which has been discussed in Refs. [58]–[64] with considerable detail. Recently, the creation of master mold has been simplified considerably, after the use of 3D printing technology. Although a large number of polymeric materials have been explored in creation of microfluidic channels, PDMS has always been the most preferred material in fabrication due to its physical and chemical properties detailed in the next section. The compatibility of PDMS to CMOS devices is one of the main reasons behind the success of this polymer in the ever-growing microfluidic LoC field. In addition, with the commercialization of microfluidic LoC moving towards flexible devices, paper microfluidics have entered the world of LoC microfluidics and is considered as a more compact, foldable and flexible alternative to the PDMS microfluidics. Due to its optical clarity and ease of processing, PMMA has also been used in microfluidics applications where elastic nature of PDMS is a concern. The entire process of microfluidic channel formation can be divided into three sections [65]: a) Master-mold creation; b) Microchannel fabrication and c) Microfluidic bonding and interfacing. The development and methods evolved in creating such LoC devices is detailed in Fig. 2.6. With the advent and adaption of 3D printing many of these complicated and expensive processes can be avoided, as indicated in Figure 2.6 with the bold arrows. Augmenting this process and introducing electrodes for sensor connections is not trivial and should be done with care to avoid leakage and impact on flow dynamics. The electrical interfacing of electrodes to fluidic channel requires a lot of precision and critical design considerations which are detailed in the forthcoming sections. Much-like the 3D-printing of molds, printed electrodes also cut-down on the complexity and cost implied by Figure 2.6.

Figure 2.6 Traditional microfluidic device fabrication process flow[66].

2.2.2.1 Traditional Microfluidic Devices Polydimethylsiloxane (PDMS) (Fig. 2.7) is one of the actively used materials in microfluidics accounting for its excellent physical and chemical compatibility in fabricating CMOS compatible fluidic devices. The PDMS elastomer, which is commercially available, consists of a base liquid and a curing agent. Cross-linking between the base and curing agent creates leads to the formation of a flexible elastomeric microfluidic material with physical and chemical properties detailed in Table 2.2 [67]– [70]. Flexibility and optical clarity as well as chemical and thermal stability are the main reasons behind its adoption.

Figure 2. 7 Chemical Structure of PDMS [71].

An additional advantage of PDMS is its compatibility to both biomaterials, fluids and CMOS process integration. Inertness to mechanical, chemical, environmental and 38 physical conditions have made PDMS an outstanding material of choice for LoC microfluidic device development used in bio and environmental sensors.

Table 2.2 Properties of PDMS Property (Physical/Chemical) Comments Optical Transparent in range of 240 nm to 1100 nm Thermal Insulator – Thermal conductivity (0.2 W/mK) Electrical Insulator – Breakdown Voltage (2e7 V/m) Mechanical Elastomer – Young’s Modulus (750 KPa) Surface Hydrophobic – Surface energy (20 erg/cm2) Air – Permeable Permeability Water – Impermeable Reactivity Inert to chemicals

2.2.2.2 Paper Microfluidics In many LoC applications, the analytical system must be extremely affordable since it is intended for a single use. This may be necessary to avoid contamination or biohazards, which can increase the cost of medical care considerably. As an alternative to PDMS and response to these requirements, use of paper for microfluidic systems have also become popular in the last decade. The micro-porous and fibrous structure of paper leads to whisking of water and many other polar fluids, which make it especially attractive in low volume of solutions with an active pump for fluid motion. Microfluidic paper based analytical devices, an example of which is shown in Fig 2.8, is composed of cellulose mostly, and hydrophobic barriers to divert fluids. Such a structure can be subsequently processed via printing techniques to form electrical sensors in biosensing devices, which is one of main advantages for paper to be used in microfluidic sensor applications [72]–[75]. The main disadvantage of paper is that it is not a medical grade material.

 Biodegradability  Light Weight  Wide availability  Cost-effective (lowest for microfluidics)  Hydrophilicity (Water Wicking)  Electrophoretic Capability  Disposable  Flexibility  Low dielectric substrate

Figure 2.8 A paper based microfluidic device example and its main advantages [76].

A paper microfluidic device is created by several techniques which focus on creating hydrophobic barriers to aid the flow of fluid through the hydrophilic paper material. Wax printing, negative tone lithography and plasma activation are some of the major techniques involved in creating this hydrophobic surface on paper [77]–[86]. Paper based sensors have been explored in the past with the importance given to the strain encountered by the flexible substrate. While using passive sensing elements, the strain induced by the paper should be considered as a parasitic effect during the analyte measurement. This has been incorporated in electrochemical and resistive sensing techniques [87]–[91]. An illustration of electrochemical sensor and effect of strain on the flexible paper substrates is shown in Fig. 2.9 (Liu et al.). Paper based microfluidic devices have been also incorporated into this dissertation, since they are ideal for printing and low-cost applications. However, the natural surface roughness and strain is always a concern in characterizing a passive sensing element (capacitor) during analyte detection (physical or chemical). This is why, besides standard device structures, alternative physical and geometrical arrangements and redundancy should be explored as approaches to improve the accuracy of novel paper- based capacitor sensors as explored by four-channel parallel measurement in Chapter 4.

Figure 2.9 Paper based MEMS sensor with strain response [92].

2.2.3 Fluid Mechanics in a Microfluidic Channel The behavior of fluids at nanoscale and confined to a microchannels is very different from normal flow of bulk fluid. The fluid mechanics, dynamics and behavior changes at microscale have been well studied and exploited to create unique LoC devices over the years. Surface tension plays a dominant role as opposed to gravity in such small volumes of fluids that can be subjected to also very large pressure differentials. In fact, this small volume and large flow rates constitute some of the major advantages of microfluidic LoC devices. As a result, expensive analytes may be conserved and tested in high speeds which is made possible by appreciation of fluid flow in such confined volumes. A well-scaled microfluidic LoC device can easily result in massive (1/1000 or better) reductions of fluid volume under use, that can be especially important for drug development and testing. In any confined basic microfluidic or capillary flow, two of the prime parameters with significant impact on the flow are:  Reynolds Number  Capillary Number Reynolds number compares the inertial and viscous effects present in the fluid flow and determines the flow patterns in a microchannel. Whereas, capillary number compares the viscosity of the fluid to the surface tension of the substrate-fluid interface and determines the elasticity and capillary effects including the droplet size on the surface 41

[93]. Reynolds number can be represented as the ratio of inertial and viscous forces (Eqn. 2.1) 푓 푅 = (2.1) 푓 The Reynolds number for water in a microfluidic system with specific ranges of design considerations is given in Table 2.3.

Table 2.3 Calculation of Reynolds number for a typical microfluidic channel. Fluid Water Viscosity 1.025 cP Temperature 25ºC Density 1 g/mL Radius/Height 1 µm – 100 µm Velocity of fluid 1 m/s – 1 cm/s Reynolds Number 10-6 - 10

The Navier-Stokes equation is the primary rule governing fluid dynamic properties in fluids and also holds for a microchannel (Fig. 2.10). It expresses the momentum conservation in a capillary fluid flow.

Figure 2.10 Navier-Stokes equation (vector form) indicating the forces acting on a fluid in a microchannel.

A low Reynolds Number, which is the main regime applicable to microfluidic applications based on very small device dimensions and large pressure differentials, indicates that the viscous forces dominate the inertial forces and the LHS of equation shown in Fig. 2.10 cancels out resulting in a linear flow.

Electro-osmotic flow is one of the main condition in characterizing dielectrophoretic effects (DEP) [94], [95]. Surface charge manipulation to control the electroosmotic flow has been vastly explored in the past [96]. This approach also enabled the design of electro-osmotic pumps using the concept of fluid dynamics to drive a fluid flow at controlled rates in a microchannel with biasable electrodes. However, the concept of exploiting the electroosmotic properties of polar fluids to predict the flow rate of a solution in motion has never been explored in the past. In this dissertation, the electrokinetic/electroosmotic effects in combination with a surface induced electrical double layer (EDL) has been utilized to develop a capacitive flow-rate sensor which is detailed in Chapter 4. 2.3 Printed Electronics Printed electronics (PE) came into existence in early 1960’s when printed circuit boards with flexible characteristics were considered as suitable alternatives to develop future electronic devices. Albert Hanson is considered as the first person in the world who discovered the concept of printed circuits [97]. However, rapid expansion of silicon micro-chips and resulting rapid integration of electronics at chip level took away this early interest. With the Moore’s law remaining in effect for five-decades, scaling down of devices from macroscale to nanoscale, the traditional electronic devices and components is reaching to its natural limits today. Thus, interest has shifted from pure miniaturization to utility, portability and wearability of electronic devices. At this critical juncture, PE plays a crucial role since it offers alternative ways to morph existing electronic systems into daily items and come up with entirely novel applications such as flexible displays and wearable electronics. Hence, as rapid growth of conventional electronics due to Moore’s scaling slows down, PE has created a new revolution and a commercial outburst in research and industrial sectors with its key impact felt in as many field as sensors, photovoltaics, battery, displays and the traditional CMOS devices. With the continuous requirements resulting ever more capable printed systems every day and growing demand, PE is likely to become a major driver in electronics. A forecast of evolution and influence of PE for future technology is provided in Fig. 2.11.

Figure 2.11 Future Flexible devices along with IDTechEx Market Research displaying the impact of PE on future technology [98], [99].

PE offers novel pathways to successfully fabricate light-weight, flexible, cost- effective and scalable electronics. Besides offering entirely novel devices, PE has also helped conserve the resources needed to fabricate conventional electronic devices. In contrast to the conventional fabrication processes like lithography, which is a subtractive process, PE is an additive process for electronic device fabrication. Moreover, PE proves efficient in comparison to traditional semiconductor fabrication by providing inexpensive and high throughput devices using roll-to-roll (R2R) manufacturing toolset. In fact, R2R can drop the fabrication costs even further for PE, thus providing a huge boom for the commercial production of electronic devices. PE is turning out to be a huge success in development of directly printed sensors, photovoltaics, LED, battery and CMOS devices [99]. Its main drawback is limited resolution in high-speed printing and manufacturing, which is typically limited to ~50 microns. 2.3.1 Types of Printing PE development process can employ a variety of methods to create the desired devices. The selection of application specific printing method is dependent upon the resolution of device structure. A list of existing printing methods is shown in Fig. 2.12. For instance, the resolution of the inkjet printable device depends on the volume of ink jetted out of the nozzle. The latest devices have scaled down to droplet sizes in the range of fL using nanoplotting tools and custom designed nozzles [100]. The two major categories in printing are contact and non-contact modes. The printed technology used in this dissertation uses a piezoelectrically driven microplotter (Sonoplot®) which can perform both contact and non-contact printing to achieve a resolution in the order of few 44 microns. Sonoplot uses custom-made tips with diameter decided in accordance to the feature size requirements. The other alternative approach used in this work is a commercial material inkjet tool (Dimatix DMP-2831), which delivers the deposited materials in small droplets.

Figure 2.12 Printing methods to create PE devices [101].

One of the main fields of growth for PE is in developing low-cost and flexible devices which are compact and disposable. With its capability to print on different materials, PE changed the structure of sensors developed today and is the main reason for flexible sensors gaining prime importance in the healthcare industry. PE has been explored by a number of research groups around the world to create functionalized biosensors, transistors, LED’s, high-resolution printed patterns, bioelectronics, biological microarrays, stretchable electronics and field-effect transistors (FET’s) [102]–[112]. However, the application of PE to print high-resolution interdigitated capacitors (IDC) for a wide range of sensing applications by functionalizing the dielectric layer between the capacitance has not been accomplished yet. This dissertation focusses on developing such novel biosensors and chemical sensors interest to LoC/PoC systems and environmental applications using printed capacitive sensors integrated with microfluidics. 45

The two printing tools used in this work has proven ability to print on flexible substrates such as PET and Cannon extra glossy paper. Fig. 2.13 shows the capacitors printed on flexible substrates via Sonoplot. The typical range of such printed capacitor ranges from 500 fF to 1nF, depending on its design and area. The capacitance values can be designed to match the application requirements by varying the length, distance between and the number of fingers printed. The working of such printed and annealed capacitors should be tested and confirmed using an LCR meter, especially on paper surfaces that have rough surfaces. For glass-PDMS microfluidics, IDCs were printed on a glass slide.

Figure 2.13 Printed capacitor on (a) PET and (b) Cannon Extra Glossy Paper.

2.4 Bio & Chemical Sensors Professor Leland C. Clark Jr. (The 2005 Russ Prize recipient – Ohio University) is called the father of biosensors for his invention of the first glucose monitor. The Clark Oxygen electrode invented in 1954 is referred to as a setpoint to measure the oxygen levels in blood till date [113], [114]. Biochemical sensors are key components today and are an integral part of extended fields of research as shown in Fig. 2.14.

Figure 2.14 Venn-diagram demonstrating the significance of biochemical sensors.

The historical milestones for the development and evolution of biological and chemical sensors are detailed in Table 2.4 [115].

Table 2.4 Evolution of Bio and Chemical Sensors [115]. YEAR Development YEAR Development 1916 First Report of Protein Immobilization 1975 First microbe-based sensor 1922 First glass pH electrode 1975 First protein-binding Biosensor 1925 First blood pH electrode 1975 First Immunosensor 1954 Oxygen electrode 1979 Surface acoustic wave sensor 1954 pCO2 electrode 1980 Fiber-optic pH sensor 1962 First Amperometric Biosensor 1982 Fiber-optic glucose biosensor 1962 Lipid Bilayer Membrane Generation 1983 Molecular level device fabrication 1964 Piezoelectric Quartz sensor 1986 First tissue-based Biosensor 1969 First Potentiometric Biosensor 1987 First receptor-based Biosensor 1974 First commercial glucose analyzer

Healthcare industry is one of the primary commercial sectors that will benefit from developments in nanomaterials and PE technology empowering creation of novel bio-chemical sensors. In addition to hand-held compact medical LoC and PoC systems for traditional health care applications such as disease detection, analysis and drug delivery, these sensors drive today entirely novel application areas. As the technology is moving towards flexible and wearable sensing technology, preventive health monitoring systems and fitness tracking have become a part of modern life, almost like a necessity 47

(Fig. 2.15). In fact, as summarized in Fig.2.15, wearable health monitoring market is expected to grow exponentially in the next five years (2019-2024). High quality analytical tools and low-cost measurement techniques developed mainly for LoC/PoC systems, will gradually find their way to such wearable devices thanks to affordable and flexible PE technology.

Figure 2.15 Wearable health monitors will lead to a boom in bio-medical sensor market as anticipated by CCC Insight market research [116].

2.4.1 Types of Biosensors A biosensor includes a set of components to detect the analyte and turn it into an end-user display (Fig. 2.16). Apart from healthcare application, the demand for bio and chemical sensors have been escalating in field of biomedicine, tissue engineering, food industry (bacterial and fungal monitoring) and environmental applications (heavy metals, chemical and pathogen detection) [117]. 48

Figure 2.16 Architecture of sensor - from analyte to end-user display.

The first step in development of a biosensor is to determine the analyte to be detected. This could be a biological cell, gas molecules, proteins, metal ions, pH, bacteria, or nucleotides (DNA/RNA) [118]–[139]. Once the target is determined, there must be a receptor/binder/aptamer which specifically attracts the target molecule. This step is called functionalization, which can be categorized into polymers, metal binding groups, chelators, proteins, RNA’s, antibodies and enzymes [140]–[148]. The functionalization is typically done on the surface of the sensor electrodes. However, it is often a demanding chemical process that may lose its effectiveness in air and overtime. It is especially useful when a very specific molecule needs to be detected but could be expensive and limited for the same reason. In this work, we avoid electrode functionalization and instead focus on the dielectric layer (medium between the printed sensor) to induce or monitor electric charge transfer. This charge is then transduced into electric signal using capacitive sensing elements (Fig. 2.17).

Figure 2.17 Difference between electrode functionalization (common) and dielectric functionalization (this dissertation).

There are a number of ways to sense the analytes which include optical, electrical, electrochemical, thermal, chemical, mechanical and piezoelectric sensing [126]–[141]. Each kind of sensing has specific transducing devices to convert the detected signal into a set of raw data specific to the sensing mechanism. The raw data obtained has to be post- processed in any kind of sensor to improve SNR and eliminate parasitic caused by human and machine errors. This is usually done by filtering the data using a software tool. The converted data require further calibration to convert it to user-readable data corresponding to the analyte sensed. Calibration is not a trivial task and often determines how useful a biosensor is. The mission of the developing biosensing trend is focused on providing affordable testing devices for underdeveloped nations [165]. Integration of PE and microfluidics have had a huge impact in creating the foundation for the development of such affordable sensors in the current era [166]. There have been many applications of microfluidic devices and microfluidic integrated sensors focused in field of healthcare industries that will be highly relevant for low-cost solutions in the developing and disadvantaged countries [167]– [177].

2.5 Capacitive Chemical Sensors 2.5.1 Capacitance – An overview A capacitor is a fundamental passive device with the ability to store charges, due to electrostatic attraction across a polarizable medium. A traditional capacitor is represented by two parallel conductive plates of area ‘S’ separated by a finite distance

‘d’. The dielectric material has its own dielectric constant specific to each material (εr). The charge storage capacity is determined by all the above parameters and the magnitude of capacitance is calculated as shown in Fig. 2.18 [178].

Figure 2.18 Structure and working of a traditional parallel plate capacitor [178].

The difference between the structure of a parallel plate capacitor and IDC is shown in Fig. 2.19. The geometrical factors determining the capacitance in an IDC is slightly different from the traditional capacitor and is explained in the following section.

Figure 2.19 Structural comparison of a) Parallel Plate b) IDC [179].

The analytical expressions describing the characteristics of IDC’s is clearly detailed in the theoretical work by R. Igreja and C.J. Dias [180], who used specialized mapping transformation to simplify the complex 3D shape to flatten 2D electrodes. The basic design parameters and cross section of an IDC they consider for modeling is shown in Fig. 2.20.

Figure 2.20 a) Layout and b) Cross-section of an IDC [180].

As an example to calculate the total capacitance of a N-finger IDC, an equivalent circuit of a 6-finger IDC is shown in Fig. 2.21 where CE is the capacitance of external electrode with reference to the ground at either side of the structure and Ci is half the individual capacitance of the internal electrodes. Since symmetry conditions are different between such devices it is important to distinguish between these electrodes.

Figure 2.21 Equivalent circuit of a 6-finger IDC [180].

The final equation for the capacitance (C) of an IDC with at least 3 electrodes is given by eqn. 2.2. A MATLAB code utilizing this theory is provided in Appendix D. 퐶 퐶 퐶 퐶 = (푁 − 3) + 2 (2.2) 2 퐶 + 퐶

() () where, 퐶 = 휀퐿 and 퐶 = 휀퐿 , where K(k) represents the elliptical integral with modulus of k. 2.5.2 Types of Printed Capacitors Depending on the structure and application, the capacitive sensing elements can be classified into four major categories – IDC, meander, spiral and serpentine [181]. The structural differences between the four structures is shown in Fig. 2.22. IDC’s have an advantage over the other structures with respect to ease of construction and comparable sensitivity [182], [183].

Figure 2.22 Structure and design of a) IDC b) Serpentine c) Spiral and d) Meander - capacitive sensors [181]. 53

In this work, IDC’s are used as the prime sensing structures. Dielectric loading using surface functionalization has been done to develop a range of novel capacitive sensor applications. 2.5.3 Capacitive Sensors – State of Art A detailed analysis of the past, present and future of capacitive sensing is explained by Ghafar et al. [184]. The prime advantage of capacitive sensing is that it is CMOS compatible and can be done in very high speeds down to ns range. All the major applications using capacitive substrate aim at detecting capacitive changes by functionalizing the electrode or dielectric layer (Fig. 2.23). By altering the functional group and electrode material, specific detection of analytes is achieved. For most of biological applications, the antigen antibody interactions are used to detect this change.

Figure 2.23 Capacitive signaling using antibody-antigen binding induced charge transfer or redistribution [184].

The advantage of using IDC’s is that there is a strong electric field generated, which can be sensitive to changes in close proximity. Some of the advantages of capacitive sensing over the alternate existing sensing technologies are its increased sensitivity, power saving, increased SNR and easy to fabricate properties [185]. The capacitive sensor has created a revolutionary change in nanosening world and made a huge impact in the fields of healthcare, defense, electronics and environmental sensing fields [186]–[191]. Capacitive touch screen biomolecular sensing marks a significant development in instrument free direct bio detection [192]. 54

Non-contact, label-free tactile sensing applications which require proximity sensing have been hugely benefited by capacitive sensing technology. Capacitive biosensing has been replacing the optical/fluorescent sensing technology with label-free electrical signaling for detecting various proteins, pathogens, blood glucose, and various other biological parameters [193]. Moreover, capacitive sensing plays a vital part in pressure sensing technology. The impact of pressure sensing technology on major consumer products and healthcare, and a predicted forecast is detailed in [194]. In this dissertation, the concept of capacitive sensing has been expanded to novel develop physical and chemical sensors interest to biomedical and environmental monitoring. Furthermore, dielectric loading technique using application specific functional groups has been implemented. The use of a single passive electronic device to create multiple low- cost, portable, flexible and disposable chemical sensors has been achieved using the principles of capacitive sensing (Fig. 2.24).

Figure 2.24 Dissertation sensor design & development flowchart.

Chapter 3: Integrated LoC Capacitive-Sensor – Process Development This chapter of the dissertation details an intensive surface study that includes plasma activation of the surface and a novel characterization technique via AFM. Moreover, a detailed discussion explaining the design and fabrication of microfluidic devices and printed sensors along with the tools and methods used for integrating them to create a LoC sensor is presented. The process development for microfluidics and printed sensors involves unique design platforms and fabrication/characterization tools, which are also explained in this chapter. 3.1 Surface Study One of the critical steps in fabricating a sensor is preparation of the surface which is usually followed by further fabrication steps including printing and bonding. Surface treatment and characterization plays a major role in improving the quality of printing. Moreover, it has its unique impact in microfluidic device fabrication and applications. In this section, the importance of surface activation using plasma treatment has been studied in detail and a novel characterization technique to verify the activation of a surface has been proposed. A full account of this effort was presented in Ref [195]. 3.1.1 Introduction The purpose of surface activation is to modify the chemical properties of the surface to increase its affinity towards the radicals in close proximity. Existing methods for increasing the surface hydrophilicity have implemented various methods including surface functionalization [196], UV ozone treatment [197] and Piranha treatment [198]. Table 3.1 summarizes the advantage of using plasma treatment in comparison to other existing techniques. alternative experimental approaches demand more complex methods involving toxic chemicals and environmentally hazardous processes. These can be easily avoided by the simple, cost-effective and safer plasma activation. However, no optimal processing conditions is given in the literature and the implication of time plasma for activation has not been explored.

Table 3.1 Qualitative Comparison of major techniques used for surface activation. Activation Energy User Waste Corrosive Process Thermal Process Substrate Technology Usage Safety Products Impact Variables Load Time Choice Functionalization Low High Low Low None Low High None Piranha Medium Low High High Few Medium High Many UV Ozone High Low Low High Few High High Few Plasma Activation Medium High None Low Many Low Low Many

3.1.2 Materials & Methods

In this activation study, due to its native oxide (SiO2) relevant for many glass types, a p-type Boron doped Si wafer with a native SiO2 layer used as a substrate. The original wafer was cleaved into 2 cm x 2 cm samples and were cleaned using acetone,

IPA and DI water followed by blow drying with N2 gas. The plasma gas used in this study was O2, which was generated by March Instruments Reactive Ion Etcher (RIE) model CS-1701 (Fig 3.1). The RIE consists of a parallel-plate chamber which is capacitively matched to a 13.56 MHz RF source and cooled using water chiller system.

Figure 3.1 March instruments CS-1701 RIE system.

3.1.3 Surface Activation Surface activation implicates creating free radical groups by modifying the surface chemistry of any given substrate surface. Surface activation is a nanoscale surface modification procedure. Activation has been used in wafer-to-wafer bonding processes and microfluidics [199], [200]. The purpose of oxygen plasma activation was to improve the hydrophilicity of the surface by creating a high density of siloxane (Si-O-Si) groups by bombardment of native SiO2 surface with ionized gases. 57

3.1.4 Surface Characterization A traditional method to characterize an activated surface is through contact angle measurements, which can take time and is not able to provide a microscopic understanding for activation. In comparison, a novel approach to characterize the surface with force spectroscopy using atomic force microscopy (AFM) has been proposed in this study. All the samples under study were characterized within 5 min O2 plasma activation.

Agilent 5500LS AFM was used for the force-spectroscopy measurements. SiO2 colloidal tips (r > 2.5 µm) were mounted on a still cantilever (f >100 kHz, k =14 N/m) for increased surface interaction of tip with the activated surface. Each data point reported consists of a statistical average of 16 measurements averaged in a given area of the sample (50 µm). The same cantilever was used in all measurements to avoid errors in data. The process flow is schematically explained in Fig. 3.2, where the stiction energy of the surface is measured using this colloidal tip and related to the hydrophilicity of the surface.

Figure 3.2 Contact mode AFM force spectroscopy process on an activated surface.

3.1.5 Verification of Surface Activation

First, to confirm the hydrophilic response to activation, a O2 plasma activation with fixed process parameters (pressure = 300 mTorr, t = 50 s, flow rate = 10 sccm) and two different power levels – 75 W (low) and 250 W (high) was done on the Si substrate. Next, to check if the activation was a physically (roughness) driven process or a structure 58

chemistry driven process, a N2 plasma was with same conditions was also tried. The contact angle measurements obtained from a control sample (no activation) and the three different plasma processes are detailed in Table 3.2. DI water (18 kΩ) was used as the droplet for all contact angle studies.

Table 3.2 Contact angle measurements for different activation conditions and plasma gases. O2 Plasma – Power = 0W O2 Plasma – Power = 75W

O2 Plasma – Power = 250W N2 Plasma – Power =15 0W

Obviously, activation causes the surface to increase its hydrophilicity. Also, the fact that the N2 plasma made it slightly more hydrophobic, leads to an assumption that O2 activation might be more of a chemical driven process and not a physical roughening of the surface with ion bombardment. This claim was verified further with the XPS data to be presented in the forthcoming sections. A similar simple verification using the proposed novel AFM spectroscopy was done on activated silicon surfaces. The effectiveness of determining the stiction energy using a colloidal SiO2 tip is demonstrated in Fig. 3.3, where the stiction energy of a unactivated Si surface is compared to two samples activated at different plasma power levels (100 W and 200 W). The area under the triangle represents stiction energy and it is clear that higher plasma power levels results in increased stiction energy.

Figure 3.3 AFM spectroscopy F-d curves comparing activated and unactivated surfaces using colloidal tips.

After verifying the AFM-measured stiction energy is a good measure of plasma activated hydrophilicity, a study of surface chemistry was conducted using x-ray photoelectron spectroscopy (XPS) approach to get a clear insight of plasma-mediated surface bonds. A control Si sample was compared with three different Si surfaces activated at 45 W, 100 W and 300 W. Since the siloxane bonds were responsible for the enhanced surface hydrophilicity [201], the XPS measurements of O1s and Si2p peaks are shown in Fig. 3.4. 60

Figure 3.4 XPS study of activated surfaces displaying variation in O1s and Si2p spectra.

In the case of O1s peak the peak shifts at higher energies with an increase in

FWHM indicating the formation of Si-O2 and Si-O4 species during activation. Similarly, in case of Si2p spectra, a weakening in Si-Si bonds and increase in Si-OH bonds can be observed. It should be noted that at 300 W, the peak is shifted to lower energy along with a decrease in FWHM. This is caused by breaking of Si-O bonds at such high-power level due to high-energy collisions of ionized species. This observation suggests that although higher energy plasma bombardment leads to breaking of Si-Si bonds and availability of more O on the surface, larger number of Si-O bonds may be available at moderate plasma conditions. To make sure that the hydrophilicity is purely a surface chemistry modification, an AFM topographic study of activated surfaces was also performed (Fig. 3.5). It is clear 61 that there is no significant change in the average roughness (Ra) and RMS roughness (Rrms) of surfaces activated at different conditions in comparison to the control Si surface. This provides a clear indication that the physical morphology during activation is largely unaltered and cannot be responsible for large increase in hydrophilicity.

Figure 3.5 AFM topographical scans of a) Si control sample and plasma activated samples at power levels of b) 45 W, c) 100W and d) 250 W.

3.1.6 Optimization of Surface Activation With the effectiveness of force-spectroscopy (F-d) curves using AFM to determine the surface hydrophilicity established and surface activation confirmed, an optimum processing condition to gain the maximum hydrophilicity was methodically studied. A statistical approach was followed to deduce the average behavior of activated surface. Since F-d curves may change in ambient process conditions and on the surface area explored, 16 different readings were collected for each sample point processed to avoid such experimental errors and to pick up the minute changes in surface stiction properties. This also allows us to build a statistical approach to investigate the activation 62 across the surface, which may be important to understand device to device variations in ultra-small devices and sensors. The controllable process parameters for surface activation were power, pressure, flow rate and time of exposure. With no prior knowledge about the optimal process parameters, the process condition used for the initial tests were typical of many published recipes: 1) Power = 125 W. 2) Pressure = 300 mTorr. 3) Flow rate = 10 sccm. 4) Time of exposure = 50 s. A consolidated stiction energy plot for every process parameter sweep while setting the three other parameters at the fixed level is shown in Fig. 3.6. and Fig. 3.7. It can be observed that from the initial sweeps, power was indeed the dominant parameter resulting in increased change in hydrophilic behavior of surface, with the flow-rate, time and pressure also having unique effects on the observed activation levels. For instance, long processing times (t>60s) and large pressures (P>250mTorr) has minimal returns and mediocre flow rates (25-35sccm) is less effective. Using this preliminary result on the significant parameters and their useful ranges, a second optimization run was performed fixing the other parameters at the values corresponding to their maximum stiction energy (pressure = 250 mT, flow rate = 20 sccm and time of exposure = 65 s). The contact angles of the activated samples were measured after 24 hours because the surfaces exhibited extreme hydrophilic behavior immediately after exposure making the contact angle measurements unfeasible. With this extensive surface study conducted, it is observed that the contact angle measurements are in perfect correlation with the F-d curve method discovered. In addition, large power levels do not correspond to better activation (along with increased standard deviation) which is confirmed in the F-d curve study and also was observed in the XPS study. Increased time and flow rate are also observed to cause minimal or no impact on activation energy. With a limitation in the pressure level of RIE system used, lower pressure levels could not be explored and would definitely provide with additional flexibility in the process developed. 63

Figure 3.6 Surface energy plots obtained from F-d curve averages for varying activation process parameters: time, pressure and flow rate.

Figure 3.7 Correlation between contact angle and F-d curve averaging of surface energy for varying plasma power levels.

To investigate the temporal variations of the activated surfaces, a study of two samples activated at 100 W and 250 W were studied over a period of 100 hours (Fig. 3.8).

Figure 3.8 Temporal study of surface stiction energy for two different Si surfaces activated at 100 W and 250 W. Inset gives the same data in linear y-scale.

Regular F-d measurements were taken to see the behavior of activated bonds over prolonged period of storage at room temperature. The activation energy reaches the highest after 24 hours of plasma process indicating that the humidity and O2 levels in the stored environment play a pivotal role in determining the surface hydrophilicity. The hydrophilicity wears off over time corresponding to decreased surface energy with all the volatile radicals lost during storage at room temperature conditions. 3.1.7 Implications for Printing In summary, by systematically studying and characterizing the surface, the plasma enhanced hydrophilic behavior can be put into various applications by knowing the conditions to achieve maximum surface energy. A plasma power of 125 W, pressure of 250 mTorr, exposure time of 65 s with flow rate having the minimum impact, is found to be the most effective way to achieve maximum surface stiction on Si surfaces. The surface activation and optimization is beneficial for a number of applications including monolayer microsphere deposition, wafer-to-wafer bonding and microfluidic bonding applications. In addition, activation has been proven useful to aid printing on surfaces like PDMS and glass which have been used to create the sensors in this dissertation which is detailed in Section 3.3. Fig. 3.9 provides a clear example of how activated PDMS aids in creating a uniform and continuous ink lines using inkjet printing. 65

Figure 3.9 Optical image (30x) showing the impact of O2 plasma activation on PDMS surfaces used as substrates for inkjet printing.

Similar improvement in printing properties was observed in the case of capacitive-sensors printed using Novacentrix JS-B40G silver nanoink with Fujifilm Dimatix printer on glass substrates. The optimized process parameters for Si surfaces were used for glass substrates too, as the bonds on the surface is SiO2 and formation of siloxane groups were common in either substrates. The improvement in features of the printed capacitors is shown in Fig. 3.10.

Figure 3.10 Improvement of features on activated glass substrates in Capacitors printed using Dimatix printer.

Moreover, plasma activation is a key process in creating the microfluidic LoC devices fabricated in this work (Section 3.2). PDMS seals to glass better under optimal 66 plasma processing conditions and potential leaks under high pressure can be avoided. In addition, creating these electronegative groups on the surface of the substrate has led to the discovery of a novel flow-rate sensor which is discussed in detail in Chapter 4. 3.2 Microfluidics Microfluidics is the science of manipulating very tiny amounts of fluids (10-9 L to 10-15 L) on solids using a channel of size ranging from a few tens of microns to hundreds of microns. Microfluidic devices have established as the integral means in bringing the concept of LoC into existence with its major impact on bio-chemical research, drug- development and point-of-care health care systems. This section of work introduces the toolset developed so far and the expertise in building sensor enriched micro-fluidic devices to aid in monitoring the wet environment established for biomedical sensing, cell culturing and motion and chemical changes in analytes. Such biosensors integrated with compact microfluidic devices would benefit the field of biomedicine in numerous ways. Apart from the field of biomedicine, these sensors would impact a variety of other research fields depending on the necessity and specificity of the application. Here, microfluidic devices are used establish the framework to integrate sensors within a wet environment in close proximity with analytes. By finely tuning the type, sensitivity, and location of sensors interfaced to powerful and compact digital electronic systems, it is envisaged that an additional analytical means and capable, compact LoC tools for bio, chemical and environmental applications can be accomplished with minimal manual intervention, low cost and high efficiency. 3.2.1 Fabrication 3.2.1.1 PDMS Microfluidics For a successful microfluidic channel design, the three most important parameters to consider are the surface modification, design consideration and material selection for the substrate and the main channel. As discussed earlier, the surface modification has been accomplished in this work via plasma activation. An optimal process was found for this step and modified slightly according to the choice of channel material to alter the properties of the surface modified. The second most important parameter to consider being the material of choice to create microchannel. PDMS elastomer is a standard material and a cross linked solid which has been used from the start of microfluidic era 67

due to its physical and chemical characteristics. PDMS (-[(CH3)2Si-O-]n) is inert to most chemicals and solvents used in biological and biomolecular experiments. In addition, it is permeable to gas and air. The viscosity of the liquid state elastomer makes it easier to cast and solidify with the help of the curing agent. Moreover, the melting point of PDMS being 226ºC - 232ºC and gas transition temperature of 123.3ºC – 149.9ºC, it makes it the best choice of materials to form these channels and a robust yet flexible material of choice [23]. There are design considerations to be followed while creating a device using microfluidics [24]. The substrate of choice is usually glass. But, in case of user-specific requirements where there must be complete gas permeability, glass substrate is replaced by PDMS substrate. Illustrations, design and development of developed microchannels are provided later in this dissertation as we explain different examples. Soft-lithography is considered as a traditional method to form microfluidic channels. The fabrication process for PDMS microfluidic devices includes three basic steps:  Design and Fabrication of the master mold (Fig. 3.11).  PDMS casting.  Bonding the channel to the substrate. In the first step of preparing the mold, the negative tone resist, SU-8 has been most popular in the past couple of decades. The fabrication process involving SU-8 soft lithography to create the microfluidic master mold is detailed in Appendix A. More recently, with the development of 3D printing technology, the master mold fabrication process has become simpler, cost-effective and time-saving by directly 3D printing the structure. There are pros and cons involved in choosing either of method for mold fabrication which is detailed in Table 3.3. Although 3D printing is replacing the soft- lithography process, while design requirement necessitates channel dimensions at few micron level (below the resolution limit of 3D printing), SU-8/soft lithography is still the process of choice. The complete process of creating the microfluidic LoC device from the master mold is detailed in Appendix B. 68

Figure 3.11 Master mold and microchannel fabrication process flow.

Table 3.3 Comparison of master mold fabrication techniques. Master mold Su-8 Soft lithography 3D- Printing fabrication process Process steps Many/Complex Printing/Simple Needs UV Yes No lithography/Resist Master mold resolution 5 µm 100 µm Mask design software Layout Editor Solidedge

Mask design

Final Mask Not needed

Final master mold

The final microfluidic channel designed using the master mold is shown in Fig. 3.12. The bonding of PDMS to any substrate involves surface activation to create highly reactive surface bonds. Sealing the two highly reactive surfaces results in a durable bonding and a sturdy microfluidic channel. The holes are punched into the microfluidic channel inlet and outlet ports before the bonding process as explained in Appendix B. Variation of chamber design and substrate selection is application specific.

Figure 3.12 300µm channels bonded to glass substrate after surface activation.

Multilayer microfluidic channels are also prepared with the same processing sequence following a bottom-up approach. One of the multilayer microfluidic channels fabricated included a double layered PDMS structure with the aim of stabilizing the insulin released by pancreatic islets or beads used in the ion-exchange resin beads (Chp.5). The traditional islet imaging chamber purchased and used for this purpose was a 22 mm diameter and 1-2 mm height open chamber (Warner Instruments) shown in Fig. 3.13. This chamber has a volume capacity of 1mL. The drawback with this chamber was that the islets needed to sit in the given volume for almost an hour to get detectable insulin levels. The aim of this work was to concentrate the islets inside a tiny chamber where detectable amount of insulin is concentrated in the volume every minute.

Figure 3.13 Warner Instruments 22mm diameter culture/imaging chamber.

A two-level chamber design was proposed to concentrate the targeted volume of insulin in the chamber. The bottom layer was designed to hold the pancreatic islets in place and not be disturbed by the fluid flow. A 5 mm x 5 mm x 0.2 mm diamond chamber as shown in Fig 3.14a was designed. The top chamber was designed to be a 5 mm x 5 mm x 0.4 mm diamond chamber (Fig. 3.14b), with inlet and outlet fluid flow lines connected to the diagonal edges of the diamond chamber. Bonding the two chambers resulted in a microfluidic device with dimensions of 5x5x0.6mm (V=15µL), sufficient for tracking of 1ml insulin secretion over an hour with 1 minute sampling rate.

Figure 3.14 a) Bottom chamber and b) Top chamber of concentrated insulin 2-layer microfluidic device. 71

3.2.1.2 Paper Microfluidics In addition to PDMS microfluidic devices, paper microfluidic devices were also created and tested. Various paper substrates with different porosity were inspected. Xerox Colorcube 8570 wax printer (Fig. 3.15a) was used to print the microchannels designed using Adobe illustrator. The printed and heat-infused wax creates printed designs that are completely hydrophobic and forms the blocking interfaces, allowing the fluid flow along the paper surface. The printed patterns need to be cured at 120ºC for 2 mins to create a working paper microfluidic device as shown in Fig. 3.15b.

Figure 3.15 a) Xerox was printer used to create the b) printed and cured paper microfluidic channels.

The sensor is printed on the other side of the printed microchannels. An illustration of a novel quad capacitive sensor explained in chapter 4 is shown in Fig 3.16. The paper microfluidic channels possess an advantage in flexibility over PDMS-glass microfluidic channel. The bend compensation in this flexible sensor and the parallel 4- channel measurement is detailed in Section 4.2.

Figure 3.16 Paper microfluidic capacitive-quad sensor showing the wax printed microchannel side and printed sensor side.

3.2.2 Flow Integration – Syringe Pump Fluid flow setup is one of the important steps to be implemented in a microfluidic LoC device fabrication. Fluid flow is crucial when it comes to dispensing a fluid through sub-micron (<100 µm) channels, which would take too long in gravity fed systems for practical time scales and in very narrow channel it may not even be sufficient. Depending on the application under use, the flow rate can be in the order of few m/min up to several ml/min. For cell counting and sorting experiments using printed capacitors and electrodes, a slow and steady flow is needed. This is achieved by using syringe pumps. In our experiment we use OEM – NE – 500® pumps for accurate control and dispensing of solutions into the microchannel. This syringe pump uses software which can be a manual/automated(programmed) controlled using a computer interface. The complete setup of the fluid connection to the microfluidic chamber is shown in Fig. 3.17. The programmable software (Syringpump pro®) was used to set up flow rates on a loop without human interference and need to control for experiments which needs to be run over indefinitely or until the fluid runs out.

Figure 3.17 Arrangement of Syringe pump fluidic setup for microfluidic channel flow. 73

The programming procedure used to control the flow rate is detailed in Appendix C. Fig. 3.18 shows a screenshot of the fluid flow control software programming setup used.

Figure 3.18 Screenshot of the fluid control interface and programmable flow control software.

The flow-control GUI interface lets the user select the type of syringe used, the flow volume, target flow rate and allows parallel control of up to 10 syringes at a time. This controlled setup was used to create programmed rhythmic pulses to mimic the function of insulin secretion by pancreatic islets and control the cell culture environment over an hour [202]. Fig. 3.19 illustrates the response of the pancreatic islets to forced oscillations of glucose stimulated Ca2+. Moreover, the pulsed and controlled flow setup is a crucial part of the capacitive- flowrate sensor developed which is explained in Chapter 4.

Figure 3.19 a) Ca2+ fluorescence response of 9 islets to glucose stimulation induced by forced oscillations. b) Average of overall response given in Fig 3.19a [202].

3.2.3 Microfluidics Summary Microfluidic LoC design and fabrication has been explored in detail which include PDMS and paper microfluidic device study. Surface activation studies have found to be helpful in the fabrication process. A novel PDMS microfluidic LoC was designed and fabricated to increase insulin concentration in pancreatic islets. Although paper microfluidics have not been deeply studied, a flexible quad capacitive sensor was designed which led to the development of bend sensor. A programmable flow setup was utilized to measure forced oscillations of Ca2+ in pancreatic islets. Printed capacitive sensors are an integral part of the LoC sensors fabricated in this research. The printing process and integration of printed sensors with the microfluidic devices is detailed in Section 3.3. 3.3 Printed Capacitors The capacitive and resistive passive sensing elements used in this work were printed using three different printers – Sonoplot Microplotter, Fujifilm Dimatix Pattern Editor (DMP-2831) and Epson Workforce inkjet printer (only for paper microfluidic devices).

3.3.1 Introduction The development of capacitive sensors involves a two-step process of printing the sensing element and integrating it with the PDMS microfluidic channel. In some cases, where the fluid or analyte material is corrosive, a 1um later of PDMS protective layer is spun on the printed sensor before integrating it with the microfluidic channel. Sonoplot microplotter (Fig. 3.20) is a high-precision closed-loop controlled system which uses capillary action to withdraw an ink from the ink well and has a highly accurate position control along the x,y and z axis. The ink is filled using capillary action into the micropipette which is attached to a piezo electric plate (PZT). By applying an AC current, vibration at ultrasonic frequency (0.4 MHz to 0.7 MHz) is produced by the PZT plate that pushes the ink out of the pipette.

Precision positioning system

CCD Camera

Fluid Dispenser Ink Well Figure 3.20 Sonoplot Microplotter used to print printed passive sensing elements.

DMP-2831 (Fig. 3.21) is a benchtop compact digital inkjet printer with two print heads capable of producing a 1 pL and a 10 pL drops. It has a capability to print with a 5 µm drop spacing. Patterns up to 200 mm x 300 mm can be printed with a vacuum plate capable of mounting substrates up to 25 mm thick. Each cartridge contains 16 nozzles spaced 250 µm apart.

Figure 3.21 Fujifilm Dimatix Material Printer (DMP-2831).

A comparative study on Sonoplot and Dimatix printers have been presented earlier [203], [204]. A comparison of printing parameters of the two printing techniques is given in table 3.4. In this research, applications requiring larger capacitive values were printed using Dimatix printer since the range of capacitances obtained by Sonoplot were from a few 100 fF to a couple of pF.

Table 3.4 Comparison of Sonoplot Microplotter and Dimatix Printer. Sonoplot Microplotter Fujifilm Dimatix Operation Frequency 400 KHz – 700 KHz N/A Operating Principle Piezoelectric Vibration Piezoelectric expansion Substrate Holder (Vacuum) No Yes Temperature Control N/A Adjustable Resolution 2µm 5µm droplet Ink Viscosity Upto 450 cP 8 – 12 cP

Moreover, A capacitance prediction model was created for the IDE capacitors, based on the work by Rui Igreja and C.J. Dias [205], [206]. An application was created using MATLAB to predict the capacitance of printed IDE (Appendix D). The change in capacitance of the printed IDE depends on 7 primary factors which include:  Width of printed electrode  Length of printed electrode  Distance between two electrodes 77

 Number of electrodes  Substrate thickness and dielectric constant  Dielectric constant of capping layer on the electrode (Air =1)  The frequency dependence of the capacitance is not included. A default frequency of 25 KHz is considered. Table. 3.5 shows the agreement of the modelled capacitor value to the measured capacitance value after printing. It can be seen that this model proves to be highly accurate and is very useful in determining the design of the capacitors before real fabrication.

Table 3.5 Modelled capacitance vs Measured capacitance. Electrode Electrode Electrode Reference Variable Gap Number Length No of Electrodes 10 10 10 15 10 10 30 Length of Electrodes (mm) 3 3 3 3 1 3 3 Line Width of Electrodes (µm) 50 50 50 20 50 50 80 Gap between the Electrodes (µm) 250 450 100 40 100 100 120 Measured Capacitance (pF) 0.67 0.57 0.83 1.02 0.36 0.83 3.2 Modelled Capacitance (pF) 0.66 0.55 0.84 1.05 0.4 0.85 3.1

With the printed capacitors in place, we now integrate the microfluidic channels created with the sensors. This involves three steps: 1) Protecting the printed ink by spinning a layer of polymer. 2) Plasma activation of microfluidic channel 3) Bonding On glass substrates, the silver nanoink used to print is annealed at a relatively high temperature (> 200ºC) and resistant to most solvents used (ethanol, methanol and DI) for testing. On paper lower temperature (~60˚C) in a vacuum anneal oven is used to achieve the same level of electrode conductivity. To increase the durability of as printed sensors in specific solvents intended for detection, the printed capacitors can be protected by a ultrathin layer of an inert polymer like PMMA or PDMS. Since we have optimized the usage of PDMS, a thin layer (<1 µm) of PDMS was spun over the printed sensors at 2500 rpm for 100 s and cured at 70ºC overnight. High temperature annealing of spun 78

PDMS was avoided to prevent the electrodes from damage. The PDMS is carefully peeled off around the contacts for external electrical connectivity. This step is followed by plasma activation of PDMS channel surfaces (after punching in the holes for inlets and outlets) and bonding it with the activated PDMS covered sensors resulting in the sensor integrated microfluidic device shown in Fig. 3.22.

Figure 3.22 Capacitive sensor integrated microfluidic device.

3.3.2 Sensing Platform To ensure proper working of capacitors, the LCR meter was used to check the capacitive measurement along with the phase angle. A phase angle >85˚ shows an acceptable quality (negligible parallel conductance) for the capacitor. LCR-meter is a highly accurate, sizeable and expensive device that would not be practical to use in LoC/PoC systems for capacitive monitoring. Real time monitoring of the solution passing through the channel and change in capacitance was needed for practical applications. A Texas instrument (TI FDC 1004 EVM, a 4-channel capacitance-to-digital converter - CDC) was used to monitor minute capacitive changes along the channel. It has a limited capacitive sensing range (±15 pF) compared to its improved version (FDC 2114 EVM and 2214 EVM) which goes up to 250 nF. Lower cost 1004 EVM board has a lower resolution of 12 bits as comparison to 28 bits of the 2214 EVM board. Both uses I2C to interface with the MCU with direct USB connectivity. The capacitance of a 100 µm printed capacitors with four fingers on either side was measured to be 0.43 pF. Together with the parasitic capacitance of the wires (0.02 pF), the capacitance sensed increases up to 0.45 pF. Initial tests using the printed sensor 79 shows that excessive external noise sources such as moving objects, shaky wires nearby, large temperature and humidity swings can have large impact on . The test setup is shown in Fig. 3.23.

Figure 3.23 Test setup used for sensor integrated microfluidic device characterization.

3.3.2.1 Types of CDC Two kinds of CDC’s were used according to the sensing limits of capacitor under test – FDC1004EVM and FDC2214EVM. A table comparing the differences between the two cards and their properties is detailed in Table 3.6. The CDC comes with a GUI interface which can be used to view and log the real-time output of the sensing element. The resolution and data acquisition rate can be adjusted at the user end window. In this dissertation, all the measurements were recorded at a rate of 10 samples/s. The control panel of the CDC and sensing solution EVM GUI is shown in Fig. 3.24. The FDC2214EVM card has more user modifiable parameters for capturing the capacitance data in comparison to FDC1004EVM, including individual channel compensation capacitances for wire parasitic and an adjustable frequency (10 KHz to 10 MHz).

Table 3.6 Comparison of CDCs used for sensor characterization. CDC FDC1004EVM FDC2214EVM Number of Channels 4 4 Resolution 12 bits 28 bits Upper Sensing Limit 15 pF 250 nF Lower Sensing Limit 3.7 fF 0.93 fF Excitation Frequency Fixed (25 KHz) Variable (up to 10 MHz) Shield External ports Built-in EMI shield Interface I2C I2C

Figure 3.24 Sensing Solution EVM GUI control panel and real-time data interface.

3.3.2.2 Noise Elimination The FDC1004EVM board provides an additional signal output called “SHIELD”, which can be implemented on the design along with the sensor to eliminate the background noise caused by environmental changes. The shield and the signal are kept in the same potential. The shield can be designed in a couple of ways. One is to completely cover the sensor background with a conductive shield. Due to design restrictions and complications using printed capacitors, we implement the shield to be around the sensor as shown in Fig. 3.25. In a finished product, typically all empty areas are filled with a ~1 mm mesh lines to increase the shield area and minimize unintended temperature and field variations across the surface. 81

Figure 3.25 Printed shield around the capacitor to eliminate environmental noises.

Improvement in the signal to noise ratio, with and without shield is shown in Fig. 3.26. It can be observed that the noise level has significantly decreased with addition of shield. It is noticeable that the shield also helped in measuring the accurate value of capacitance comparable to that measured on the LCR meter (0.45 pF).

Figure 3.26 Demonstration of improvement in signal quality with shield implemented in the design.

In addition, the noise elimination can be done during post-processing of raw data. It can be observed that the original data in Fig. 3.27 corresponding to the raw data of the capacitance has a lot of noise in the data caused by the electrical and mechanical vibrations created during the hotplate coil heat up. A GUI names “Data Cleanser” was developed in C# which can be used to filter this noisy data to create a smooth signal 82

(processed data – Fig. 3.27) without compromising the data’s originality. The filtration process is explained in Appendix E.

Figure 3.27 Interface of "Data Cleanser" GUI developed using C#.

3.4 Chapter Summary In summary, the following development efforts and findings are reported in this chapter of the dissertation:  A novel surface characterization tool using AFM spectroscopy has been reported to precisely predict the surface energy of plasma activated surfaces.  Application and usefulness of surface activation in various printing techniques have been demonstrated.  Multilayer PDMS microfluidic device designs are proposed for concentrating insulin secretion of pancreatic islets to improve the cell-culture throughput.  A GUI for mathematical modelling to predict IDC device values has been developed.  A controlled flow automation program has been created to study Ca2+ by mimicking pancreatic cell insulin secretion has been created. 83

 A GUI for post-processing raw sensor data to eliminate external noise without compromising the data integrity has been developed. Utilizing the sensor integrated microfluidic LoC setup developed and with the optimization of data acquisition and post-processing, a number of capacitive-sensors have been designed and developed. The integration of capacitive sensors with microfluidics and automation of flow control has aided in development of some novel, simple, cost- effective physical and chemical sensors which are characterized in Chapter 4 and Chapter 5.

Chapter 4: Capacitive Physical Sensors 4.1 Capacitive – Flow Sensor Flow sensing in microfluidic platforms has been vastly studied and the detection techniques generally involves either MEMS, thermal, calorimetric or optical detection methods [207]–[211]. In this section, the exploration of a novel capacitive flow sensing approach using the fundamental electro hydro dynamics has been detailed. Simple printed interdigitated capacitors (IDC) have been uniquely tailored to create a flow-rate sensor for polar conducting fluids by exploring the underlying concepts of fluid dynamics and electrokinetics. It will be shown that the novel capacitive sensing approach is sensitive to charge redistribution due to electro-osmotic effect within the range of Debye length which can be detected using the capacitive-sensor. To understand the design and working of this flow-rate sensor, basic concepts of fluid mechanics must be understood. Coutte flow is one of the principal flow method inside a microchannel. This flow is considered as a surface driven flow and has no pressure gradient in the system which causes the flow. It is a completely potential driven flow induced by the surface charges [212]. This unique flow regimes have been previously considered for building mini/micro pumps by phase/pulse driven electrodes with no moving parts. However, the reverse mechanism, whereby force-flow driving charge re-distribution near a surface, has not been studied in detail or considered for sensing applications. Thus, a reliable description of this sensing approach requires a profound understanding of underlying electro-kinetic flow which will be provided in Appendix F. 4.1.1 Electro-osmosis – Integral boundary layer analysis 4.1.1.1 Surface driven flow properties This section describes how the boundary conditions in a microfluidic channel aids in solving the Navier-Stokes master equation for flow dynamics, introduced in Chapter 2. It is intended to explain the electro-osmotic effects of the charge present in the system [213]. Consider a glass plate with a drop of water on it as shown in Fig. 4.1.

Figure 4.1 Glass surface with a drop of electrolyte.

If an “imaginary” voltmeter of high degree of precision is used to measure the voltage at the surface of the wall (~10 nm from wall) and the bulk (in the fluid), they will be different. The reason for this difference can be explained as follows. At equilibrium, the electrochemical potential must be same everywhere. But, the chemical potential of ions at the wall and bulk are not the same. To explain the reason for the difference in ionic potential at the surface and bulk, consider the surface of the glass. It consists of Silane groups (Si) in the bulk, terminated by hydroxyl groups (OH) on the surface forming Silinol groups as shown in Fig 4.2.

Figure 4.2 Silinol bonds present on a glass surface.

If a liquid layer with pH7 is placed on this surface, then it acts like a weak acid as it interacts with and strips the H+ bond off the OH- groups and the water molecules in this + - interaction turns into H3O complex, leaving the surface with negatively charged SiO groups. Hence the SiO- ions make the Si-based interfaces (glass or PDMS) aggregately negatively charged, leading to formation of a dipole layer at the glass water interface due to this H-H interaction. An important observation to be made in this context is that surface driven flow, i.e. osmotic flow that is the subject of this section, changes its rate depending on the pH of the solution. This negative charge density at the wall results in a lower electrical potential at the surface. This potential difference between the wall and the bulk decays as 86 we move away from the wall and becomes zero in a very short distance known as Debye length, which could be as large as ~10 nm in DI water and rapidly drops to ~1nm at higher ionic concentrations. 4.1.1.2 Integral Boundary Analysis If there is a surface (wall) of the microfluidic channel where there exists a wall potential φ = 휑 and as we move towards the bulk in the y-direction, this potential decays to zero within a small unknown distance in the order of a few nanometers as shown in Fig. 4.3.

Figure 4.3 Boundary conditions in an infinitesimal volume from wall of a microfluidic channel.

The flow is parallel to the wall in the x-direction and a no slip boundary condition with zero velocity at the wall (푈=0). When an electric field is applied on this system, it affects the flow in a unique and fundamental way [214]. Since there are two different electric fields, to avoid confusion during solutions, the two fields are distinguished as shown in Fig. 4.4.

1) 퐸⃗ is in the x-direction and is not a part of the system. It is caused by an external power supply connected to the system and is considered as an extrinsic electric field.

2) 휑 is the voltage difference between the wall and the bulk and causes an electric field along the y-direction. This field is considered an intrinsic electric field and is caused by the natural chemistry of the surface.

Figure 4.4 Types of field in the microfluidic channel.

The Navier-Stokes equation (Eqn. 4.1) is electroneutral and there is no electric field influence. To perform the integral analysis, consider the same assumptions used for Couette flow described in Appendix F. There is no pressure gradient and the flow is unidirectional. In addition, since an additional electric field is applied, the net charge density is not zero and the system is not electroneutral [215].

⃗ 휌 + 휌푈⃗ ⋅ 훻푈⃗ = −훻푃 + 휂훻푈⃗ + 휌 퐸⃗ (4.1) Due to the assumptions mentioned above, this equation simplifies into eqn. 4.2.

휂훻 푈⃗ = − 휌퐸⃗ (4.2) Eqn. 4.2 describes that in any controlled local volume of fluid, if there is an electric field that is driving it, there is a net flux of viscous momentum counteracting the applied field. It is assumed that the liquid has a net charge density of 휌, which is known and controlled as an intrinsic property of the system. The charge density in the area near the surface is driven by the extrinsic electric field (퐸⃗). To apply the boundary conditions to this relationship; the Gauss’ Law (eqn. A7.16 in Appendix F) can be invoked, which relates the divergence of electric field displacement with the net charge density. The displacement vector is represented in terms of permittivity is 퐷⃗ = 휀퐸 where 휀 is spatially uniform. Applying this relationship into the Gauss’ Law and recalling that electric field is divergence of potential (퐸⃗ = −∇휙), the modified Gauss’ Law is given by eqn. 4.3. −휀훻 휙 = 휌 (4.3) This relationship helps in relating the net charge density to the spatial description of the potential (ϕ). The Navier-Stokes eqn. (4.1). can now be represented as

휂훻 푈⃗ = 휀퐸⃗훻 휙 (4.4) 88

The LHS of eqn. 4.4 describes the volumetric viscous force represented as a Laplacian of the velocity vector. The RHS of eqn. 4.4 describes the Coulomb force (electrostatic force) given by the permittivity, electric field and Laplacian of the electric potential. The integrity of this argument can be verified by the Gauss’ Law which states that there must be a charge present, if there is a Laplacian of the potential.

Eqn. 4.4 can be written in 1-D form with the assumptions that 푈⃗ and 휙 is a function of y-direction as seen in eqn. 4.5. 휕푈 휕휙 휂 = 휀퐸 (4.5) 휕푦 휕푦 Before attempting to solve this equation, one needs to be aware of some of the assumptions for solving the Navier-Stokes equation with integral boundary layer analysis.  The aim of this 1-D analysis is to find the velocity of the flow, which can be obtained with the x-direction analysis with minimal information about the system.  If we solve for the Navier-Stokes equation in the y-direction, there will be a pressure increase at the wall which must be accounted for. We have a negatively charged wall and positive ions nearby. There is a force pulling them together which must be balanced by a static pressure. This effect is called the “Osmotic Pressure” of electrical double layer (EDL) detailed in Section 4.1.4.  Solving in y-direction tells us about the static pressure and not about the flow dynamics which we are interested to unravel.  The constraint with y-direction solution is that net flow in this direction is zero and there could be only local, vortex formation inside the channel. To get a meaningful result in this direction, a spatial distribution solution of the EDL in y- direction needs to be taken into consideration. Whereas, in the x-direction, we can get the flow results by solving the integral boundary layer analysis utilizing the boundary conditions. To continue solving the Navier-Stokes equation in the x-direction, a double integration of eqn. 4.5 w.r.t. ‘y’ is done which results in eqn. 4.6.

휂푈⃗ = 휀퐸휙 + 퐶푦 + 퐶 (4.6) 89

When the system becomes large, no unbounded velocities can exist which results in the conclusion that 퐶푦 =0. For y=0 case, at the wall, we know that the velocity must be zero (푈=0), since the no slip boundary condition exists. Applying this condition to eqn. 4.6, we get, 퐶 =−휀퐸휑, since the potential at the wall is 휑. Applying these two constants obtained from the boundary conditions back into eqn. 4.6, we obtain the term for velocity given by eqn. 4.7. 휀퐸 푈 = − (휑 − 휑 ) (4.7) 휂 Eqn. 4.7 states that, in this flow, there is a velocity that is varying with ‘y’, specifically because we have ‘푈’ as a function of the local potential, which is in turn a function of ‘y’.

To verify, at the wall where y=0, 휑 = 휑 and hence from eqn. 4.7, 푈 =0. At a distance far away from the wall, 휑 =0, so in the bulk fluid, the velocity is given by eqn. 4.8 휀휑 퐸 푈 = − (4.8) 휂 In conclusion, the following observations can be derived from the integral boundary layer analysis solving for the velocity of fluid flow in the microfluidic channel.  If the microfluidic wall has a charge distribution different from the bulk (fluid), and an external electric field is applied parallel to this wall, a velocity distribution is obtained that varies as the potential varies.  The potential variation from the bulk to the wall happens over the Debye

length (λD), a very short distance ≤10 nm. Even in a comparatively large (~100 µm wide) microfluidic channel, such potential changes can have an important effect on the flow.  Outside this 10 nm layer, there is a uniform velocity distribution that depends on the extrinsic electric field applied and is proportional to the voltage

difference at the wall (휑) i.e the flow is proportional to the chemistry driving

this 휑 potential.  The velocity of fluid flow is inversely proportional to the viscosity and the fluid flow is slower as viscosity increases. The reason for this phenomenon is 90

that while the fluid is pushed with a coulomb force, the viscosity of the fluid is the term that balances this force in the opposite direction. If this potential change in the small distance away from the wall is plotted, we get a curve as shown in Fig. 4.4, which exponentially decays from φ=휑 at the wall to φ = 0 in the bulk. The distance at which this surface phenomenon happens is called the Debye length (λD). The velocity distribution within the Debye limit is also indicated in Fig. 4.4. If we zoom out of Fig. 4.4 into the whole microfluidic channel (Fig. 4.5), depicts the flow in the channel as observed by a normal eye or even an optical microscope.

Figure 4. 5 Optical view of the surface driven flow in a microfluidic channel.

In summary, to a naked eye, when we have an electrolytic solution moving in the channel, all we can observe is a uniform velocity moving at the speed given by eqn. 4.8. But, the surface driven flow plays a major role in deciding the velocity and charge distribution near the surface. In addition, the effect on an applied pressure on the velocity at the surface is an important phenomenon in developing this capacitor-flowrate sensor. With the understanding of impact of surface charges on the flow, the concept of EDL and an applied pressure gradient details about this novel sensor developed. 4.1.2 Hagen-Poiseuille Law Section 4.1.1 focused on electric field driven flow systems and explained the necessity of understanding surface charge layer distribution for the flow inside the microfluidic channel. But, to validate the capacitive-flowrate sensor developed, an understanding of the impact of a pressure gradient applied parallel to the flow direction is also important. This section solves the velocity profile dependent on a pressure gradient in the channel by solving the Hagen-Poiseuille’s law [216]. 91

Consider a cylindrical channel of radius ‘R’ which has a pressure gradient applied in z-direction and orthogonal to the radial ‘r’ direction as shown in Fig. 4.6.

Figure 4.6 Cylindrical channel with a pressure driven flow.

The equation for velocity would be same as eqn. A7.10 in Appendix F, except for the change in the cylindrical coordinate system. The equation governing the velocity distribution is given by eqn. 4.9 which differs only by a factor of two in the denominator compared to eqn. A7.10 due to presence of cylindrical coordinate Laplacian. 1 푑푃 푈 = − (푅 − 푟) (4.9) 4휂 푑푧 Integrating eqn. 4.9 to get the mean velocity of flow results in eqn. 4.10. ∫ 푈2휋푟푑푟 푈⃗ = 휋푟 1 푑푃 푈⃗ = − 푅 (4.10) 8휂 푑푧 Eqn. 4.10 is the Hagen-Poiseuille’s Law which represents the spatially averaged volumetric flux (Q) given by the average velocity distribution as a function of the driving pressure field. This equation is graphically represented in Fig. 4.7.

Figure 4.7 Hagen-Poiseuille’s Law - Pressure driven volumetric flux in a microchannel.

∆ We can represent the pressure gradient in a normalized form, = , where, ‘L’ is the length of channel and the average velocity as the volumetric flux per unit area, 푈⃗ = . With the assumptions above, Hagen-Poiseuille’s Law can be represented by eqn. 4.11. 푄 1 ∆푃 = − 푅 (4.11) 휋푅 8휂 퐿 This equation can be rearranged to eqn. 4.12. 8휂퐿 ∆푃 = − 푄 (4.12) 휋푅 Eqn. 4.12 provides information about hydraulic resistance, 푅 = . This can be compared to the electrical equivalent of Ohms law as shown in Table 4.1.

Table 4.1 Comparison of Ohm’s law and Hagen-Poiseuille’s Law. Ohm’s Law Hagen-Poiseuille’s Law

∆퐕 = 퐈퐑 ∆퐏 = 퐐퐑퐡

Therefore, a pressure applied to a microfluidic channel, causes a net volumetric flux (average velocity per unit area) which depends on the hydraulic resistance of the system which is a function of length of the channel, radius of the channel and the viscosity of the fluid in the system. 4.1.3 Hydraulic Capacitance – Compliance For a rigid microfluidic system, the pressure driven volumetric flux depends on the Hagen-Poiseuille’s Law described by eqn. 4.13 푃 = 푄푅 (4.13) But in case of PDMS microfluidic channels, the volume is not fixed because of the following three reasons: 1) Flexible walls: A PDMS microchannel is flexible and can be compared to a water filled balloon where the total volume of the chamber is not fixed and is dependent on the pressure of the fluid flowing through. At high pressures, we can observe the walls expanding due to the elastomeric properties of the material. 93

2) Bubbles in the chamber: A bubble inside the chamber is unavoidable in a microfluidic channel even if not visible to normal vision. These micro-bubbles contract and the walls of the bubble shrink in proportion to the volume and pressure of fluidic forces it experiences. This causes the total volume inside the chamber to vary. All the above-mentioned effects cause an effective change in the volume of the fluid which can be termed as the compliance (Cn) of the system, given by eqn. 4.14. 푑푉 퐶 = (4.14) 푑푃 It represents a differential volume change of the system as the pressure changes [217]. To convert this to see its effect with time and compare it to its electrical counterpart, we multiply eqn. 4.15 with the time dependent pressure change. 푑푃 푑푉 푑푃 퐶 = 푑푡 푑푃 푑푡 푑푃 푑푉 퐶 = (4.15) 푑푡 푑푡 The change in the volume (V) of the system can be related to the expansion caused by the change in the flux (Q) of the fluid, i.e. = 푄. Eqn. 4.15 can be compared to its electrical equivalent of capacitance and the current in the capacitor as mentioned in Table 4.2.

Table 4.2 Comparison of capacitance in a fluidic and electrical systems. Fluidic system Electrical system 퐝퐏 퐝퐕 퐐 = 퐂 퐈 = 퐂 퐧 퐝퐭 퐝퐭

Therefore, the change in compliance (hydraulic capacitance) caused by a pressure differential applied to the fluid corresponds to the volumetric flux induced [218]. This parameter is an important concept for the capacitive-flowrate sensor developed. A change in pressure gradient applied, causes a change in the compliance which can be measured as a function of the volumetric flux change comparable to the current change of a capacitor with differential voltage applied. IT is this change in the flux (Q) which can be 94 traced back to the potential distribution in the electric-double layer in the electro-osmotic flow that the printed capacitor will be sensitive to. 4.1.4 EDL & Debye Length Section 4.1.1 emphasized the electro-osmotic flow due to the formation of a thin EDL with boundary conditions limited to the Debye length. To further understand the formation of this EDL and how it affects the velocity of flow inside the microchannel, consider Fig. 4.8, where there is a negatively charged wall (surface) and a cloud of positively charged ions close to this wall. There is a top wall in the microfluidic channel separated from this bottom wall by a distance ‘d’. There is a no slip boundary condition where φ = 휑 at the wall and decays to φ = 0 at the bulk.

Figure 4.8 EDL formation inside a microfluidic channel.

The top and bottom walls are so distant that EDL on either side does not affect the other side. The EDL overlap only under two conditions: i) making ‘d’ very small by decreasing the height of the channel and ii) making ‘λD’ very big. Debye length describes the physical length at which a point charge’s field is compensated by the polarization it induces in a dielectric polarizable medium. In other words, it is the screening length for a point charge in an ionic or electronic ‘cloud’. The Debye length for a isotropic electrolyte is defined as [219].

휀푘푇 (4.16) 휆 = 2푞 퐼 where, k is the Boltzmann’s constant = 8.617e-5 eV K−1, 휀 is the permittivity of the fluid, -19 T is the temperature of the fluid, q is the elementary charge = 1.602e C, 퐼 is the ionic concentration of the fluid. At room temperature, to increase 휆, the major parameter that 95

can be changed is the ionic concertation. But, the effect of change in 퐼 on 휆 is miniscule. For instance, for a 2 mM NaCl salt solution 휆 = 9.6 nm versus 휆 = 300 nm when the concentration is lowered to 2 µM. Therefore, for large channel thickness d (100’s of microns), even high ionic concentration would not affect the flow mechanism caused by the EDL on either side. This is a very important observation made in the capacitive-flowrate sensor developed.

The flow rate is independent of the ionic concentration when d>>흀푫. Varying the ionic concentration from 0.1% w/v, 1% w/v and 10% w/v did not change the flow-rate response detailed in the results section (Fig. 4.14, Fig 4.16 and Fig 4.18). Thus, the flow pattern in the microfluidic channel designed strictly follows the osmotic-flow as depicted in Fig. 4.5. But, since we have an additional pressure gradient added to the system, an analysis of its effect on the flow needs to be further understood. With the knowledge of current flux and volumetric flux, we can comprehensively describe the effects of these parameters on the flow rate with the matrix given in eqn. 4.17 [220]. 푄 휒 휒 −∇ 푃 퐴 = (4.17) 퐼 휒 휒 −∇휙 퐴 This linear matrix describes the small deviations from thermodynamic equilibrium by relating the pressure gradient and electric field to volumetric flux and

current flux. Solutions for the matrix constants are given by, 휒 = and 휒 = − which describes the expected velocities in the area as a function of a forced pressure. These constants are directly related to the integral boundary analysis of osmotic flow velocity distribution explained in section 4.1.1.2. 휒 gives a mathematical description of the current distribution when a system is forced with pressure (Capacitive-flowrate sensor case) and 휒 gives the current distribution when a system is forced with an electric field. 4.1.5 Comprehensive background behind capacitive-flowrate sensor With all the analysis and background from the previous sections, the principle behind the pressure driven flow rate change can be well understood and comprehended into one equation governing the process. Consider the microfluidic channel with a pressure gradient applied in the x-direction as shown in Fig. 4.9. 96

Figure 4.9 Pressure driven flow in a microfluidic channel.

As discussed in section A7.1.2 in Appendix F, if there is a pressure gradient in x- direction, it creates a flow with a parabolic distribution of velocity (Poiseuille’s flow). It should be noted that the velocity at the wall is comparatively small but finite. As there will be a net negative potential at the wall, the ions from the fluid is attracted towards the wall and form a cloud of positive ions, creating an EDL with a Debye length determined by the chemistry of the surface. This creates a net charge of density at the wall and these ions will experience a motion by this pressure induced flow. There will be ions present in the bulk which experience this motion as well. But, just the movement of ions does not induce any capacitive current. It is only at the wall where there is a net charge density, the motions of these ions will create a finite capacitive current [221]. An expression for this flow induced current in the system is given by eqn. 4.18.

퐼 = 푈휌푑퐴 (4.18)

Eqn. 4.18 is independent of the source (pressure or electric field) inducing the current. As in case of the capacitive-flowrate sensor, if the pressure gradient is considered within the EDL limit, we can get a straightforward description of the current. If a pressure ∇푃 is applied in the x-direction, it causes a parabolic flow profile with a velocity distribution given by eqn. 4.19. 1 푑푃 푈 = − (푑 − 푦) (4.19) 2휂 푑푋 This is derived in a similar way of the solution to Poiseuille’s equation (Appendix F-eqn. A7.10). 97

The charge density can be determined at the wall if there is a specific interfacial potential at the surface (φ = 휑). For the given 휑 which is determined by the chemistry of the surface, the potential distribution is given by eqn. 4.20. (||) φ = 푒 (4.20) 휑 where, d is the distance of wall from center of channel and |푦| is the absolute value of ‘y’. Eqn. 4.20 describes how the charges are distributed in the system from which the net charge density can be calculated.

From the previous sections, it was concluded that, for a linearized case, 휌 tends to decay exponentially away from the wall with a decay length of 휆 as shown in Fig.

4.9. In a system with EDL, 휌 only exists very close to the wall. Magnifying at the EDL and plotting the pressure induced velocity distribution together results in a distribution shown in Fig. 4.10.

Figure 4.10 Intercept of ρE and U distribution inside the Debye length.

Velocity distribution at the length scale within the Debye length limits looks like a linear function even if the pressure driven velocity is a parabolic distribution on macroscale. The intercept shown in Fig. 4.10 is a multiplication of an exponential charge density distribution with a linear flow field distribution. Representing this analytically, the charge density per unit area is given by eqn. 4.21. 퐼 푑푃 휀휑 = − (4.21) 퐴 푑푋 휂 From the analysis, we can determine the two other factors of the matrix given in eqn. 4.17. 휒 =휒 = − which describes the velocity distribution because of pressure 98

gradient and 휒 = 휎 + describes what happens due to ion transport caused by the flow. In summary, the electro-kinetic current and electro-osmotic current are similar in nature described by eqn. 4.22 and eqn. 4.23 [220]. 푄 휀휑 = (−∇ 휙) (4.22) 퐴 휂 퐼 휀휑 = − (−∇ 푃) (4.23) 퐴 휂 These two equations refer to a reversible process. An AC electric current can lead to pressure differences above the EDL layer that can lead to flow in the parallel direction, which is the principle of electro-osmotic pumps proposed for microfluidic systems [222]– [225].Conversely, an average velocity or volumetric flux in a forced flow can induce a capacitive current at the surface in a micro channel, i.e. a current density is generated by a gradient in pressure in electro-osmotic effect. This second process is not previously explored as a sensor to monitor flow rate changes in microchannels via capacitors. Therefore, the EDL formation and the impact of surface driven osmotic flow on the net charge density distribution can be captured using a printed capacitor and can be directly related to the flow rate. Development of such capacitive-flowrate sensor can be summarized as the detection of pressure induced current density which is directly dependent on four parameters: i) The fluid permittivity (fluid type or concentration), ii) the net potential distribution (pH) or surface chemistry (Debye Length), iii) the fluid viscosity (type of fluid), iv) the pressure differentials applied. Thus, it should be possible to pick up capacitively these four factors in a microfluidic channel driven with time dependent pressures. In what follows we provide experimental evidence and direct observations of this novel sensing concept that has not been explored before. Moreover, the electrodes are the printed digits of the capacitors, making them low-cost and easy to fabricate at many scales. 4.1.6 Design and development The capacitive sensor for the flowrate sensing application was printed using the Dimatix inkjet printing system and Novacentrix silver nanoink JSB40G. The capacitor (Fig. 4.11) had a length of L= 0.5 cm and electrode gap of 200 µm. In air, it had a 99 capacitance of 2.5 pF. The microfluidic chamber was designed to have a square central chamber of 1cmx1cm. The reason behind this design is to get the maximum sensitivity and flow rate responsivity. A two input one output inlet design was used. The design mold used for the PDMS channel fabricated is shown in Fig. 4.11.

Figure 4.11 Dimatix printed capacitive-flowrate sensor.

OEM syringe pumps were used to pump the fluids into the chamber. Syringepump-pro software was used to program to set and adjust the flow rate to the desired value. Three to four different flow rates were tested on each fluid under test. The program was set to create pulses of flow rates (n-second flow, m-second pause). Initially the ‘n’ and ‘m’ values were assigned to different values. But, to test the flow rate sensitivity on a periodic scale, the values of both ‘n’ and ‘m’ were set to 30 s. The flow rates were set at 100µL/min, 200 µL/min, 500 µL/min, 1 mL/min, 2 mL/min and 3mL/min. For any given test, depending on the polarizability and ionic concentration, four of flow rates were assigned to be tested. Various tests with different solvents containing ions of different concentration were performed. The test setup is shown in Fig. 4.12. All the tests were performed at room temperature.

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Figure 4.12 Capacitive-flowrate sensor test setup.

The input pressure gradient was a typical square wave with amplitude determined by the flow rate and pulse width of 30 s. FDC2214EVM CDC was used to record the capacitance data output corresponding to the applied pressure pule. The data obtained was post processed using MATLAB to create a moving average filter with dynamically assigned window size that depends on the signal to noise ratio of the raw data. 4.1.7 Results 4.1.7.1 Pure polar fluids All measurements were taken using the same capacitive-flowrate sensor for better comparability and analysis. The initial flow test was done using DI water – a polar solvent with no or negligible number of ions. Four different flow rates were tested; F1=100 µL/min, F2=500 µL/min, F3 = 1 mL/min and F4=2 mL/min. The flow rates were dynamically changed during the experiment to test the robustness and sensitivity of the flowrate sensor. The flow pattern used was [F2-F4-F1-F3]. It is important to re- emphasize that the pulsed flow happens in 30 s ON/OFF square-wave pattern and different flow rates (Fn) changes the strength (height) of this pulsed-flow. The comprehensive response of change in capacitance is shown in Fig. 4.13. It is clear that measured capacitance displays a saw-tooth pattern with clear correlations between the flow rate and the capacitive signal. Capacitance response was not observed at lower flow- rates (F1 = 100 µL/min). 101

Figure 4.13 Comprehensive response of varying flow rate test using DI water.

More specifically, it can be observed that there is a distinguishable difference in the area and slope of the ‘teeth’ in the patterns capacitive response to different flow rates. As explained in the previous sections, the polarizability of the fluid, causes a change in the electrical displacement and can be directly related to the potential change in the surface. The change in pressure gradient of the Poiseuille flow causes a switch in polarization of the fluid causing field to drop near the surface of the capacitor. This EDL driven potential change is reflected as a change in the IDE capacitance, thus essentially forming the basis for a capacitive-flowrate sensor. Note that there is an overall exponential ramp in the capacitance itself caused by the flow. The cause of this baseline offset could be one of the two reasons. It can be considered that there is a noise induced offset often observed in the system. The overall baseline offset is in the order of 0.7 pF. But this does not explain the decrease in offset at a lower flow rate. Therefore, even in the absence of ions in the fluid, the offset in the capacitance must be caused due to the compliance of the fluid flow as explained in Section 4.1.2. The compliance can be related to a net increase in the pressure driven volumetric flux. This also explains the decrease in ‘Q’ with decrease in flow rate caused by a reduced pressure gradient. In the case of ionic solutions, there can be an additional factor causing this baseline shift which is explained in the successive result discussions. A magnified version of every flow rate is presented in Fig. 4.14 showing a distinct capacitive amplitude change. Area under the curves were calculated using trapezoidal 102 integration approximation in MATLAB and it can be clearly observed that the area under the curve increases with increase in flow rates. Calculated area under the curves and rate of increase rate of capacitive response (leading slope) are presented in Table 4.3. While both of these figures increase with the flow rate, the slope appears to be more sensitive to the flow-rate changes It should be observed that the change in capacitance for the flow rate measurements are induced because of change in effective volumetric flux and polarization caused by the pressure gradient change. There were no ionic species in this case except for minimal H+ ions causing an EDL forced surface flow. The salt ions induced EDL causes the flow that has even stronger response as compared to the case on pure polarizable fluid which are explained in the successive results.

Figure 4.14 Capacitive flow rate responses of DI flow with magnified images showing unique increasing area with flow rates (yellow – 0.5 mL/min, red – 1 mL/min & green – 2mL/min).

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Moreover, there is a minimum threshold of flow rate sensitivity depending on the dielectric permittivity and polarizability of the fluid. In case of pure DI, we are not able to detect flow rates below 500 µL/min, which may change with the viscosity and Debye length of the fluid (DI water) in question

Table 4.3 Magnitude and slope of flow rate responses shown in Fig. 4.14. Flow Rate (mL/min) Area under the curve (pF.sample count) Slope (pF/sample count) 0.5 16.9902 ± 0.5 3.1e-5 1 17.3415 ± 0.5 4.58e-5 2 17.4655 ± 0.5 6.6e-5

To test the responsivity of the capacitive-flowrate sensor for a polar solvent with OH- groups, ethanol (dipole moment = 1.69 D) was used to and the experiment was replicated as in the case of DI. The observed capacitive response is shown in Fig. 4.15.

Figure 4.15 Comprehensive Capacitive-flowrate sensor response for varying ethanol flow rates.

Similar flow rates were used and dynamically varied along the experiment for ethanol. It can be observed that there is no unique response for change in flow rates except for the baseline offset and fall caused by the flow dependent volumetric flux. The reason for the unresponsiveness is because of the OH- termination which hinders the 104 creation of EDL. Since the glass surface is negatively charged in the EDL formation, the capacitive-flow rate sensor does not respond to a solvent with negative side-chain such as Ethanol due to absence of electrokinetic mechanisms explained in the previous sections. Clearly stronger polar nature of DI water is one of the requirements for the capacitive- flow sensor to work. 4.1.7.2 Ionic Polar Fluids The important component of electro-osmotic flow and electrokinetics in fluids is the EDL forming an ion cloud at the surface. The net current density changes and follows the current density and flux density conditions as mentioned in section 4.1.4. Another important criterion for the capacitive-flowrate sensor is ionic concentration in the fluid.

To test this hypothesis, Nickel(II)Acetate (Ni(OCOCH3)2·4H2O) was dissolved in DI to create an ionic polar fluid. As a proof of concept, a 1% w/v nickel acetate salt solution was made and tested using the exact setup and the same capacitive-flowrate sensor. Pressure gradients (flow rates) below 500 µL/min did not work for the ionic fluid either. So, flow rates of F1=500 µL/min, F2=1 mL/min and F3 = 2 mL/min were used for this test. The overall comprehensive response of dynamically changing pressure gradient is shown in Fig. 4.16.

Figure 4.16 Comprehensive Capacitive-flowrate sensor response for varying 1% w/v Nickel Acetate in DI flow rates.

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It can be observed that there is a clear change in the amplitude of capacitance change with increasing flow rates. The only difference to be observed here is that the capacitance change in the flowrate sensor decreases with every pressure differential applied. It has an opposite behavior compared to the DI counterpart. Recalling eqn. 4.18, the reason for this reversed response should be the divalent salt ion solution which causes a switch in potential (positive) at the surface on interaction with surface charges induced by the electroosmotic flow in the EDL [226]. The phenomenon is called ‘charge inversion’ and has been reported as a frequently recurring phenomenon while the electrolytic solution contains divalent ions [227]–[230]. Drop in capacitance with pressure pulses can be clearly observed in the magnified response of sensor shown in Fig. 4.17. The extracted features of the capacitive signal (integral area and slope of rise) for different flow rates are detailed in Table 4.4.

Figure 4.17 Capacitive flow rate responses of Nickel Acetate flow with magnified images showing unique increasing area with flow rates.

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It can be noted that this flowrate response of capacitor is confirmed by a steady non-fluctuating capacitive response at the end of every flowrate before switching to a new flowrate. This intermittent period (~3000 sample count) where the pump is in idle mode without creating any pressure gradient, the capacitive response is non-pulsating which gives us additional confirmation on the concept of the capacitive-flowrate sensor driven by pressure gradients. When pulsation stops, we only see the linear change in the background associated with the compliance of the overall system as discussed earlier.

Table 4. 4 Magnitude and slope of flow rate responses shown in Fig. 4.17. Flow Rate Area under the curve (pF.sample Slope (pF/sample (mL/min) count) count) 0.5 21.0286 ± 0.5 2.5e-5 1 22.1009 ± 0.5 3.2e-5 2 22.9114 ± 0.5 1.1e-4

To prove that the flow rate sensor dependence is only weakly dependent on ionic concentration at high molarity levels, the same sensor was used to test a 10% w/v solution of Nickel Acetate for capacitive flow rate response. The results of this experiment are shown in Fig. 4.18.

Figure 4.18 Comprehensive Capacitive-flowrate sensor response for varying 10% w/v Nickel Acetate in DI water flow rates.

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The flow rates in this case were varied dynamically and not in an ascending order to test the accuracy, sensitivity and responsivity of the capacitive-flowrate sensor. It can be observed that the sensor response is almost instantaneous (except occasional transients in the initial pulse) with the flow rate. The pressure gradient applied to the system was the exact 60 s:60 s flow:pause pattern. For visualization purposes, the overlay of the input pressure gradient pulse applied over the acquired capacitance data for the case of 2 mL/min in this experiment is shown in Fig. 4.19.

Figure 4.19 Overlay of applied pressure gradient pulse compared to the capacitive response for a 2 mL/min flow-rate.

It can be observed from Fig. 4.19 that the response of the capacitor to the applied pressure gradient is also almost instantaneous. To check the independence of flow rate sensor on the ionic concentration, the magnified capacitive response of individual flow rates is shown in Fig. 4.20. It provides a proof of repeatability of the flow rate sensor for different flow rates. The response is also comparable to Fig. 4.16 and proves that the capacitive response is indeed weakly dependent on ionic concentration of the fluid (1% w/v vs 10% w/v). The increase in the overall capacitance value by 1pF in case of DI vs Nickel(II)Acetate experiments was due to the dielectric change caused by the initial flow of DI resulting a slight increase in dielectric constant. Although the capacitive area 108 change is comparable (Table 4.4 vs 4.5), there is a slight shift in slope which can be attributed to the weak dependence of ionic concentration in case of 1% w/v Nickel(II)Acetate and 10% w/v Nickel(II)Acetate concentrations.

Figure 4.20 Capacitive flow rate responses of 10% w/v Nickel Acetate in DI flow with magnified images showing unique increasing area with flow rates.

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Table 4.5 Magnitude and slope of flow rate responses shown in Fig. 4.18. Flow Rate Area under the curve (pF. sample Slope (pF/sample (mL/min) count) count) 0.5 20.9942 ± 0.5 1.3e-5 1 21.4440 ± 0.5 2.9e-5 2 21.9513± 0.5 1.06e-4 1 (Test #2) 21.4464 3e-5 2 (Test #2) 21.9438 1.1e-4

A second test of ionic dependence is carried out using a less polar fluids like ethanol. We needed to confirm that the salt added in fluid had no major role in creating the electroosmotic flow as discussed in previous sections. To prove this, a 0.1% w/v solution of Nickel Acetate was mixed with Ethanol, which is an electronegative solvent. In the earlier results presented in Fig. 4.15, it was clear that the electronegative nature of ethanol failed to produce a capacitive response in the flow-rate sensor. With the same solvent ethanol, which partially dissolves the salt, we try to justify the claim that the ionic species in the solution play a major role in the electrokinetic flow induced mechanism. As expected, there was no distinguishable indication of responsivity of the capacitive- flowrate sensor to this electronegative solvent even when salt is added, as shown in Fig. 4.21. The overall background rise and fall is associated with the compliance of the microfluidic system, and not driven by pressure gradients (pressure pulses). Thus, we can safely claim that ionic changes that can alter the Debye length, has very little influence on the nature of the capacitive signal. This is reasonable in the sense that capacitive sensor is driven by EDL formation and not its special extent which is well within its sensing range.

Figure 4.21 Capacitive-flowrate sensor response to flow of 0.1% w/v Nickel Acetate in Ethanol. 110

4.1.7.3 Capacitive-pH sensor As described in section 4.3.1.1, the electro-osmotic flow is a surface driven flow and is dependent on the H+ ions concentration of the fluid under use. A strongly alkaline fluid (pH = 11) with a large amount of OH- ions and negligible H+ ions would be unresponsive in the capacitive-flow rate measurements. In contrast, a strongly acidic fluid (pH = 3) will create a distinct response. Therefore, by observing a well calibrated capacitive response to pulsating flow in a fluid, the increase in acidic content can be detected in a given microfluidic setup. To prove this concept, the same capacitor used for flow rate measurements was exposed to two different standard pH buffer solutions with pH values on both extremes of pH scale. The flow rate was fixed at 2 mL/min with a 30 s flow-30 s pause pulsating pressure gradient applied at the input. The results with the magnified responses are shown in Fig. 4.22 and Fig. 4.23.

Figure 4.22 Capacitive flow-rate response at fixed flow rate (2mL/min) for pH = 11 solution.

It can be observed that there is no distinct flow rate sensitivity as expected. With no H+ ions, there is no surface driven flow induced potential or electro-osmotic effects. As expected, the capacitive-flowrate sensor is flowrate insensitive to the highly alkaline solutions with only glitches from baseline value (55.25 pF) caused by noise (higher in this case since no shielding was used).

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Figure 4.23 Capacitive flow-rate response at fixed flow rate (2 mL/min) for pH = 3 solution with magnified view. The large dip is the result of initial transient and most likely caused by an air bubble that got stuck and released, which drops the capacitor.

A very distinct flow rate response can be clearly observed for the same flow rate (2 mL/min) for an increased H+ ions concentration. Thus, in principle, we propose a novel concept of determining pH of the solution using the capacitive-flowrate sensor that has not been reported before. With further calibration, the capacitive-flowrate sensor has opened a path to develop a pH-sensor which can be used to determine the pH change of the fluid along with flow-rate measurements. One caveat for this proposed sensor is the fact that although ionic protons are essential for EDL formation on glass/PDMS layers and effect the Debye length, once the EDL is formed its relative change do not have large influence on the capacitive signal. Therefore, the expected pH sensor will not be linear. Instead it will have sigmoid-like non-linear dependence with good sensitivity only in a limited range. Moreover, the range of pH values that it will be transitioning is likely to be around pH7, depending on the actual solvent, surface chemistry and flow rates used. Thus, extensive calibrations are needed to explore the proposed pH sensor, which could be very useful, especially in biomedical studies where pH values of interest typically remain around 6.5. 112

4.1.8 Summary In our exploration of novel physical sensing mechanisms for printed capacitors, flow-sensing has been identified and explored in this section. This is a novel application of capacitive sensing that has not been documented previously. Appreciation of this novel sensing platform requires a deep understanding of surface-based electro-kinetic flow, electro-osmotic effect, EDL formation and Debye Length. A capacitive flow-rate sensor is proposed by exploiting the electro-kinetic flow mechanics. The reverse mechanism of using an electric field to induce a flow inside a microfluidic channel has been vastly explored in the past in creating an electro kinetic pump [231]. But, the reverse mechanism was not employed previously for detection. Thus, we show that a (printed) capacitor can be used to capture the change in a pressure driven flow rate inside the microfluidic channel. The proposed sensor  can be used to track flow rate change in polar ionic solvent and can be uniquely calibrated for different fluid types. Non-polar solvents and electronegative solvents which are commonly poor solvents of salts cannot be used in the developed sensor due to lack of polarization and ionization (EDL).  is largely independent on ionic concentration of fluid under test change in Debye lengths does not significantly alter the capacitance near the electrodes. The weak dependence on the ionic concentration is due to highly non-linear response that must be studied in greater detail.  On Silinol (Si=O) terminated surfaces found in glass, PDMS or silicon substrates would be sensitive to pH of the solution in a highly non-linear fashion. This could be a means to build a pH sensor in a specific window near pH7, interest to biomedical and LoC systems  can be applied to other substrates with a net positive surface charges, whereby alkaline solutions and anionic polar fluids can be detected and analyzed. It would be prudent to analyze the general feature of the proposed capacitive flow sensor. A brief comparison of the sensor developed with a commercially available flow rate sensor is given in Table 4.6. As is evident from this table, the proposed flow sensor has a number of redeeming and even unique features as compared to the state-of-the-art commercial products such as 113 ultra-small dimensions, non-moving parts, high accuracy (based on CMOS capacitive sensing technology), low sensitivity and low power. However, it may require more careful calibration and may not respond well to non-polar solutions and specific solvents.

Table 4.6 Comparison of sensor characteristics of Capacitive-flowrate sensor vs Commercial flowrate sensor Sensor Flowrate Type Commercially available Capacitive sensor Product DIGITEN G3/4 Water Capacitive-flowrate Flow Sensor sensor Operation Principle Rotor/Hall Effect – Capacitive Mechanical Size 10cmx10cmx5cm 1cmx1cmx100µm Reliability N/A 0.005519 (Highly Reliable) Precision/ 90% to 95% 97% Accuracy Repeatability N/A 0.0039 – Repeatable Adaptability in LOC No Yes Selectivity Different Sensor type for Ionic Polar Solutions Different Liquids Average Cost $10 <1¢ Detection Limit 1L/min 100µL/min (Resolution) Range 1L/min – 60L/min 100µL/min – 5mL/min (Fixed) (Variable - Microchannel Design dependent) Response Time <1s <20s Operating Temperature ≤ 80ºC ≤ 200ºC Liquid Temperature ≤ 120ºC ≤ 120ºC Portability Portable Portable Power Requirement 5V 5V Lifetime N/A N/A Development Complex Very Simple Calibration Not required Required before first use.

4.2 Capacitive-Thermometer (CapT) In this section, the Printed Interdigitated Capacitor (IDC) is explored and optimized to be used for another unique application which has not been explored before. The printed capacitive-thermometer utilizes the temperature dependent capacitive 114 properties along with an added high-k ferroelectric temperature sensitive dielectric material. The effect of temperature on the dielectric constant change is a complex phenomenon involving polarization physics that is unique for every material. The rise in temperature alters this intrinsic property in alternating ways depending on the material composition and defects. The overall goal in this work is to design a capacitive thermometer embedded in the microfluidic device replacing the traditional thermocouples for temperature detection, which has not been analyzed in the existing literature. Moreover, an all-passive LoC platform utilizing only resistors and capacitors would avoid fabrication complexity and eliminate the need for additional sensor interfaces, high-resolution analog-to-digital circuitry. A general sketch of the targeted LoC platform is described in Fig. 4.24, which is intended to serve the following four purposes: i) cell counting; ii) cell velocity differential measurement; iii) cell culturing via resistive heating in central chamber, and iv) temperature & flow monitoring. In doing so multiple capacitors (CapT1, CapT2, CapT3) are used for both proximity sensing as well as thermometers. In the present section, we focus on the design, characterization and analysis of printed capacitors thermal response on various substrates (glass, polymer, paper) and within a PDMS based microfluidic device. It is shown that this predictable thermal response can be translated to temperature measurements not only for LoC applications but also for other applications where accurate measurement of capacitor response is available.

Figure 4.24 3D representation of LoC created with microfluidic channel capped over the printed passive electrodes

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The concept of CapT is simple: characterize and calibrate the temperature response of IDCs so it can be used to track temperature. This intrinsic response is characterized on multiple flexible substrates and at different humidity levels. We then enhance this response by introducing add-on dielectric layers such as polymers (PMMA, PDMS) and high-dielectric constants (so-called high-k) ferroelectric layers Barium

Titanate (BaTiO3), with a Curie temperature of 120ºC. Therefore, no tests were conducted beyond 120ºC as BaTiO3 undergoes phase changes its structure. But, in the temperature ranges intended to use, it maintains the tetragonal structure. Above the curie temperature it goes into the orthorhombic structure where the impedance dependence becomes complex. The dielectric constant change with temperature are caused due to multiple reasons. With the increase in temperature, the oxygen molecule gets ionized and causes oxygen vacancies as a result of higher intrinsic polarization and this in turn increases the dielectric constant. Moreover, change in intrinsic ferroelectric response also increases the dielectric change. Enhancement of mobility of charge carriers can also result in increase in the dielectric response [232], [233]. 4.2.1 Design and Development for CapT Our target applications are consumer products and LoC systems, therefore the CapT developed will be able to detect temperature changes within the range of 10ºC to 100ºC accurately. To illustrate the concept, the thermal response of a commercially available ceramic capacitor (C = 10 nF) is tested and the response of this capacitor is shown in Fig. 4.25.

Figure 4.25 Thermal response of a standard ceramic capacitor (C = 10nF). 116

The noisy nature of the raw data is due to mechanical vibrations created by the hotplate in the process of heating. A simple yet robust GUI is developed using ‘C*’ and Matlab code which eliminates the background noise and could be interfaced to produce live moving average results. The GUI used for smoothening in this process involves post processing of the acquired raw data. The noise elimination/data smoothening GUI is named “Data Cleanser”. For a standard ceramic capacitor, the capacitance and temperature are related by the Capacitance Temperature coefficient which is given by eqn. 4.24

훼 = × 10 ppm/ºC 4.24 () where 훼 is the capacitance temperature coefficient, 퐶 is the measured capacitance at a given temperature ‘T’, 퐶 is the capacitance at room temperature. In CapT, the capacitance coefficient is the slope from the C vs T curve and is dependent on the capacitor design with the core influencer being the dielectric material used. The frequency dependence of dielectric constant of each substrate was extracted by determining the capacitance change with the frequency sweep (1 kHz to 2 MHz) of a parallel plate capacitor fabricated with a fixed area. Fig. 4.26a shows these sweeps done on six different substrates. Then, using the basic capacitance relation, C = εA/d, the dielectric coefficient corresponding to measured capacitance was extracted. In the development of capacitive-thermometer, various substrates like soda-lime glass (εr = 7.3), paper (εr = 2.3), polyethylene terephthalate (PET) (εr = 3.4), polyimide (εr

= 2.9), polyamide (εr = 2.8), and glossy paper (εr = 2.5) were studied. These are some of the most popular substrates in creating flexible sensors and LoC devices. Fig. 4.26b shows the IDC’s printed on different substrates. A unique response was expected out of the capacitive-thermometer printed on each of these substrates. Novacentrix JSB40G silver ink used in printing of these IDC’s using Sonoplot Microplotter had a nanoparticle size of 60-80 nm with 40% silver concentration, viscosity of 8cP and a conductivity of 6.3e7/Ωm. The sample capacitors and frequency sweeps shown in this plot attest to the fact that we have fully functioning printed capacitors in a variety of substrates.

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Figure 4.26 a) Capacitance dependence on the frequency sweep for printed IDC’s used for comparison and control b) Printed IDCs on 4 different substrates.

Teca AHP-301CPV air-cooled thermoelectric cold plate with an internal pulse- width-modulated (PWM) temperature controller was used to quasi-statically sweep the substrate temperature from 10°C to 110°C over 15 mins. The relative humidity condition was maintained at 35% during the temperature sweeps. In temperatures below 10°C, we observe a condensation effect which is detailed in Section 4.2.2. This is not an issue as the nominal temperature ranges necessary for a typical cell culture falls between 15°C to 40°C. Temperature sensitivity of capacitance at these temperatures were optimized by use of a CMOS-based (Texas Instruments® FDC1004EVM), four-channel capacitive to digital converter. This evaluation board allows parallel measurements of 4 different capacitances down to 0.5 fF, which helps in concurrent evaluation of different capacitors under similar conditions for improved comparison. Moreover, thanks to the built-in ADC, I2C interface and MCU facilitates data acquisition mode. This card has a range of ±15 pF with a resolution of 0.5 fF, a fixed excitation frequency of 25 KHz. For the frequency- dependence of dielectric constants determined using a parallel plate capacitor with C ≥ 15 pF, we use the FDC2114EVM evaluation module which has a higher range up to 250 nF with a tunable excitation frequency up to 10 MHz. Post processing of the temperature and capacitance data acquired were done using a GUI created using C# and further data 118 calibration required for comparison was done using a Matlab® script. The complete experimental setup is shown in Fig. 4.27.

Figure 4.27 Experimental Setup: A – Printed Interdigitated Capacitor (IDC), B – Teca Hot/Cold plate, C – Computer control/monitor station and D – Capacitance Sensing Chip.

4.2.2 Results 4.2.2.1. Proof of concept To test the capacitive dependence on temperature for a conventional parallel plate capacitor, four parallel plate capacitors were made by placing fixed size copper contacts and sandwiching the polymers in between. The four dielectric materials used to create these capacitors were Glass, Kapton, PVDF and paper. A similar temperature sweep at two fixed humidity levels were performed. The test setup is shown in Fig. 4.28. Since both electrodes trapped the dielectric material except for a small change in dimension of both electrodes, a very slight shift or no shift in capacitive response corresponding to humidity level.

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Figure 4.28 Parallel plate capacitors - test setup.

As expected, the temperature sweep resulted in a clear capacitive change (Fig. 4.29) with temperature sweep. The change in humidity caused a slight shift in capacitive measurements resulting from the difference in size of the electrodes. The humidity seeping through this gap, causes a shift in capacitive levels. It should be noted that, the capacitance itself is huge in comparison to its printed counterparts (examined later in this chapter) due to increased dimension (5 cm x 5 cm) of electrodes.

Figure 4.29 Parallel plate capacitive response to temperature sweep at different humidity levels.

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Furthermore, a humidity sweep was performed on the four parallel-plate capacitors at a fixed temperature (35ºC) to confirm the capacitive response and resulted in a minimal change in capacitance with humidity sweep (Fig. 4.30), which shows that IDCs also work as effective humidity sensors due to their open geometry and influence of water content on the dielectric media.

Figure 4.30 Humidity sweep at a fixed temperature (35ºC) parallel plate capacitors. As expected parallel plate shields the capacitors from any significant variations

4.2.2.2 Printed IDC Thermometer IDC’s were initially printed on glass substrate and their thermal sensitivity was characterized. An interesting observation was made on the first run of temperature sweep where the condensation effect was observed. It is evident from Fig. 4.31 that that the capacitance tends to become inversely proportional to the temperature at range below T = 18°C. This happens due to the condensation of water from ambient humidity, increasing the dielectric constant of the gap between the electrodes. This effect is observed with the capacitors printed on all substrates as well as those with air or polymeric fillers used as dielectric layer. Accurate knowledge or control of humidity is thus especially desirable in experiments conducted below 20˚C where IDC is to be used for temperature control. Moreover, the use of polymeric dielectric fillers for IDC (explained in future sections) appears to exacerbate this situation, rather than alleviating the condensation. Although 121 counter-intuitive, this outcome is likely to result from increase surface area of the IDC or porosity of the polymer fillers, causing a larger overall change in the dielectric constant, which needs to be investigated further. None-the-less it is easy to distinguish between nominally increasing thermal behavior versus condensation, and an adequate fit can be still generated for reliable calibration. Furthermore, in great majority of biomedical experiments and consumable electronics the focus is on the upper range of temperature, where IDC thermometers would be well behaved. In fact, even at lower temperatures IDC thermometers will either completely isolated from the microfluidic channel or ambient, and the condensation related complications are not expected to be a significant issue.

Figure 4.31 Filtered data showing the condensation effect observed on printed capacitors on glass substrate. Humidity level of 50% was higher than standard measurements.

4.2.2.3 Enhancing Capacitive-Thermometer Response In most practical cases, IDC will be passivated with or buried under additional dielectric layers, which can affect their performance and thermal response as the high-k dielectric matrix replace the air gaps between the IDC electrodes. Similarly, the choice of substrate can also be used to alter the thermal response of the capacitors. Thus, capacitive thermometers can be designed to enhance both their measured capacitance value and the sensitivity levels. In fact, even in a given (glass or paper) substrate and (PDMS or wax) microfluidic layer, specific polymer layers can be used as fillers to increase the capacitive response. To explore such scenarios, in this section we utilize different polymer fillers such as Acrylonitrile Butadiene Styrene (ABS) (εr = 3.2 to 3.3 @ 1MHz), Polylactic acid 122

(PLA) (εr = 3.1 @ 1MHz) and Polydimethylsiloxane (PDMS) (εr = 2.3-2.8 @ 1MHz) on glass substrates. A comparative thermal response of these capacitors is provided in Fig. 4.32. It is observed that indeed capacitance values go up and slope of the curves slightly enhanced when polymeric fillers are used. However, as the dielectric constant of these polymers are not terribly large as compared to air, the actual enfacements is limited percentage, as summarized in Fig. 4.32.

Figure 4.32 Capacitance vs temperature plots (polynomial fits) of printed capacitors on glass substrate with control air (no filler) and different polymeric dielectric fillers atop. Humidity level of 50% was higher than standard measurements.

Thermal response of printed IDCs was also tested on different polymers like polyamide (PA) and polyimide (PI). But the response, as shown in Fig. 4.33, was not vastly different from the ones with polymeric filling or control (air dielectric) CapT.

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Figure 4.33 Capacitance vs temperature plots (polynomial fits) of printed capacitors on PA and PI substrates. Humidity level was standard 35%.

To induce a comparatively larger thermal response, it is necessary to use a high-k (large dielectric constant) material with a good sensitivity to temperature changes such as

BaTiO3, a ferroelectric ceramic with a relatively high dielectric constant (εr = 20-70) that can even reach values as high as εr =7000 in unique lattice structures [234]. To engineer a more effective composite filler with large dielectric constant, therefore, we suspended

BaTiO3 nano-powder (d=100 nm) in a ferroelectric polymer, Polyvinylidene fluoride (PVDF). This requires a careful mixing of two separate suspensions of 5% PVDF and 5%

BaTiO3 nanopowder in N,N-dimethylformamide (DMF) at 343K for 6 hrs, as explained by Gaur et al. [235]. For comparison, we also designed an IDC with BaTiO3 thin-film filler, formed by evaporating Acetone from a 10% (w/w) suspension of the nanopowder and annealing at 250˚C for compaction. Hence it was possible to explore a broader range of fillers on glass substrates and design high- sensitivity printed IDC’s as outlined in Fig. 4.34. According to Fig. 4.34, which shows both the actual changes in capacitance measured using an LCR meter (Agilent E4980A) and temperature response for each IDC with fillers, PLA or PDMS may be good alternatives as polymer fillers and BaTiO3- PVDF composite matrix can enhance thermal response by almost 45%. Given that actual

BaTiO3 thin-film enhancement is 88%, composite filler offers a very reasonable alternative with higher structural integrity and lower porosity. 124

Figure 4.34 Comparison of effects of different polymer fillers on the capacitance and thermal response (average of five measurements for each polymer).

Besides filling the gaps between the electrodes with the BaTiO3/PVDF nanocomposite we can also utilize this high-k dielectric layer as a substrate. To do so, the nanocomposite polymer solution was spun on a glass slide at the speed of 500 rpm for 60 s and baked on a hotplate at 200°C for 2 hrs to create a thin film. The IDC thermometers were then printed on top of this thin film. The results obtained from both approaches, nanocomposite filler versus thin film substrate, are compared in Fig. 4.35. Not only does the filling method results in a larger thermal response, it is a more linear one extending up to 90˚C. Despite higher overall capacitance, the nanocomposite substrate results in a more non-linear and narrow relative change, splitting into two distinct regimes before and after ~50˚C.

Figure 4.35 BaTiO3 filler thermal response enhancements: 44.7% results from BaTiO3/PVDF nanocomposite filler (left) vs 37.3% from BaTiO3/PVDF thin film substrate.

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4.2.2.4 Flexible Paper CapT In this section, the extension of the IDC thermometer concept onto flexible substrates used commonly on printed electronics is explored. This includes both polymer and paper substrates, as shown in Fig. 4.36 that depicts the thermal behavior for two IDC’s printed onto PET and glossy paper (PHD X98). IDC on PET polymer has a very well-behaved thermal response, whereas an IDC on the paper substrate has rather non- linear and noisy characteristics. Despite the high-density ultra-flat paper used, porous and perishable nature of the paper clearly limits the usable range of the IDC thermometer 30˚C to 50˚C, beyond which paper IDC is not reliable. Above this range, paper may deform and wither. Below this range paper does suffers from condensation effect in two phases. First, the slope changes dramatically and non-monotonically down to 0˚C, presumably due to the porosity that can absorb the atmospheric humidity. Then below 0˚C the usual condensation peak appears. Humidity also impacts paper IDC response significantly, which must be taken into consideration. The upside is the fact that 30-50˚C range is suitable for body temperature monitoring which could be used in wearable electronics applications as well as many biological decay processes in food monitoring & transportation. These applications may still use paper-based IDC thermometers if necessary.

Figure 4.36 Thermal response of flexible IDC sensors printed on a) PET and b) PHD X98 paper substrate at humidity levels test1=35% and test2=50%.

Another novel application of IDC thermometers explored in this work is the testing and development of a quad-sensor design shown in Fig. 4.37a and Table 4.7. This quad sensor is printed on the PHD X98 paper using Dimatix Materials Printer DMP-2831 using the same silver nano-ink. With this design, four parallel measurements can be made 126 simultaneously, which can improve the accuracy of IDC thermometer on the paper substrate prone to deformation and environmental factors. Indeed, for better accuracy, the change in capacitance due physical bending and differences in expansion at higher temperatures can be taken into consideration in this design. In others inaccuracy or bending effects can be corrected or compensated in the quad-IDC arrangement for paper thermometers. In an attempt to explain how this could be accomplished, the change in capacitance caused by bending the quad IDC element along the axis shown in Fig. 4.43a is provided in Fig. 4.37b. The capacitance of the paper IDC changes as the angle of curvature (or diameter) of the insulated glass rod is varied. Electrodes in the capacitor pair C1 & C3 run around the rod circumference, while those in capacitor pair C2 & C4 run perpendicular to the rod circumference. Hence, C2 & C4 experience an increasing separation of electrode spacing as the bent radius is increased. It can be seen from Fig.4.37b that quad-IDC paper thermometer is capable of extreme bending down to a radius of 2.38 cm or more, which is a significant amount of bending in real life applications.

Figure 4.37 a) IDC thermometer quad sensor printed on PHD X98 glossy paper and b) its bending response.

Table 4.7 Results of Bend test on CapT Quad sensor CONTROL BEND1 BEND2 BEND3 BEND4 BEND5 92.5º 101.2 º 108 º 124.6 º 135 º C1 (pF) 824 672 630 656 745 583 C2 (pF) 714 533 481 445 517 485 C3 (pF) 835 772 608 662 656 648 C4 (pF) 747 502 483 420 470 435

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4.2.2.5 IDC Humidity Response Another factor that needs to be considered for the practical use IDC thermometers is the impact of humidity on temperature readings, especially in cases where there is no adequate water barrier atop the IDC or the filler and the substrate material is susceptible to water absorption such as the paper example above. It is shown in Fig. 4.38 that this is indeed a valid concern, where the thermal response of an IDC thermometer built on glass substrate is measured over the set temperature range (10 to 110°C) at two different humidity levels (31% & 37%). It can be observed that the thermal response has a clear positive shift to higher temperatures and there is a slight increase in the overall capacitive response at higher humidity levels. The condensation is also observed to move to higher temperature, which can provide a convenient means to calibrate humidity levels. The observed capacitance shift due to humidity is more pronounced at temperatures below 40°C which is the typical temperature range of interest for our intended applications. Hence, a detailed study of this phenomenon is useful before deploying IDC thermometers in the presence of large changes in humidity. In fact, based on such analysis, an IDC humidity sensor can be designed, which was also indicated previously by Swiss research team [236].

Figure 4.38 Response of a IDC thermometer on glass substrate under different humidity conditions.

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In Fig. 4.39 we extend this initial result by sweeping paper-based IDC thermometers in full range at 4 different humidity levels. The sweeps were repeated 4 times (2 up and 2 down sweep in a raw) at controlled humidity levels. Besides showing large hysteresis, the plots are also perturbed with large and random vertical jumps, especially in drier conditions. In other words, the paper capacitors may be prone to larger errors in the dry conditions, especially initially, as they become drier. This can be due to water vapor degassing over longer periods from the porous paper media. As a consequence, paper CapT devices should be used with precaution in lower humidity levels or when humidity changes are very rapid.

Figure 4.39 Inkjet printed - paper substrate capacitive response to temperature sweep at different humidity levels.

Comparative Analysis: A comprehensive comparison of the CapT developed in comparison to a commercially available thermocouple is presented in Table 4.8. The statistical parameters are obtained from Fig. 4.40 where we compare the temperature recorded vs. obtained from a polynomial model fitted to the capacitive readings. The resulting comparison between the commercial and printed CapT device on glass indicate a number of competitive and problematic issues that must be considered for further development. 129

Figure 4. 40 CapT plot used for comparative analysis.

Table 4.8 Comparison of sensor characteristics of CapT vs Commercial thermocouple. Sensor Thermometer Type Commercially available Capacitive sensor Product OMEGA SA3-K Capacitive-thermometer Operation Principle Seeback Effect Capacitive Size 5cm x 5mm x 5mm + 2mmx5mmx20µm Wire length Reliability Sensitivity 39 µV/ºC 68 µV/ºC 0.05 pF/ºC to 0.1 pF/ºC (Fig) Accuracy ±0.05 ºC ±2ºC (plot) (Fig) Repeatability Adaptability in LoC No Yes Selectivity Requires different types One sensor for all (J,K,T &E) for different temperature ranges. Temperatures. Average Cost $22 <1¢ Detection Limit 0.01C 1C (Resolution) Response Time <10s Instantaneous (<1s) Operating Temperature -20C to 120C (measured) Cold Junction Required Not Required Compensation Portability Yes Yes Power Requirement 5V Lifetime N/A N/A Development Complex Very Simple Calibration Not Required Required before first use

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4.2.3 CapT Summary The use of printed interdigitated capacitors as effective thermal sensors for microfluidic systems between 10˚-110˚C is explored in this section. IDC thermometers printed using Ag electrodes on glass, polymer and paper surfaces display a monotonous increase in capacitance in response to temperature increase, which was captured by a low-cost, high-resolution and compact CMOS capacitive sensor element. The calibration is accomplished by fitting polynomials of (7±2)th degree to data filtered using a moving- average window and a low-pass outlier filter, which is a sufficiently robust and efficient algorithm that can be implemented in real-time. Further improvements to sensitivity of IDC thermal response as high as 50% is possible via polymeric layers or polymer composite fillers with high-k BaTiO3 nanoparticles that can be laid on the IDC electrodes. Beyond microfluidic systems, it is possible to use IDC thermometers, even in the case of flexible applications with bending stress and humidity, with additional calibration efforts to offset such changes. Based on the results in this section, not only do we introduce a new approach to build a low-cost low-power thermometer in printed electronics that can utilize existing capacitive sensing hardware, we also illustrate how the device response can be improved and corrected due to other external factors. Thermal response of dielectric material (change in dielectric constant) used as a capping layer plays a major role in the temperature induced capacitive change. In case of no capping layers, the dielectric of the glass substrate increases with the temperature, in turn increasing the capacitance value. Although, other causes for capacitive changes which require deeper study is the change in the distance between the printed electrodes caused by thermal expansion. This effectively reduces the distance ‘d’ between the electrode fingers leading to an increase in capacitance. An ideal geometry of IDC to be used as a thermometer is completely application specific and depends on the range of temperature to be sensed and the dielectric material used as a capping layer. By optimizing the design and calibration, we can create a robust capacitor design with high sensitivity in the specified temperature range. 131

4.3 Motion (Counting) Sensor 4.3.1 Design and Development Impedance measurements have been widely used to detect and count the motion of floating bodies or elements in microfluidics [237]. Another widely used techniques are optical, fluorescent and nanopore detection of motion [238]. Capacitance cytometry has been explored in the past where parallel sputtered electrodes measure capacitive changes of a passing analyte [239]. In this section, a novel method of using a Sonoplot printed IDC to capture capacitive changes to count the analyte is detailed. Since IDC printing is straightforward and lower cost, this approach may be considered advantageous over the existing techniques mentioned above. The principle used in detection/counting is the momentary change in relative dielectric constant (εr) of the capacitor on arrival of the analyte or object. The jump in capacitance due to this temporary event is proportional to both the size of the object relative to IDC and its dielectric constant. Especially for counting purposes the best changes are between objects comparable in size but very dissimilar in dielectric constants. Although the IDC has its own parameters that determine the capacitance, as explained by the model in Chapter 2, the permittivity of the medium is always directly proportional to the magnitude of the capacitance.

Figure 4.41 3D printed Microfluidic mold used to create the capacitive-count sensor.

A simple single channel microfluidic device with single inlet/outlet ports was designed as shown in Fig. 4.41. The height of the inlet/outlet ports are 1mm and the circumference is 5 mm. The width of the channel was 100 µm. The height of the channel is a parameter which needs to be application specific. For test of solvent change or sizeable analytes, the channel height could be as large as a few millimeters. As the size or the circumference of the target object reduces, the channel height needs to be also 132 reduced to maximize the detected signal due to the dielectric change of the object. The closer the analyte is to the capacitor, larger is the amplitude change due to permittivity variation. A cross sectional representation of this his condition is explained graphically in Fig. 4.42. To fabricate channel heights below 50 µm, use of a SU-8 mold is better suited due to the limited resolution of the 3D printers below 50 µm. In cases where corrosive and strong solvents/acids are used as the buffer for analyte to be detected, the Ag electrodes must be protected with a micron sized conformal layer of an inert polymer. PDMS is the best suited for this protective layer since it is also the material of choice of the integrated microfluidic channel.

Figure 4.42 Significance of channel height design in a capacitive-count sensor.

Motion sensing is also dependent on the capacitor design. An increase in the number of electrodes with a minimum spacing between the fingers would not only increase the capacitance (As discussed in the model in Chapter 3), but, would also improve the resolution of the smallest object that can be detected with wood sensitivity. In this section, Sonoplot capacitors were used due to their minimal electrode spacing between the fingers which is important to sense dielectric changes (Fig. 4.43a). The Dimatix printed capacitor is shown as reference in Fig. 4.43b. The parameters used for capacitor design is detailed in Table 4.9.

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Figure 4.43 Capacitive-motion sensors Sonoplot printed IDCs are used in this work.

Table 4.9 Capacitor dimensions of Fig. 4.43. Parameters Sonoplot Dimatix Length of electrodes 2.5mm 10mm Width of electrodes 200µm 500µm Distance between electrodes 100µm 300µm Number of fingers 20 10 Default Dielectric constant 1 1 Capacitance 480 fF 2.4 pF

4.3.2 Results 4.3.2.1 Sonoplot printed capacitive-motion sensor For the Sonoplot printed capacitors with the given dimensions, the control capacitance (baseline measurement) read by the digital capacitance converter (CDC) card is ~59 fF. The TI FDC1004EVM was used to measure the capacitive changes because the range of capacitive change is well within the resolution limit of the CDC. An illustration of proximity sensitivity of the Sonoplot capacitor to human finger is shown in Fig. 4.44. However, this card does not offer baseline subtraction (offset capacitance), therefore actual capacitance values are used in all plots.

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Figure 4.44 Proximity Sensitivity of Sonoplot capacitive-motion sensor to human finger that approaches the IDC in four intervals (distance – 0 cm (in contact) to 10 cm away).

An IDC sensor can always be used to differentiate between different solutions, due to the unique dielectric property of each solution. In addition to the motion sensing, observing the initial step change from control-capacitance to the baseline capacitance (magnitude of capacitance after introduction of the solvent), we can determine the solvent used. For instance, the capacitive difference between DI water (εr = 80.1) and ethanol (εr

= 24.3) and isopropyl alcohol (IPA)(εr = 17.9) vs control is shown in Fig. 4.45. It is clear that the shift in capacitance is larger (0.498 pF) for DI in comparison to ethanol (0.2 pF) and IPA (0.15 pF). The tail end of the Air vs IPA plot shows the sensitivity to human finger 2mm above the channel. This illustrates the powerful sensing capability of the IDC for motion. Thus, such motion specific experiments must be done in a well-shielded environment both on the microfluidic system and overall setup. 135

Figure 4.45 Capacitive change between (a) DI (b) Ethanol & (c) IPA in comparison to control air capacitance. Pulses shows the sensitivity of the microfluidic system to finger taps to the top of the PDMS chamber.

The first printed IDC motion sensor test was performed in a steady flow of ethanol (100µL/min) intercepted by air bubbles (1mm±0.4mm) and a steady flow of DI water (100µL/min) intercepted by oil bubbles (4mm±1mm). These relatively large size bubbles are due to the use of 3D printed molds that tend to be fairly large (>100 µm in height). However, by lower the channel dimensions smaller bubbles can be easily formed and detected. The results are shown in Fig. 4.46 and Fig. 4.47 respectively. The sampling interval in these plots were set a relatively low 10sample/s but can be easily increased by 60 times if necessary.

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Figure 4.46 Capacitive-motion sensor detecting air pockets (1mm±0.4mm diameter) in a steady ethanol flow (100µL/min).

Figure 4.47 Capacitive-motion sensor detecting air pockets (4mm±1mm diameter) in a steady ethanol flow (100µL/min).

It can be observed from the figures above that a sudden change in the dielectric medium of the capacitor induces a sharp variation in the capacitance. The magnitude of change depends on the permittivity of the material. The dielectric peak magnitude of air pocket (εr = 1) and oil pocket (εr = 2.5) can be seen by the average peak value shift of 0.15 pF and 0.8 pF respectively. It can be also observed that the baseline capacitance has a trend where it ramps up and jumps back to its initial value. This phenomenon is shown in Fig. 4.48. The reason for this ramp is the flow dynamics as the static charges being build up on the capacitor electrodes during fluid flow in polar fluid, as explained in detail in Section 4.1 for the flow sensor. 137

Figure 4.48 Capacitive drift in baseline capacitance caused due to flow dynamics.

More specifically, the slow ramp in Fig.4.48between 52.95 pF and 53.1 pF is due to the electro-osmotic flow and electrokinetic effects inside the microfluidic channel as explained in section 4.1. What is surprising in this case is the fact that flow is supposed to be continuous and not in ON/OFF pulsed fashion. However, even in this case the stepper motors driving the solution are not truly continuous and operate intermittently due to the fairly small flow-rate. Therefore, the pulsed flow action is still in effect and the resulting charge build-up in the EDL can be detected. Furthermore, it can be seen that this effect is absent in Fig. 4.46 where an electronegative solution (ethanol) is used. This further proves the findings in the capacitive flowrate sensor developed. It is clear from these tests that printed IDCs integrated into the microfluidic channels have sufficient temporal, spatial and electrical resolution to detect motion of mm and tens of micron size objects with very high reliability. Furthermore, the supporting CMOS-based CDC reader can be very fast to keep up with both detection and counting with minimal power dissipation, without using expensive computational hardware for object detection. Finally, since both timing and amplitude of capacitive change is sensitive to the size and properties of the object being detected, such sensors can be specifically designed to be used for cell/vesicle or colloidal object recognition and sorting. 138

4.4 Nanogap Capacitors Our collaborator Prof. Anthopoulos and his group in King Abdullah University of Science and Technology (KAUST) have been able to develop capacitors with 10 nm spacing between two electrodes using a technique called adhesion lithography [240]. By having a nanosized gap, the capacitive sensing opens up a new and enhanced possibility to implement such nanogap capacitors for sensing applications. The significance of nanogap capacitors is the fact that the dielectric area being at such a small scale, is extremely sensitive to all the capacitive sensing techniques discussed in the previous sections. The analyte detection and transducing the capacitive signal would increase multifold with decreased dielectric area. One of the nanogap capacitors (Fig. 4.49) with Au-Al contacts is used to sense humidity levels in the environment in this work.

Figure 4. 49 a)Nanogap capacitors with Al-Au electrodes and 10nm gap in between the electrodes b) SEM image showing the 10nm gap between the electrodes [240].

A temperature sweep from 20ºC-100ºC was performed at different humidity levels (25%, 35%, 45% and 55%) and the corresponding capacitive measurements were performed using FDC2114EVM CDC. The results are shown in Fig. 4.50 (Time vs Capacitance & Time vs Temperature). There is a shift in y-scale (magnitude of capacitance) with increase in the humidity levels. With the increase in the water vapor content in the environment, the 10 nm dielectric is ultrasensitive to these minute changes. Although capacitive humidity sensing has been explored by researchers in the past [241]– [243], use of nanogap capacitors for humidity sensing is novel and the sensitivity of signal is comparatively better. Even a small change in atmospheric water content is clearly captured by the 10 nm dielectric region. 139

Figure 4.50 Nanogap capacitors - capacitive response & temperature response comparison at different humidity levels.

The noise in the signal and signal jumps are result of mechanical disturbance during the experiment. Moreover, there is a clear and stable differentiation of capacitive signals with increasing humidity. To reconfirm the sensitivity of nanogap capacitors to humidity, a humidity sweep was done between 25% and 45% for both nanogap and paper capacitors in parallel with temperature fixed at 35ºC (Fig. 4.51).

Figure 4. 51 Humidity sweep at a fixed temperature (35ºC) showing capacitive response of nanogap and paper capacitors (Inset - humidity sweep applied). 140

There is a distinct change in capacitance with increasing humidity. As expected, there is a repeatable and stable response in case of nanogap capacitors and a noisy and varying response in paper resulting from humidity getting trapped and bubbled out of paper pores. In summary, the nanogap capacitors which have added to reduced scalability have proven to be excellent humidity sensing elements. Further investigation of nanogap capacitors in sensing of various physical and chemical parameters/analytes shows a promising path and improvement potential for capacitive sensing technology. 4.5 Chapter Summary In this section, a summary of specific achievements in the development of capacitive-physical sensors are provided for clarity and brevity. 1) A unique capacitive flow rate sensor, which has never been reported before has been explored by exploiting the EDL related charge dynamics (electrokinetics) in a microfluidic channel driven by pressure transients. 2) Characterization of novel capacitive thermometers (CapT), exploiting the temperature dependence of high-k dielectric capping layers, have been provided, which can be used to track fluid temperature in the vicinity of a microfluidic channel. 3) A novel cross-capacitor design for flexible substrates have been proposed that can be used to correct for bending effects in measurements of thermal response as well as other sensing targets. 4) Capability of high-resolution printed IDC’s to detect arrival of and temporal changes to the mixture of fluids have been illustrated. The sensitivity of IDCs to the proximity and movement of ~100 µm to mm size particles were also proven, which paves the way for counting and sorting devices. 5) Novel nanogap capacitors were investigated for humidity sensing applications. They show excellent response to relative humidity in atmosphere. Further investigation of nanogap capacitive sensing for detection of various analytes promises a big step towards enhancement of capacitive sensing technology. The development of the Capacitive-physical sensors has opened a Pandora’s box for other potential sensing applications which could be developed with the findings which are detailed in Table 4.10. 141

Table 4.10 Applications and future work of capacitive-flowrate sensors. Sensor Application Future work Capacitive-  Micromixing  pH-sensor. flowrate sensor  Solvent polarity detection  Chemical dispensing Capacitive-  Cell culture  Humidity corrected thermometer  Chemical reactions thermometers.  Temperature sensitive  Flexible thermometers with experiments. bend correction. Capacitive-  Air bubble detector in  Cell counting. motion sensor medical technology.  DNA detection. Nanogap-  Relative Humidity sensing  Temperature corrected Humidity sensor in biological and chemical humidity sensors. experiments.  Nanogap capacitors for chemical and physical sensing applications.

It is evident that each sensor developed helps in understanding the other sensor with all the basics buried in the fluid-dynamics and physics of microfluidics. For instance, the electrokinetic effects in a motion sensor can be detected using flow rate sensor and corrected. The temperature changes in the flow-rate sensor channel can be detected using the capacitive-thermometer and corrected.

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Chapter 5: Capacitive - Chemical Sensors 5.1 Introduction This chapter of my research explains the application of the capacitive sensor for chemical sensing applications. The primary goal of this work was to create a Zinc- sensing platform for biomedical applications. Quantitative detection of Zn in simple and efficient manner is interest to several collaborators keen on developing unique LoC systems for cell culturing and monitoring. Along the way, additional metal ion sensing dielectrics also appeared as suitable pathways to create novel sensors which can be used for a wide range of applications. Hence, the primary sensing application for the capacitive-chemical sensors developed has gradually shifted toward heavy metal detection in water supply. Therefore, the broader vision for the use of proposed printed capacitors is to provide simple, reliable and ultra-compact electrical sensors that can interpret chemical environment and metallic contamination. Ultimately, the objective is to integrate the proposed sensors to a compact (mountable on to water supply pipes) and affordable (~$50) device with on-board wireless electronics for real-time monitoring of home water supply. There are three major classifications of sensing (Fig. 5.1) that can be used with the printed capacitors which include:  Electrode functionalization/affinity.  Proximity sensing (Touch-sensitive surfaces)  Dielectric Loading The capacitive-chemical sensor applies the dielectric loading method which detects the change in dielectric of the medium created by a chemically sensitive or absorbent capping layer. This method is simple, cost-effective, time and resource conserving in comparison to its other equivalents, especially the electro-chemical sensors that are large in size and requires frequent electrode replacements or maintenance. Therefore, in this chapter we explored types of unique, low-cost and thin-film compatible dielectric media that can be used for the detection of metal ions of interest (Zn, Cu and heavy metals). We also show that they can be incorporated into microfluidic devices so that they can be deployed for LoC and portable applications for solution monitoring.

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Figure 5.1 Depiction of dielectric loading method employed in capacitive-chemical sensor.

5.2 Capacitive-Chemical Sensors Detection of heavy metal ions using electrical and MEMS devices via adhesion to functionalized surfaces and chemical binding groups has been vastly studied in the past. The binding groups include modified chains of cystines, metalloproteinases, metalloenzymes, synthetic phytochelatins and other modified acidic chemical chains [244]–[254]. In this chapter, we looked into cost-effective, simple and naturally occurring minerals commonly employed for heavy metal sensing. The types of commonly found minerals as a potential sensing element in environmental and bio-chemical sensors using a (printed) capacitor is entirely novel and has not been explored before. 5.2.1 Magadiite

Magadiite (Na2Si14O29) is one of few naturally occurring mineral which has found its application as an adsorbent in water treatment and removal of toxic components. It has a unique, unparalleled affinity toward Zn2+ ions, as experimentally shown by Ogawa et al. [255], that exceeds that of even ion exchange membranes and other layered clays. The process of adsorption is commonly confused with absorption. The difference is depicted in Fig. 5.2. The adsorption, is a surface phenomenon in contrast to absorption which is a bulk phenomenon. The important factors affecting adsorption are temperature, concentration of solute adsorbed and the surface area of the adsorbent.

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Figure 5.2 Difference in adsorption vs adsorption (used in this study) process.

The primary building block present in magadiite is sodium silicate (Na2SiO3).

Magadiite is a mineral with a chain of Na2SiO3 bonds. The structure of this polymer chain is shown in Fig. 5.3. The Na+ ions are primarily responsible for the ion exchange in the adsorption process. The Na+ ions are weakly bonded to the oxygen ions in the chain and are easily replaced by the divalent cations in the solution. In the proposed capacitive sensors, this readily available and low-affinity sites for Na+ cations can be exploited for detection by exchanging larger metal ions of interest, Zn in our case, with weakly bonded Na+ cations.

Figure 5.3 Chemical structure of Sodium Silicate chain.

5.2.2 Ion Exchange Resins (IER) The Dowex G26 – Hydrogen form (DG26) is a strongly acidic cationic IER with a sulphonic acid termination suspended in a styrene-divinylbenzene matrix (Fig. 5.4). The 145

IER’s are insoluble in most of the major acidic, alkaline and organic solvents but on the other hand, highly reactive materials. Some of the current applications of the IER’s include water softening, DI water manufacture, radioactive waste treatment, water purification, desiccation, pharmaceutical and medical industry [256]. The application of IER’s in microfluidic LoC environment has been understudied. Although the ion- exchange membranes are used in battery and fuel cell applications as semi-permeable and selective barriers [257]–[259], they have never been considered for capacitive sensing applications. Therefore, there is sufficient merit to explore this class of materials for capacitive sensing and utilize them in compact and re-usable sensors that can detect heavy metal ions in uniquely designed microfluidic chambers integrated with capacitive sensors. Such unique integration could potentially pave way to a class of sensors as there are several such unique ion exchange classes specialized for different metal ion exchanges. Since many IERs can be reclaimed by a reversal of the ionic exchange, such sensors could even be re-usable in principle, which can be an environmentally friendly solution for a compact water quality sensor.

Figure 5.4 Structure of Dowex G26 IER beads.

The IER is shaped in the form of spherical beads (600 – 700 µm diameter) with the sulphonic termination spread throughout the circumference of the sphere as shown in Fig. 5.5. The cation IER can be either Na+ form or H+ form. The Na+ form is named standard IER and can be easily converted into H+ form which is commonly used for 146 demineralization and strong ion exchange processes by activating the resin in 1M HCl for 24 hrs. The simple understanding of the working of IER can be explained with the weak bonding of H+ ions of sulphonate group to the bead. In presence of any other positively charged ion in the solution, these H+ ions get replaced by the incoming positive cations. Similar to cation exchange resins, there are anionic exchange resins intended for alkaline anion adsorbing applications. Of course, it is worth mentioning that, the very property of reversible proton exchange of IERs in strong acidic environment also implies their use for pH sensing in highly corrosive applications. However, such an application would require more durable electrodes such as platinum or gold, for which low-cost nano-inks are not available for Dimatix and Sonoplot printers we utilize for capacitor development. Also, a separate inert dielectric layer to protect electrodes may be necessary in such an application that could lower the sensitivity of the sensor. Therefore, this unique potential is merely pointed out as a future work and not explored at the present time.

Figure 5.5 Optical view (50x magnification) of DG26 H-form IER with chemical- termination representation.

5.3 Design and Development

The sensing elements used in this study were liquid glass (Na2O(SiO2)x.xH2O) and DG26 strongly acidic cation exchange resin, both purchased from Sigma Aldrich. As sensing analytes, we utilize a number of model solutions with strong metal cations. All the metal ion solutions were made by dissolving the required weight of salt in the determined volume of DI Water (18 KΩ). The salts used were zinc (II) acetate 147

3 (Zn(CH3COO)2.2H2O) dihydrate with density of 1.73 g/cm , zinc chloride (ZnCl2) with 3 3 density of 2.9 g/cm , copper chloride (CuCl2.2H2O) with density of 2.51 g/cm and nickel 3 chloride (NiCl2.6H2O) hexahydrate with density of 1.92 g/cm . Due to health and safety considerations in our lab, which does not have suitable chemical hoods, we refrained from using heavy metal ions, such as Pb, Hg and Cd. However, in the case of IERs, their use in heavy-metal absorption is very well documented and all the observations for above metals for the DG26 resin can be generalized for these heavy metals. The capacitors used were printed using Dimatix printer and had two different designs. For the Na2SiO3 sensor, a narrower capacitor design; and for the novel IER sensor design, a broader capacitor designs whose respective dimensions are detailed in Table 5.1. Since the dielectric capping material was the solvent in the case of DG26 rather than the material itself (as in case of Na2SiO3), a larger capacitor was preferred for better electrical characterization.

Table 5.1 Design Parameters used for the printed capacitive sensors. Parameters Na2SiO3 Dowex G26 IER

Length of electrodes 5mm 10mm Width of electrodes 500µm 500µm Distance between electrodes 300µm 200µm Number of fingers 10 18 Default Dielectric constant 1 1 Capacitance 2.5 pF 8.3 pF

5.4 Results 5.4.1 Proof of Concept 2+ To test the responsivity of Na2SiO3 to divalent zinc ions (Zn ), a 5% (225 mMol) and 15% (675 mMol) solution of Zn(CH3COO)2 was prepared by dissolving zinc salt

(Zn(CH3COO)2·2H2O) with 99.999% metal basis and molecular weight of 219.51 in DI water (Molarity calculations – Appendix G). The Dimatix printed chemical-capacitive 148

sensor was capped (drop coated) with 0.5 mL of Na2SiO3 solution. The capacitive measurements were done using FDC1004EVM CDC and 1mL of Zn(CH3COO)2 was dropped on the sensor but not cured. The response of the sensor is shown in Fig. 5.6.

Figure 5.6 Response of uncured Na2SiO3 (liquid form) to varying Zn(CH3COO)2 concentrations.

It can be observed that the with the increase in the Zn2+ ion concentration in the solution, Na2SiO3 adsorbs the ions and thereby induces a distinct change in capacitance. This drop test uses a comparatively large quantity of solution (not comparable to the fluid flow inside the channel), but to start with, provided a proof of concept that Na2SiO3 is a promising dielectric sensing platform to detect divalent cations for printed IDCs. Next, to confirm the responsivity was distinct to divalent ionic solution, the

Na2SiO3 cap on the IDC sensor was cured at room temperature for an hour and was initially tested with pure DI water drop followed by 15% Zn(CH3COO)2 solution. As

Zn(CH3COO)2 (15%v/v) ionic solution is added to initially pure DI the capacitance increased. The sensing layer (Na2SiO3) clearly started adsorbing the divalent ions and reached saturation from the relatively larger volume of solution (~1mL) covering the entire capacitor and its surroundings. It can also be observed from the response (Fig. 5.7) that the capacitive increase is proportional to the increasing ratio of ionic solution (number of Zn2+ ions adsorbed). There is an initial 2pF change for the first droplet (4:1

DI:Zn(CH3COO)2 - 3% v/v) followed by a consecutive increase after 5 drops of ionic 149

solution (4:6 DI:Zn(CH3COO)2 - 6% v/v) and four added droplets (4:10

DI:Zn(CH3COO)2 - 11% v/v).

2 Figure 5.7 Response of cured Na2SiO3 to Zn + ions – Changing the ratio between DI water and 15% Zinc Acetate solution.

From Fig 5.6 and Fig 5.7, the capacitance change corresponding to the Zinc Acetate concentration was extracted and presented in Fig. 5.8. Since the data for 15% concentration reached saturation due to the capacitive digital card resolution limitations, only a positive standard deviation is presented for this data. The relation is fairly linear 2+ and makes Na2SiO3 an effective sensing medium to predict Zn ion concentrations. By changing the thickness, composition and electro-gap size, it should be possible to optimize the sensitivity (slope) according to the application of interest. Our interest lies in the identification of the effective dielectric medium; hence such optimization is deferred until the application is decided.

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2+ Figure 5. 8 Concentration vs Capacitance relationship for Na2SiO3 capping layer for Zn ion detection (three measurements per concentration).

To show the effectiveness of the proposed cured dielectric and contrast it to other sodium-rich dielectrics that actually react with the Zn, the same drop test was attempted using sodium carbonate (Na2CO3) layer. Sodium carbonate (Na2CO3) is soluble in water and therefore easier to be spun as a thin layer of dielectric sensing cap layer (Fig. 5.9). It can be observed that there is a robust increase in the measured capacitance, although it is not as prominent as the Na2SiO3 case in comparison. This is because, the reaction that takes place in the case of Na2CO3 is not a mere adsorption but a chemical reaction leading to phase change, as shown in eqn. 5.1. This chemical reaction does not create considerable change in the dielectric property of the capping layer, at least as compared to the massive response magadiite (Na2SiO3) displayed earlier. Weaker attachment of Zn2+ ions to silicate group in magadiite appears to lead to a more polarizable medium than the reaction product zinc carbonate in eqn.5.1

푁푎퐶푂 + 푍푛(퐶퐻퐶푂) → 푍푛퐶푂 + 푁푎퐶퐻퐶푂 + 퐶푂 (5.1)

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Figure 5.9 Sensitivity of Na2CO3 capping dielectric to 15% Zn(CH3COO)2 drop test.

Next, the water soluble Na2CO3 salt was added directly to the liquid

Na2SiO3 solution before it was cured at room temperature. The resulting complex

was then used as a dielectric capping layer to test if it improved the pure Na2SiO3 responsivity due to increased availability of the Na2+ concentration. Fig. 5.10

shows the response for this test. It can be observed that the added Na2CO3 +

Na2SiO3 composite did not lead to a significant overall change (ΔC ~ 0.15 pF) in

the measured capacitance with increasing Zn(CH3COO)2 concentration. In fact,

the addition of Na2CO3 even hindered the responsivity of the Na2SiO3 capacitive response which is extracted as a separate plot in Fig 5.11. It appears that instead

of supplying an additional pathway for Zn to be absorbed, Na2CO3 created a more effective (higher affinity) pathways thus stealing Zn atoms away from surface

states in Na2SiO3. 152

Figure 5.10 Response of Na2SiO3+ Na2CO3 cured capping layer for 15% Zn(CH3COO)2 drop test.

Figure 5.11 Concentration vs Capacitance relationship for Na2SiO3+Na2CO3 composite capping layer for Zn2+ ion detection (three measurements per concentration).

Once the adsorption response was studied by drop method, the same test was carried out inside a microfluidic channel. To begin with, a pulsating flow rate of 30 s flow - 60 s pause (flow rate = 1 mL/min) of 15% Zn(CH3COO)2 was performed. The response is shown in Fig. 5.12a. As expected, inside the channel, for a comparatively smaller volume of fluid in contact with the sensing element at a given time, the fast ramping of capacitance was not present. The sudden rise in the capacitance (between 5 – 8 min) is due to the electrostatic disturbance/noise captured by the capacitor. As the flow 153 continues and fills up the entire area of the capacitor, the measured capacitance settles down to the value corresponding to dielectric of the ionic solution. There was a slow but observable ramping of capacitance with the pulsating flow of controlled volumes. Another layer of complexity in the microfluidic channels is the electro-kinetic effects on the EDL at the liquid/glass interface, which was the subject of discussions for the flow-rate sensing phenomenon in chapter 4. Since the flow-rate of 1ml/min was fairly large, such electro-kinetic effect was observable. Thus, interpretation and analysis of the capacitive response is not trivial by any means. To simplify the picture, we can only consider the capacitance values when the pressure was OFF only. Thus, we can read the capacitance only from the top of the resulting waveforms, obtaining the pure ramping caused by the Zn2+ ion adsorption (~2 pF) as shown in Fig. 5.12b. It is evident that the capacitance values solely shifted even 20 min after the initial transients filling the chamber. Such changes in measured capacitance is related to the impact of prolonged flow on Na2SiO3, even after curing, which was less obvious in the drop test. It appears that the response of the proposed sensor is relatively slow and requires further study. Before additional work in device characterization Na2SiO3 curing needs to be improved to avoid erosion with continuous fluid flow in a microchannel, so it does not impact the magnitude of the capacitance over prolonged periods of exposure.

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Figure 5.12 Zinc adsorbed capacitive ramp caused by a pressure driven pulse inside the microfluidic channel.

5.4.2 Integration of Magadiite Zn Sensor 2+ Having shown that a Na2SiO3 presents a viable Zn sensing platform, the natural next step is to develop it to a fully characterized and integrated sensor. However, a number of challenges must be addressed before doing so. Some of the drawbacks faced in using Na2SiO3 even after curing at moderately high temperature of (50˚C) are:  pH Dependence. Zinc adsorption takes place rapidly initially and hits saturation gradually due to reduction in the number of binding sites over time. Also this adsorption process is primarily pH sensitive and for solutions with pH>7, the adsorption sharply falls down to zero at pH9 [260]. This can lead to reliability concerns as well as calibration challenges. 155

 Temporal drift: Solution/sensor contact time plays an important role in metal adsorption [261]. In case of magadiite, the need for prolonged exposure for noticeable capacitive change may result in erosion of the sensing material and causes random features on the surface morphology. Unless curing is exact and well understood, we cannot completely eliminate the effects of adverse erosion in the capping layer that can introduce fractures to the sensing surface and damage it irreversibly, even when the flow is off.

One way to make magadiite (Na2SiO3) layer more sturdy and insoluble is to cure it at elevated temperatures whereby it undergoes structural changes and expels all the water out of the composition [262]. Therefore, the Na2SiO3 capped layer was dried on a hotplate at 120ºC until it was free of all water molecules. But, this did not produce very smooth or uniform layers: It was not possible to control the structural changes Na2SiO3 undergoes during curing in air. Therefore, a reproducible structure was not possible by curing Na2SiO3 in air as shown in Fig. 5.13. It can be seen that the exact volume of

Na2SiO3 capping layers cured at same conditions can fluctuate significantly and have different surface areas.

Figure 5.13 Optical Images (10x magnification) of different Na2SiO3 capping layers cured at identical conditions.

Therefore, an additional approach is needed to improve the Na2SiO3 as a capacitive sensor dielectric before further sensor development can take place. Such 156 approaches may include development of novel polymer-magadiite composites or curing of magadiite in different ambients such as nitrogen or argon in various temperature ramp rates. Since presence of water is always an issue, the former approach will be a preferred solution with more favorable outcomes in the long run. Once such more stable dielectric is created, then a more precise and quantitatively reliable calibration of Zn2+ sensor can be attempted. 5.4.3 Ion Exchange Resin Based Capacitive Heavy Metal Sensors Another alternative dielectric medium suitable for metal ion detection via capacitive charge monitoring is ion exchange resins (IER). Originally developed for metal reclamation processes in industrial refuse, this material also enjoys the benefit of being reversible by re-exchange of the metals in strong acidic baths. In our sensor development work, the strongly acidic cationic IER (DG26)-hydrogen form with styrene- divinylbenzene matrix was used for metal ion detection. The IER is considered as a better alternative to Na2SiO3 for the following reasons:  It is insoluble in any medium (DI water, strong acids, strong bases and organic solvents).  The strong-acid cationic IER used in this case is sensitive to metal ions in the entire pH scale. Before using the as received material, the IER was soaked in 1M HCl solution for 24 hrs to replenish the H+ ion exchange groups on the surface. The activated IER beads were placed as a capping layer on the capacitor for the ion exchange process as shown in Fig. 5.14 to determine the effect of IER in the capacitive-chemical sensing process.

Figure 5.14 Bottom view of the IER’s used as a capping layer on the printed IDC. 157

The setup was a two-layer PDMS integration with the bottom layer consisting of the IER holding chamber to ensure the resin beads do not flow in and out of the input/output microchannel. The second layer (top chamber) consists of the actual microchannel itself. The depiction is shown in Fig. 5.15.

Figure 5.15 Two-layer microchannel integration with a) top chamber for fluid flow and b) bottom chamber for resin holder.

Unlike the Na2SiO3 cap, the resin beads cannot be bound to the capacitor which results in a situation where the resin beads must be placed in close proximity to the sensor. Also, the beads can be moving around with the fluid flow if placed in the flow channel. To overcome this challenge, the bottom chamber (Fig. 5.15) was included, which solely serves as a holder chamber for the IER beads so that they do not get washed away with the fluid flow. As a result, relatively larger IER beads (~500 µm) rest randomly over the IDC electrodes (gap = 200 µm), Thus it is expected that many more relatively stagnant fluidic pockets between the beads and electrodes will be established, as depicted in Fig. 5.16, which will have the largest impact on the capacitors. Clearly such spatial non-uniformities are troublesome for characterization, especially in an open flow system that can also lead to temporal fluctuations. However, very inert chemical nature of IERs and their mechanical sheer strength prevented us from casting, spinning or grinding of this material into other forms.

158

Figure 5.16 Cross sectional sketch depicting the assemble of IER on printed sensor.

As explained earlier, when the adsorption of ions occurs, the dielectric constant of the solvent should increase as depicted in Fig 5.17a (Andelman et. al). Therefore, as adsorption takes place, it is expected that lower ionic concentration inside the limited- flow fluidic pockets between the beads and the electrodes should lead to an increase of the capacitance provided that IERs have sufficient sites for adsorbing large amount of ions in the solution [263]. It must be noted that continuous flow on the upper side of the chamber leads to a higher diffusion rate keeping the ionic concentration steady. The rotating vortices inside the mini chamber will ensure that there is still flow at the bottom as well, but the established pockets will certainly slow the diffusion at the lower half. Thus, ionic depletion is likely to be effective inside these lower pockets of fluids.

a) b) Figure 5.17 a) Concentration vs Relative Dielectric constant for ionic solutions [263] b) Adsorption timeline for the cationic IER [264], [265]

159

Another aspect that must be understood is the fact that adsorption process takes place in the order of several minutes in the case of Dowex IER as shown in Fig. 5.17b [264], [265]. The saturation and peak adsorption phenomenon can take an average of 10- 20 minutes depending upon the pH, resin type, capacity and contact time. Thus, any capacitive changes could easily take place in minutes as opposed to seconds. With this background information in mind, the response of the IDC covered with IER beads to flow of cationic solution is shown in Fig. 5.18. To test the accuracy of the response for divalent cations, a 1% CuCl2 solution was flown at a rate of 50µL/min through the test setup, the very low-rate of flow was chosen to ensure that the beads do not move, and electro-kinetic effects detailed in capacitive-flow sensor (Chp.4) are not a concern. It can be observed that, with the flow and adsorption of Cu2+ ions, the capacitance ramps upward as the dielectric constant of ion-depleted solution inside the pockets near the bead-electrode interface rises. However, such rise is upset by intermittent movements of beads or inconsistent flow. When this happens, the capacitance reaches back to its original lower value due to increased circulation and diffusion in and around the ion-depleted pockets, lowering the local dielectric constant due to higher ionic concentration. This process repeats as the beads randomly and/or collectively move, as can be seen from bunching of peaks in Fig.5.18.

Figure 5.18 Capacitive response of IER to Cu adsorption with the solvent being the dielectric.

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This preliminary experiment shows that indeed IERs provide an opportunity to sense concentration changes for divalent metal ions. By exploiting the varying capacitance value, a quantitative conclusion about the presence of metal ion contamination in solutions or drinking water supply can be made with the help of IER’s. However, with the beads not anchored, the response of the system is dynamically varying and the change in capacitance values are dependent on the flow pattern of the ionic solution and position of IER beads in the chamber. Since the flow pattern through the crevices of the IER beads cannot be practically controlled, a novel design of microfluidic channel was attempted, and electrical characterization was accomplished as detailed in section 5.4.4. 5.4.4 Three Chamber Design To stabilize the response of IER beads and increase the reliability of the capacitive measurement, a novel triple chamber design (Fig. 5.19) was proposed and developed. The electrical conductance of a solvent is commonly used in practice to verify the ionic content of the solution. For instance, in preparation of DI water, the decrease in conductance (from ~1 S/m to ~1 µS/m) is a good indicator of completion of the DI water production process. However, such an increase could happen due to many types of ions as well as pH changes and is therefore not a good indicator of the type of contamination, especially when it comes to heavy-metal ions. In the proposed three-chamber design incorporating printed IDCs in a microfluidic channel could accomplish this analysis in a simple, cost-effective, repeatable and compact manner. The structure of this design is shown in Fig.5.19.

Figure 5.19 Three Chamber Design concept for metal ion detection using IER. 161

 The inlet chamber (chamber 1) has a printed cap-sensor to measure the initial capacitance of the ionic solution.  The middle chamber (chamber 2) is dedicated for the ion exchange process using the activated IER.  The outlet chamber (chamber 3) has a printed cap-sensor to measure the final capacitance of the ion-free solution after passing through the IER.  A two-layer microfluidic design is utilized in order to ensure IER beads remain immobilized and that the capacitive signal is stable. Three different divalent metal cation (Zn2+, Ni2+ and Cu2+) solutions were used to test the three-chamber setup. The DG26 IER has a selectivity towards individual ions determined by the degree of cross linking of DVB chain. For the 10% DVB crosslinking used in this experiment, the relative affinity hierarchy of IER , as compared to the control affinity of H+ ions normalized to a value of“1”, is Cu2+ (3.15) > Ni2+ (3.08) > Zn2+ (2.77) [266]. Thus, the IER is more sensitive to Copper than Zinc.

First, a 1% w/v solution of NiCl2 in DI water was passed through the setup. The response of the capacitive-chemical sensors is shown in Fig. 5.20. FDC2214EVM CDC was used to capture the values of two identical capacitors (capacitor1 (Chamber 1) and capacitor 2 (Chamber 3)) simultaneously. The measurement of capacitance of the ionic solution (Chamber 1) remains constant throughout the length of experiment. This is the capacitance value with the ionic solution as the dielectric layer. After adsorption/exchange of Ni2+ ions in chamber 2, the ion-depleted solution with increased dielectric constant (as explained in Fig. 5.17) enters chamber 3. If the change in capacitance 3 at the outlet chamber tends to be greater than change in capacitance 1, it clearly indicates the presence of metal ions in the solution which has been adsorbed by the IER. This gives us a definitive signal indicating the presence of metal ions in the solution under test. If the specific IER type is adopted, it should be possible to even distinguish between the ion types in parallel channels of similar types. Although both the capacitors in Chamber 1 & 3 were identically designed and printed (Inset in Fig. 5.21), the difference in the baseline value of both the capacitors is due to the variation in dimension of copper contacts used and software induced baseline shift present in the 162 capacitive digital cards. However, the main parameter of interest is the capacitive difference (ΔC) between the two capacitors, which is accurately tracked.

Figure 5.20 Capacitive response of three-chamber measurement setup with inset1 (right) showing the ion exchange process setup and inset2 (left) showing the discolored/swollen 2+ IER upon adsorption of 1% w/v NiCl2 in DI water carrying Ni ions @ 50µL/min flow rate.

The left inset of Fig. 5.20 provides us with an additional robust confirmation of presence of Ni2+ ions in the test solution. The optical image clearly indicates the IER swollen and discolored due to the adsorption of Ni2+ ions. The beads become greenish blue in color after the ion exchange process. Therefore, in addition to electrical characterization of solution, an additional optical confirmation for the presence of metal ions in the solution can be obtained using the IER and the three-chamber setup. Similar experiments using a 1% w/v of ZnCl2 and 1% w/v CuCl2 solutions were performed and the sensor responses shown in Fig. 5.21 and Fig. 5.22 respectively. In CuCl2 case (Fig.5.22), the recording was started after the initial chamber was filled up (inadvertently) with the ionic solution considering the comparatively reduced flow rate.

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Figure 5.21 Capacitive response of three-chamber measurement setup for 1% w/v of 2+ ZnCl2 in DI water carrying Zn ions @ 50 µL/min flow rate.

Figure 5.22 Capacitive response of three-chamber measurement setup for 1% w/v of 2+ CuCl2 in DI water carrying Cu ions @ 20 µL/min flow rate. Note that the measurement for Cap1 occurs after the chamber1 was full, therefore remains on average constant.

It can be observed that a similar capacitive response is displayed in both cases, with additional noise at the initial phase of the flow causing instabilities and bubbles which clear as the flow is stabilized. The slower flow rate used for the CuCl2 solution

(20µL/min) in comparison to ZnCl2 and NiCl2 case (50µL/min) was intended to test the 164 effect of flow rate on the adsorption process. As the flow rate decreases, the time for filling up the chamber to establish an equilibrium steady state dielectric capping layer increase. This is the reason for the slow ramping of the capacitance in case of CuCl2. Another important observation made is the contact time of solution with the IER. With decreased flow rate, the contact time increases, and more ions are adsorbed, which is also confirmed by the optical image displaying complete discoloration of IER beads (inset in Fig. 5.22). In contrast, with higher flow rate and decreased contact time, amount of adsorption is affected (inset in Fig. 5.20). This leads to an increased capacitance signal in chamber 3 of the setup. Therefore, there is a tradeoff between the rate of capacitance signal change and its overall magnitude in this setup. The use of IER for metal ion detection, does not only provide a simple cost- effective method in microfluidic channels, but also a reusable setup where the IER beads can be replenished to their original form for reuse. A simple flushing of the IER with 1M HCl regenerates all the H+ terminations on the surface of the IER beads. These IER’s can be reused until the lifetime of the printed sensor is reached. The regeneration process is explained in eqn. 5.2 and Fig. 5.23. 2+ + - + Resin-Cu + 2.H Cl  Resin- H + CuCl2 (5.2)

Figure 5.23 DG26 IER – Optical image (50x) of the three stages of IER – activated, ion exchanged, and regenerated.

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In order to differentiate between different ionic contaminations captured by the IER beads (e.g.: Ni, Cu, Fe, Hg, Cd, Pb), optical verification can be pursued with the help of an inexpensive white LED and several photodiodes with optical filters as verified in Fig. 5.23. In the case of colorless metal ion detection, this additional verification can be done by using a photometric ion sensitive salt. For instance, for Zn2+ ions, a salt called 2+ Zincon (C20H16N4O6S) which has been widely used for photometric detection of Zn ions even at very low concentration of 0.1ppm [267], can be used to optically verify the presence of the metal ion. This photometric metal ion sensing salt can be placed at the outlet container, where an optical color change will provide a secondary confirmation of the presence of metal ions in the test solution along with the capacitive-chemo sensor signal. 5.4.5 Software Development – CionXR The exact amount of IER needed for a specified concentration of metal ion removal can be calculated [268]. A software application has been developed to calculate the exact amount of IER required for a specified ion exchange process before placing it to the middle chamber. The sample calculation is provided in Appendix H, which can be used to calculate the exact amount of IER needed for any heavy metal cation in a given cationic resin. We have only considered cationic IER’s, which adsorb positive cations. In case of alkaline solutions where anions need to be adsorbed, a strong basic anionic resin should be used. Another advantage of this strong acidic cation exchange resin is that it works in the entire pH range. In the weak acidic cation exchange resins, a given type of resin works only in a specified set of pH range. For ease of calculations and future development, a novel software application named CionXR (screenshot – Fig 5.24) has been developed with this concept, using Universal Windows Platform (UWP) framework for Windows and Android platform. The user can input the solution & IER used, and the application calculates the exact weight of IER required for the ion exchange process. The MATLAB algorithm used for this development is given in Appendix I for reference.

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Figure 5.24 Screenshot of the Application developed (CIonXR).

The application can be also utilized to calculate the concentration of metal ions in an unknown solution. The CionXR usage proposed for such a calculation is detailed below.  Available information o An accurate volume of resin required for a given concentration o An accurate volume of solution containing specific ions. o The max capacitance value after the ionic solution enters chamber 1, “C2” in Fig. 5.25. For instance, recalling Fig. 5.20, the reference level would be 57.49 pF. o The capacitance of the filled chamber 3, shown as “C3” in Fig 5.25. After the ion adsorption in chamber 2, the ion-free solution enters and fills up chamber 3 between T1 and T2. In case of Fig 5.20, C3 would be 56.5 pF.  Assumptions o Flow-rate is constant. o Initial DC capacitance of sensor in chamber 3 is “C1”. o C2H

Now, as the flow continues (T2 to T3). After a specific period of time (T3), the IER beads are saturated (no more adsorption feasible), the original ionic solution starts filling up chamber 3. This results in a steady dielectric constant decrease (T3 to T4) and the capacitance value reaches the capacitance of chamber 1(C2) at T4. In case of higher ionic concentration (Fig. 5.25b), the IER beads of the same volume as in Fig. 5.25a would saturate in a shorter time scale. This would result in a shorter time interval (T2 to T3) of capacitive maximum observed in Chamber 3 and larger difference between C1 and C2H, as well as a decreased time to reach C2H (T3 to T4), as chamber 3 gets filled up with a concentrated ionic solution. In contrast, in case of lower ionic concentration, the time (T2 to T3) will increase as a result of longer adsorption process and correspondingly, the time (T3 to T4) will also increase for C3 to reach C2L. Hence CionXR uses the above capacitive differences and times to calculate either the concentration of the solution charging the IER for a chosen salt, or the time it would take to exhaust IER’s adsorption capacity for a limited number of site in per unit volume.

Figure 5.25 Prediction of ionic concentration of solution using CionXR.

The high selectivity of Zn sensing using Na2SiO3 has proven to have a sensitivity of 10 pF/M. The biologically relevant sensing regime is in the order of µM. The current resolution of pF/M is equivalent to aF/µM. To tailor the existing sensor to be sensitive in 168 such reduced molarity levels, we would be able to use the high-resolution CDC, which can resolve up to aF level. In case of Zn measurements, by fixing the flow-rate at a constant level and using continuous flow with flow rates as low as µL/hour, we would eliminate the compliance induced in the PDMS chamber. 5.5 Chapter Summary Highlights of this chapter that included work toward development of low-cost, simple and compact printed capacitive chemical sensors via the concept of dielectric loading is provided below. Since chemical sensing via printed capacitors have not been studied previously in detail, the data presented in this chapter could be very valuable to pursue a number of novel applications. Some of the novel aspects of our work and unique features of the sensor concepts developed include:  A novel divalent Zn2+ sensing platform based on sodium silicate dielectric is proposed and illustrated, which works on the principle of Zn replacing weakly coordinated surface-bound Na+ surface states. Preliminary characterization show that the sensing effect can be integrated into microfluidic cells and have sensitivity of 20 pF/M, which can be improved significantly via better IDC design or using Sodium Silicate polymer composites.  Ion exchange resins (IER) were identified as an additional dielectric media suitable for chemical-capacitive sensing. In addition to proof of concept, a unique prototype device concept, featuring a three-chamber microfluidic design with integrated IDCs was proposed. The proposed design can detect the presence of divalent metal impurities and heavy metals in a straightforward manner. Based on the selection of IER type, it also has the potential of providing quantitative measurement of (heavy) metal content via time required to saturate a given amount of IER capturing capacity.  A unique software code, called CionXR, was developed for quantitative analysis of IER amount necessary to fill the filter chamber in the middle and concentration of (heavy metal) ions detected Although, not complete by any means, this chapter lays the groundwork toward development of a number of novel sensor devices. More specifically, just to give a few examples, the proposed sensors may be used to develop the following applications. 169

 Since the IER adsorb radioactive wastes, the setup developed can be used for a safe and low-cost way for detection of radioactive wastes via capacitors.  By integrating a flexible MCU on the LoC, with wireless datalink, it should be possible to develop a compact and low-cost clean water monitoring system. In the wake of recent developments in the USA that witnessed water quality concerns in industrial towns, an affordable monitor could be extremely useful and marketable.

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Chapter 6: Conclusions and Future Work 6.1 Conclusions The primary focus of this research has been the exploration of novel capacitive sensors for physical and chemical measurements that can be enabled by interdigitated capacitors (IDC) built via low-cost printing processes on flexible substrates such as plastic, paper, glass. Such devices can be incorporated into future lab-on-chip (LoC) and wearable electronic systems for health and environmental monitoring applications. It appears that these applications will immensely benefit from advances in computational techniques such as machine learning and data fusion, which places the importance on high number of ultra-compact sensors as opposed to one expensive and large sensor that could be super accurate. The premise of this work is that low-cost printed capacitors are simple but effective elements that can fulfill this need, provided they can offer novel and quantitative sensing capabilities, which is explored here. Throughout this dissertation, we provided novel processing techniques for microfluidics integration of printed sensors in general and IDCs in particular. Using this know-how and toolset, sensor enriched microfluidic devices and novel sensing capabilities were developed for the upcoming era of fast adaption of LoC systems and wearable biomedical monitors. Types of novel devices proposed and illustrated include both physical and chemical sensors. We primarily used the dielectric loading technique, i.e. enhanced surface or volume interactions of the dielectrics atop IDCs, to explore the novel capacitive sensors by detecting the dielectric change due to factors such as temperature, humidity, ion species of interest, proximity of other bodies solution types and flow dynamics. More specifically the unique outcomes of this research work include novel processing techniques and new sensor devices for physical and chemical activities on interest as well as experimental setups and software elements that facilitate low-cost LoC research, as detailed in the following list: 1. Novel Processing and Experimental Techniques: a) AFM-based Surface Activation: A novel method to characterize surface energy properties of plasma-activated substrates was developed using AFM force- 171

spectroscopy [269], which has been used in microfluidic fabrication, flow control, nano-ink printing and development of flow-rate sensor presented in this work. b) Low-Cost Microfluidics: A number of low-cost solutions in flow automation and application specific PDMS and paper microfluidic designs via 3D and wax- printing were developed. An example of such implementation was used for optimized detection of insulin in a confined volumes resulting in more efficient detection of insulin in pancreatic islets [270]. 2. Novel IDC Physical Sensors: Several novel and previously unexplored sensors for tracking physical variables were developed on printed IDCs, which includes: a) Flow-rate sensor: By creating electronegative substrates using plasma activation process and tracking the change in potential via dielectric substrate material, a novel capacitive flow rate sensor has been developed. It was argued that EDL formation and pressure-driven electroosmotic flow is responsible for charge redistribution being sensed capacitively. This redistribution happens within the Debye length of the ionic solution and can be also used to track pH changes. b) Thermometer: Capacitive dielectric loading combined with temperature sensitive

high-k dielectric materials such as BaTiO3 were studied to accurately capture and translate the capacitive measurements into temperature data (Appendix J – Reference 1). This potentially turns all IDCs, already in the system for other reasons (proximity, chemical or flow sensing), to capacitive thermometers, reducing the complexity of using external thermocouples and cost of LoC devices. Although their accuracy of ±2ºC, was not significantly better than commercial thermocouples, they do offer scalability and relevant range for bio-medical applications (20-50˚C), which is especially noteworthy. c) Humidity Sensor: Capacitive sensing is also sensitive to humidity levels, which can be exploited to detect change in water vapor content or leak in micro-fluidic systems on both glass, plastic or paper substrates. It was found that nanogap (~10nm) capacitors obtained from collaborators and built via equally low-cost technique [240] may provide an especially stable and repeatable results, as compared to paper devices, which may become a very effective alternative for humidity sensing. 172

d) Motion/Proximity sensor: The concept of dielectric change with proximity was utilized to build integrated IDC devices in microfluidic channels that can detect the change in capacitance with fluid medium change as well as motion of particles in close proximity. Together with the flow-rate sensors developed, they may provide very low-cost and high-speed sensors for tracking cell development or counting via low-cost paper and/or plastic-based LoC solutions. 3. Novel IDC Chemical Sensors: Using the dielectric loading approach, several additional sensors that can utilized for chemical identification of specific ions were also proposed, which includes

a) Divalent Zinc sensor: Identified Sodium Silicate (Na2SiO3), which has high level of Zn selectivity and commonly found in magadiite mineral, as a unique IDC dielectric that can be used to capture and quantify Zn in standing and flowing solutions with a high sensitivity of (~10 pF/M), which can be further optimized depending on IDC design and sodium silicate processing conditions. b) Heavy Metal Detection: Ion exchange resins (IER) were shown to be an effective medium as ion absorbing dielectric for IDC’s, especially if they can be encapsulated within other polymer networks. IERs can also be incorporated as an effective filtering element in microfluidic devices with a novel three-chamber design, and together with integrated IDCs, these novel devices can detect presence of heavy metal cations and high-risk industrial contaminants, given their well-established use in metal remediation. 4. Infrastructure & Software Development: Several unique device designs and software elements were developed in an effort to capture response of the novel sensor elements in a streamlined fashion and improve or correct its response. This includes a) Quad-Capacitance sensors: A novel cross-arrangement of flexible IDCs on paper were proposed, based on wax printed microfluidic channels on one side and a novel quad-sensor with 4 capacitors on the other side. Such devices could offer the capability to correct for bending related factors, estimate strain and lead to more accurate calibration of measured capacitance changes on flexible substrates. b) Software development: To speed up sensor design and data analysis, the following code developments took place: 173

i. Analytical IDE predictor: A GUI to determine the expected value of printed IDC was developed to streamline the design of electrodes before printing them on a given substrate. The critical dimensions required for an application specific capacitance can be easily predicted using this tool with <2% error. ii. Datacleanser: Used for post processing of raw capacitance data to eliminate spikes and noises caused by external mechanical disturbances and electro- magnetic interference and improve sensing integrity via moving average and outlier analysis. iii. CionXR: A novel GUI for windows & android platform named ’CionXR’ was created to determine the exact amount of IER necessary to complete a specific concentration of cation adsorption. By combining with the data from three-chamber design, the exact concentration or timing of (heavy) metal ion capture process can be calculated for a variety of ions and IERs in the literature. On a broader context, work carried out in this dissertation provides context and specific examples for the major advantages of the using capacitive sensing technique and its integration to microfluidic systems. In particular, sensors implemented in this work offers advantages in terms of  Compatibility: The capacitive sensing technique is CMOS compatible and can be easily interfaced with the existing digital microcontrollers with high precision capacitive sensing down to ~1aF or lower [271]–[274]. There are very few electrical measurements that can come with such compatibility to digital electronics and high accuracy. All measurements were taken in using low-cost high-accuracy (~$2) CMOS digital capacitive readers  Cost: Printed IDCs offer considerable reduction in manufacturing costs due to lesser complexity and amount of material (precious metals) used for electrodes. They can also be integrated onto CMOS chips if necessary without additional cost. Common Silver nanoink was used to create all IDCs in this work and it is anticipated that cost of writing would not exceed $1 and can come down to few cents in roll-to-roll processing common to printed devices [275]. 174

 Variety of substrates: IDCs can be printed, as many times shown in this dissertation, on variety of flexible surfaces such as paper, plastic, glass, even wood in which fluidic channels may be implemented. This would be especially valuable in applications where one-time use sensors are necessary to minimize contamination or field work where tools or budget is limited.  Multiplexing Capability: Where necessary and possible, the same capacitive sensor can be used to detect several parameters as well as specific analytes. For instance, a change in both humidity and temperature can be detected in the capacitive-thermometer and a change in flow-rate can be detected using the motion sensor. This not only provides opportunities to correct for measurement errors it can also lead to savings in area in LoC system design and development.  Scalability: Based on the first experimental results using an ultra-fine nanogap (~10nm) capacitive sensor that can be implemented on flexible polymer and glass surfaces, the required area for the IDCs can be remarkably reduced. This would in turn reduce the area and cost of LoC chip significantly. while also increasing the sensitivity and stability of capacitive sensing at unprecedented levels.  Variety of applications: As explored in this dissertation, just by changing the dielectric loading layer, one capacitive sensor can be used to detect a wide range applications, analytes and physical/chemical parameters. This paves the way for a wide range of LoC applications and wearable devices, and can lower the cost further, since it is possible to avoid multiple microelectron-mechanical (MEMS) sensors with expensive packaging or many different polymers and inks necessary to build different printed sensors in different processes that can be expensive. 6.2 Future Work Besides being uniquely implemented via printed IDCs, each sensor developed in this work offer many novel features on their own and stands out from the existing commercial sensors available for similar applications. However, in many cases, an extensive study was not performed on the sensor design space and performance, although a comprehensive understanding of their operation, limitation and challenges are provided in all cases. Driven by motivation to explore novel types of IDC sensors as opposed to highly accurate alternatives to existing devices, such lack of design depth can be 175 understood. Once specific application needs, and contexts are determined, this type of design tasks and sensor optimization work can be tackled in future studies and ensure that novel sensing phenomena and device ideas presented here carry forward as more complete solutions and mature products. The comprehensive exploration of capacitive sensing in this work has opened the doors to explore and develop numerous electrical sensor applications in physical and chemical realm. The current research work has a potential to expand in extended directions in the near future.  Nanogap capacitors have proven to be a promising direction to explore for all sensing applications. With a stable and repeatable output, nanogap devices can provide better accuracy in measurement and sensitivity. There needs to be a change in design to accommodate sufficient area to make proper contacts to the device.  Better shielding is one of the factors which would greatly help in reducing external EMI noises. By designing effective shield structures for each sensor device or application, there is room to improve accuracy and reproducibility down to aF level.  Capacitive pH sensing is an important path to follow with its potential in fields of chemistry and biological sciences. Via electrokinetic effects explored here, the concentration of H+ ions can be easily predicted, at least with biologically relevant range, in an induced flow which would be a huge benefit for various application. In addition, since a bare capacitor can be utilized for pH sensing without necessity for any functional layer, it would prove to be a very simple and compact pH sensor.  Wireless data transmission would help eliminate operator or wiring related noise and external EMI by isolating the sensor from external disturbances. As a matter of fact, we have already accomplished Bluetooth connectivity for capacitive sensing in this research (not reported) and is in preliminary stages of development. PSoC4 [276] with its “Capsense” capability and built-in BLE interface is a promising technology to be pursued for future of wireless sensing applications. 176

 With increasing commercialization opportunities and demand for compact LoC systems and ever-cheaper wearables applications, capacitive sensing implemented with a thinned-down flexible CMOS controllers [277] would further reduce the size and cost requirements since digital capacitive controller and BLE units now can be incorporated within the flow devices.  In case of heavy metal ion detection, further exploration of capacitive sensing mechanisms for detection of these nasty impurities in drinking water systems is a clear path to be pursued. The capacitive sensor printed on flexible substrates would bring the cost down to very affordable levels, not only for the main unit, but also for any replaceable parts. Thus, a next-generation clean water detection system with wireless CMOS interface could be implemented at a reasonable cost and user-friendly commercial device for masses.

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Appendix A Fabrication of Master mold – Process  A 3-inch Si wafer is cleaned using Acetone, IPA and DI water followed by a N2 blow dry.  SU-8 negative photoresist is spun on the wafer using the spin-coater. Su-8 negative tone resist are divided into Su-82010, Su-82025 and Su-82050 with increasing viscosities. The last two digits of the resist name indicate the thickness of photoresist that can be coated (i.e. Su-82010 can form a layer up to 10µm thick.  The resist spin speed is decided and spun according to the desired layer thickness. Fig 3.28 shows a spin speed-thickness curve of Su-8 resists obtained from the manufacturer [278].

Figure 3. 28 Spin-speed vs Thickness curves for Su-8 Negative tone photoresist

 This is followed by a prebake of resist in a conventional oven at 90ºC for 30 minutes. 204

 Next, the mask is loaded in the Karl-Suss MJB-3 contact aligner and the exposure is done in contact mode for 30 seconds.  The sample is then removed and postbaked at 120ºC for 2 minutes on a hotplate.  The sample is then rinsed using Su-8 developer to remove the unexposed resist layers to create the structure. The whole process flow can be detailed as shown in Flowchart (Fig 3.29).

Figure 3. 29 Process Flow of master mold creation process using soft-lithography.

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Appendix B Microfluidic Device – Process Development  Once the master mold is created, both Su-8 mold or 3D printed mold follow the same set of steps to achieve a working microfluidic device.  PDMS is mixed with the curing agent in the ratio 10:1 and mixed vigorously until they are completely blended.  Degassing is done to remove the mixture of gas bubbles for 1 hour in a desiccator.  The PDMS mixture is poured/casted on the master mold.  The mold is cured at room temperature for 24 hrs or on a hotplate at 90ºC for 4 hours. This step also removes any tiny bubbles remaining after degassing process.  Once the PDMS is hard, it is cut around the edge of master mold and is peeled off the mold surface.  Inlet/outlet holes are punched on the side which contains the channel. This is to avoid the channel side to be free of contaminations.  A scotch tape is used to remove any additional debris on the surface of the PDMS.  The substrate (glass slide) and the PDMS channel formed are placed in the RIE with bonding faces up.

 O2 plasma treatment is done on the surface to increase the hydrophilicity.  Both the surfaces are placed in contact and put on a hotplate at 90ºC for 30 minutes to create a sturdy bonding without defects.

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Appendix C Syringe Pump Automation  First step involved detecting the syringes on the pump control GUI.  Once the syringe is detected, each pump can be individually programmed.  The programming is done by creating a pump program list (PPL) using the inbuilt software.  First a loop is started by using the “loop start” command.  Next, the pump number, syringe diameter and target volume for dispensing is set.  “Rate for time” command allows to set the infusion rate which is usually set to mL/min or µL/min. For slower rates, mL/hour or µL/hour is used.  A pulse can be created by using the “pause” command where the pump goes into halt for a specified number of seconds.  A series of application specific flow rates can be set. Each command is assigned to a particular phase number.  A loop can be created by using the command “Jump to Phase” and entering the start of phase number to be looped.  Once the program is created, the software autosaves the program, where the file extension has to be renamed to “.PPL” for the GUI to load the file.  Once the programmed file is loaded into each pump, “Run all” command on the GUI starts all the programmed pumps simultaneously.  To set the start of pumps at different times, the “pause” command should be used in the beginning of the program to create a start delay.

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Appendix D Algorithm used to develop mathematical modelling of IDC. %inerdigitated cap modelling %% physical model of idc W = 100e-6; %width of the electrode G = 100e-6; %Gap between the internal electrodes lamda = 2*(W+G); %Spatial wavelength %Eqn 1 - metallization ratio (neta) neta = W/(W+G); %also equals to (2*W/lamda) netaalt = 2*W/lamda; %alternative cac=lculation for neta %% single semi-infinite capacitance layer C1 = %half capacitance of one interior electrode CE = %capacitance of the exterior electrode wrt ground N = 6; %number of electrodes %Eqn 2 - Total Capacitance C = ((N-3)*(C1/2)) + (2*((C1*CE)/(C1+CE))); %% N-later capacitance Chinf = 1; %cap of air Ch1 = %geometrical cap of layer 1 depending on height 'h1' Ch2 = %geometrical cap of layer 1 depending on height 'h2' epsilon1 = %relative diectric constant of layer1 epsilon2 = %relative diectric constant of layer2 %Eqn 3 - Capacitance of sensor with different dielectric layers Cu = Chinf + (epsilon-1)*Ch1 + (epsilon2-epsilon1)*ch2 %% Computation of interior half capacitance C1 r = h/lamda; %Eqn 5 k' = sqrt(1-k^2); %complementary modulus K(k) %complete elliptocal integral of first kind with modulus k K(k') %complete elliptocal integral of first kind with modulus k' 4*r = K(k')./K(k) %Eqn 4 = Rectangle of x plane in fig 4 mapped in z plane in tems if elliptical integral of 1st kind v2 and v3 are Jacobi Theta functions 0 REF 27 k = =v2((0,q)/v3(0,q)).^2 %Eqn 6 where k = modulus in Eqn 4 q = exp(-4*pi*r); %Eqn 7 %% Calculation of complete elliptic integral of first kind %step 0 - find fourr k = 0:0.01:1;% modulus kp = sqrt(1-k.^2); K = ellipke(k); % complete elliptic integral of first kind Kp = ellipke(k'); fourr = K./Kp; clc; clear all; close all; %step 1 - find r using alternative method W = 100e-6; %width of the electrode G = 100e-6; %Gap between the internal electrodes 208 lamda = 2*(W+G); %Spatial wavelength h = 200e-6; L = 1000e-6 r = h./lamda %step 2 - find q q = exp(-pi*fourr) qalt = exp(-pi.*4.*r) %step 3 - find knew using jacobi theta functions syms n v1 = 0 v2 = symsum((qalt.^((n+1)/2).^2),n,0,30) % ref wiki v3 = symsum(qalt.^(n.^2),n,0,10) v4 = symsum((-1)^n.*qalt.^(n.^2),n,0,10) knew = (v2./v3).^2 % step 4 - find t4 t4 = vpa(1./knew); %step 5 - find t2 using jacobis elliptical function K = vpa(ellipke(knew)); % complete elliptic integral of first kind neta = W/(W+G); %also equals to (2*W/lamda) netaalt = 2*W/lamda; %alternative cac=lculation for neta M = (knew) U = (K.*netaalt) M1 = 0.0000034873666793649857853712806664782 U1 = 0.78539884814163780107774555863176 [sn,cn,dn] = ellipj(U1,M1) t2 = [sn] %step 6 - calculate k1 k1 = t2.*(sqrt((t4.^2-1)./(t4.^2-t2.^2))) %step 7 - calculate k1p k1p = sqrt(1-(k1).^2) %step 8 - calculate c epsnot = 8.854e-12 epsr = 1 Kk1 = vpa(ellipke(k1)) Kk1p = vpa(ellipke(k1p)) C1 = epsnot*epsr*L*Kk1./Kk1p fprintf('Capacitance : %4i fF %4f',C1*1e15);

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Appendix E Data Cleanser Algorithm Approach 1: The mathematician’s approach In each experiment, the recorded Capacitance values had outliers, which were identified and removed as follows. Let X denote a set of Capacitance values, X={x_1,x_2,…,x_n } By trial and error, a window size, w, was chosen. Let X_i be a subset of w values defined as: X_i={x_i,x_(i+1),…,x_(i+w -1) } Let x ̂_i ̇ be the average of values in X_i and σ_i be its standard deviation. A value x_i in X is retained if: x ̂_i-(nσ)≤x_i≤x ̂_i+(nσ) Initially, n and w are set to 1 and 50 respectively. To aid selection of an optimal value for n and w, the resulting values were continuously plotted on a graph and adjusted until a smooth curve was seen. Approach 2: The writer’s approach In each experiment, the recorded Capacitance values had outliers. In a set of Capacitance values, a given Capacitance value is considered to be an outlier, if the difference between the value and the average of values in its neighborhood is significantly higher than the standard deviation of the values in neighborhood. The number of values that comprises the neighborhood was initially set to 50 and then adjusted appropriately. Similarly, the tolerance for a given value was initially set to 1 standard deviation and then adjusted appropriately. To aid selection of an optimal value for the window size and tolerance values, the resulting Capacitance values were continuously plotted on a graph and adjusted until a smooth curve was seen. 210

Appendix F A7.1 Flow patterns A7.1.1 Couette flow To understand the design and working of this flow-rate sensor, basic concepts of fluid mechanics must be understood. Coutte flow is one of the principal flow method inside a microchannel. This flow is considered as a surface driven flow and has no pressure gradient in the system which causes the flow. It is a completely potential driven flow induced by the surface charges [212]. Consider two parallel plates of infinite length (x-direction) and width (z- direction), separated by a finite distance in the y-axis. The flow between this plate is a steady flow in x-direction as shown in Fig A7.1. Also, symmetry in z-direction is assumed.

Figure A7. 1 Parallel plates of infinite length and width separated by a finite distance.

The Navier-Stokes equation is given by eqn. A7.1. 휕푈⃗ 휌 + 휌푈⃗ ⋅ 훻푈⃗ = −훻푃 + 휂훻푈⃗ (A7.1) 휕푡 where, 휌 is the charge density. 푈⃗ is the velocity vector. 푃 is the pressure. 휂 is the viscosity of the fluid. 211

⃗ Since the flow is steady, 휌 =0 and since there is no pressure gradient, 훻푃 =0, eqn. A7.1 becomes, 휌푈⃗ ⋅ 훻푈⃗ = 휂훻푈⃗ (A7.2) where, 휌푈⃗ ⋅훻푈⃗ is the convective flux of the momentum and 휂훻푈⃗ is the net volumetric viscous force. Since it is assumed that the flow is unidirectional, the velocity vector (푈⃗) and the gradient of velocity vector (훻푈⃗) are orthogonal to each other which makes the dot product in eqn. A7.2 to be zero. With these assumptions, we can denote eqn. A7.1 as, 휂훻푈⃗ = 0 (A7.3) Eqn. A7.3 is the governing equation for the Couette flow. Solving this vector equation with the assumptions that the velocity (푈) has only the x- direction component, it is considered that the x-component of the velocity and y- component of its Laplacian. Eqn. A7.3 can be denoted in the scalar form as 휕푈 휂 = 0 (A7.4) 휕푦 Integrating eqn. A7.4 w.r.t y,

푈 = 퐶푦 + 퐶 (A7.5) From eqn. A7.5, it can be observed that 푈 is the velocity given by a linear distribution. Since the Couette flow is a surface driven flow, the boundary conditions (B.C’s) needs to be solved. Fig. A7.2 shows the B.C’s existing inside the channel.

Figure A7. 2 Couette flow inside the microfluidic channel & its corresponding B.C’s 212

푈 is the velocity of the fluid at the bottom wall (y=-h) and 푈 is the velocity of fluid at the top wall (y = h). The axis y=0 at the center of the channel while the height of the channel being 2h. The velocity of the fluid with a Couette flow is given by eqn. A7.6. 푈 + 푈 푈 − 푈 푦 푈 = + (A7.6) 2 2 ℎ A7.1.2 Poiseuille flow The Poiseuille’s flow is a pressure driven flow [212]. To derive the velocity profile for this flow, the same infinite parallel plates as assumed in Coutte flow as shown in Fig. A7.3 is considered. The assumptions made for this flow are: 1) The flow is steady and unidirectional. 2) There is a pressure gradient in the x-direction. 3) “No Slip” condition exists, and the plates are motionless i.e. the velocity 푈=0 at the wall. Applying these assumptions to the Navier-Stokes equation (eqn. A7.1) results in eqn. A7.7. −훻푃 + 휂훻푈⃗ = 0 (A7.7) We can observe from eqn. A7.7 that in this system, there is a pressure gradient; and at steady state, there is a net viscous volumetric force (휂훻푈⃗) balancing the pressure gradient (−훻푃).

Figure A7. 3 Parallel plates of infinite length and width separated by a finite distance.

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Solving eqn. A7.7 for the pressure in x-direction, results in eqn. A7.8 푑푃 휕푈 − + 휂 = 0 푑푥 휕푦 푑푃 휕푈 = 휂 (A7.8) 푑푥 휕푦 Since the pressure gradient is uniform, is a constant. Integrating eqn. A7.8 twice, 1 푑푃 휕푈 푦 = + 퐶 휂 푑푥 휕푦 1 푑푃 푦 = 푈 + 퐶푦 + 퐶 (A7.9) 2휂 푑푥 Eqn. A7.9 is a second order polynomial equation which is a parabola. Applying the B.C’s for the system, Velocity 푈=0 @ y=h (top wall) and 푈=0 @ y=-h (bottom wall) Velocity distribution for a Poiseuille’s flow is given by eqn. A7.10. 1 푑푃 푈 = (ℎ − 푦) (A7.10) 2휂 푑푥 This flow can be graphed as shown in Fig. A7.4.

Figure A7. 4 Poiseuille’s flow inside the microfluidic channel

The maximum velocity, 푈 happens at y=0 and the parabolic decay of velocity can be observed as we move from center towards to the walls where y = ±h and 푈 approaches zero. 214

To detail the forces acting on a finite controlled volume of fluid inside the channel, consider the cubical volume shown in Fig. A7.4. The magnified version of this control volume is depicted in Fig. A7.5 along with the forces acting on this volume.

Figure A7. 5 Controlled volume of fluid inside the microfluidic channel with Poiseuille’s flow.

In any such controlled volume of fluid, with the pressure gradient (훻푃) in the x- direction, there is a bigger pressure sensed by the volume in the positive x-direction and a smaller pressure in the negative x direction, as indicated by the red arrows in Fig. A7.5. In addition, there is a net viscous force acting against the pressure force of the fluid in the negative x-direction. From eqn. A7.10 it can be observed that as the height of the channel (separation between the two walls) becomes larger, the velocity of flow becomes higher. A7.1.3 Summary From the analytical derivation for the Couette and the Poiseuille’s flow, the following important conclusions can be made. 1) The velocity of a fluid in Couette flow is independent of the height of the channel. 2) The velocity of a fluid in Poiseuille’s flow is proportional to square of the channel height (h2). 3) When moving towards smaller microscale systems, the surface driven force becomes predominant. 4) Electrokinetic flows which is discussed in section A7.1 at microscale are always surface driven flows. 215

5) In microscale systems, electrokinetic and capillary forces (surface forces) become dominant. A7.2 Electrostatics in Microfluidics In a microfluidic channel where there is an electrolyte containing ions moving inside the channel, the velocity or motion is way slower compared to the electronic counterpart of an electron moving in a conducting wire. There is an electric field associated with this moving ion which is considerably greater than the magnetic field induced by these ions zipped around in water. Thus, from the electrostatic standpoint, the magnetic fields in microfluidics can be ignored for the analytical calculations. A7.2.1 Electrostatics of point charge It is known that source charges create electric displacements given by eqn. A7.11. 푞 푟̂ 퐷⃗ = (A7.11) 4휋 훥푟 퐷⃗ is the electric displacement vector. 푞 is the electric charge. 푟̂ is the unit vector in radial direction. 훥푟 is the distance in the radial direction. The distance 훥푟 can be the distance between two points anywhere in the system, not necessarily the origin of the system. Eqn. A7.11 states that if a charge is placed in a location, we can observe effects of this charge in the surrounding in terms of electrical displacement (퐷⃗) which is a function of 푞 and is a vector aligned radially away from 푟̂. The magnitude of 퐷⃗ is inversely proportional to the distance of displacement 훥푟. If an electric field is introduced into the system, it induces a force on these test charges which is given by eqn. A7.12. 퐹⃗ = 푞퐸⃗ (A7.12) The electric displacement is partially described by the electric field but also involves the polarization of the medium. For instance, if the charge is in free space, there is an electric field, but, there is no medium to be polarized. However, when the charge is in water, it creates an electric field and induces polarization of the medium. Consider the structure of water molecule shown if Fig. A7.6. 216

Figure A7. 6 Structure of water molecule.

The water molecule has an inherent dipole which is randomly ordered due to the hydrogen bonding. Statistically, the orientation in space is random. But, if a charge is placed around this water molecule, it is going to create an electric field and induce a polarization of the water molecule. For instance, if a negative charge is put in water, it rotates the H+ bonds and be much closer to the negative charge nearby. The physics behind the polarization and electric displacement can be described by eqn. A7.13. 퐷⃗ = 퐸⃗ + 푃⃗ (A7.13) where 푃⃗ is the vector polarization. Eqn. A7.13 states that the displacement has two components: 1) Induced electric field (퐸⃗). 2) Relative polarization of the medium (푃⃗). Eqn. A7.13 can be further expressed in a form describing the relation between displacement and electric susceptibility (휒e) given by Eqn. A7.14

퐷⃗ = 휀퐸(1 + 휒) (A7.14) This relation allows to clearly delineate the difference between the part of free space which cannot be polarized and is responding to the system (휒 = 0) which results in the electrical displacement 퐷⃗ = 휀퐸 and the polarizable medium responding to the system described by susceptibility (휒 = 푓푖푛푖푡푒) for water. Electric displacement which describes what an effect of a charge is in eqn. A7.11 is linearly related to the electric field which in turn describes how the environment acts back at other charges in the system. If we consider permittivity (ε) is a constant, it means that the medium responds instantaneously fast and is linear. 217

To explain the definition of permittivity, consider the permittivity of deionized water (DI) given by eqn. A7.15

휀 ≃ 80휀 @ room temperature (A7.15) where 휀 = 8.854푒 C/Vm Eqn. A7.15 implies that if a charge is placed in water, it will respond and polarize. If the charge is doubled, then it doubles the polarization as described by the linear relation 퐷⃗ = 휀퐸. This response is instantaneous and is independent of frequency i.e. the characteristic time in which the water molecule rotates must be fast as compared to the perturbation applied to the system. The typical time for a water molecule to rotate is close to 1ps [279] . This means that until the system is exposed to a frequency of f = 1/1 ps = 1012 Hz, the frequency independent assumption is valid. Also, until the electric field reaches 1010 V/m, the linear dependency assumption is valid. The electric field term describes what the system does to the charge and the electric displacement describes what impact does the charge have on the environment.

When a charge is placed in water, the fact that 휀 ≃ 80휀 means that a polarization is created primarily. The factor of 80 means that 79 out of 80th of what happens when a charge is placed in water is that it makes the water molecules flip (polarize) i.e. they cancel out 79/80’s of what the electric field would have been in free space. Thus, the electric field is the residual left over after the water polarization occurs. A7.2.2 Electrostatics of bulk medium Section A7.2.1 described the effects of a point charge on the electric field, electric displacement and polarization. To analyze the same concept in a real system, the differential description is crucial. From the expressions of divergence that was derived for a point charge, there are two key equations that can be developed. 1) Gauss’ Law for electricity described by eqn. A7.16.

∇. 퐷⃗ = 휌 (A7.16) which states that the divergence of electric displacement is proportional to the net charge density (휌). If there are an equal number of positive and negative ions in an infinitesimal volume of liquid, then, 휌 =0. Likewise, with increased positive charges, 휌 >0 and with increased negative charges, 휌 <0. 218

2) Volumetric force on a charged fluid given by eqn. A7.17 ⃗ (A7.17) Volumetric force, 푓 = 휌퐸⃗ Eqn. A7.17 can be related to 퐹⃗ = 휀퐸⃗ represented in volumetric form while eqn. A7.16 is the vector description derived from the spatial form of electric displacement described in eqn. A7.11. The volumetric force is a body force and not a surface driven force and can be related to the RHS of Navier-Stokes equation (eqn. A7.1). Eqn. A7.16 can be used to relate the charge density with the electric potential. All the relations described in this section defines the bulk fluid properties. To include the effect of flow in the surface phenomenon where the net charge density is, the B.C’s which specifies what the voltage is on a given wall(boundary) needs to be solved.

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Appendix G SAMPLE MOLARITY CALCULATION – 1% w/v Zinc acetate dihydrate Molar mass = 219.5 g/moles Used 0.85 grams of Znacetate Moles of solute = 0.85/219.5 = 0.003872 moles DI used 85grams Density of DI = 0.9970 DI used = 85/0.9970 = 85.2 mL = 0.085L Molarity = Moles of solute/Liter of solvent = 0.003875/0.085 = 0.0455 M = 45 millimolar

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Appendix H  STEP 1: Total Volume of solution. o The total volume of the metal ion containing aqueous solution is determined (Assume = 30mL as in case of the syringe used in the setup).  STEP 2: Molecular Weight (MW) prediction. o The average MW of the metal ions is calculated. . Case 1: In case of aqueous solution with a single ion, it is the MW of the ion. . Case 2: In case of multiple ions (Assume a case of three different ions), ( ) 퐴푣푒푟푎푔푒 푀푊 = (Consider Cu2+ ad Ni2+ ions, Average MW = (63.546+58.6934)/2 = 61.119 g/mol)  STEP 3: Calculating weight of salt in solvent used. o Weight of metal ions in the solution needs to be calculated in parts per million (ppm). . 1ppm = 1 g of Metal in 106 g of solution. = 1µg metal in 1g of solution. Since density of water is 1g/mL (1mg/µL), 1ppm = 1µg of metal in 1mL of solution. . We used 1% w/V concentration where 1g of salt is dissolved in 100mL DI water.  104ppm of Cu, i.e. 104µg/mL = 104mg/L and 104ppm of Ni.  2*104mg/L*30mL = 600mg of metal ions in the given volume of solution (30mL).  STEP 4: Convert weight of salt to equivalence. o To find equivalence, we need to determine the equivalent weight of salt first . Equivalent weight = Average MW/Average Valency 2+ 2+ . Valency: In case of Cu in CuSO4 and Ni in C4H6NiO4 is 2. . In the case considered Average Valency = 2 eq/mol. 221

. / . Equivalent Weight = = 30.5595 푔/푒푞. / o Equivalence = Weight of salt / Equivalent Weight. . In the case considered = 19.6 meq. . /  STEP 5: Calculating the volume of IER required o The capacity by wetted bed volume for DG26 IER is 2 meq/mL. o Volume of IER needed = Equivalence/Capacity of IER . = / = 9.8 mL. . STEP 6: Calculating the weight of IER required o The density of DG26 IER is 1.22 g/mL Therefore, the weight of IER needed to detect the given amount of Ni2+ and Cu2+ ions in the solution = Volume of IER needed * Density of IER => 11.956 g.

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Appendix I MATLAB Algorithm used for development of CionRX %Calculating IER weight clc; clear all; close all; cprintf('-err', 'STEP 1: DETERMINING TOTAL VOLUME OF SOLUTION USED AND MOLECULAR WEIGHTS\n\n'); Solution_amount = input('Enter the volume of solution used for testing (mL) : '); cprintf('\n'); Numbmber_of_salts_used = input('Enter the number of salts in the solution : '); MW1 = []; Saltname = []; Salt = []; for n = 1:Numbmber_of_salts_used cprintf('\n'); Salt{n} = input('Enter the Symbol of the Salts used (Eg: Ni for Nickel)','s'); if strcmp(Salt{n},'Ni') MW1{n} = 58.6934; Saltname{n} = 'Nickel'; elseif strcmp(Salt{n},'Cu') MW1{n} = 63.546; Saltname{n} = 'Copper'; elseif strcmp(Salt{n},'Na') MW1{n} = 22.9897; Saltname{n} = 'Sodium'; elseif strcmp(Salt{n},'Mg') MW1{n} = 24.305; Saltname{n} = 'Magnesium'; elseif strcmp(Salt{n},'Al') MW1{n} = 26.9815; Saltname{n} = 'Aluminium'; elseif strcmp(Salt{n},'K') MW1{n} = 39.0938; Saltname{n} = 'Potassium'; elseif strcmp(Salt{n},'Ca') MW1{n} = 40.078; Saltname{n} = 'Calcium'; elseif strcmp(Salt{n},'Mn') MW1{n} = 54.938; Saltname{n} = 'Manganese'; elseif strcmp(Salt{n},'Fe') MW1{n} = 55.845; Saltname{n} = 'Iron'; elseif strcmp(Salt{n},'Co') MW1{n} = 58.9332; Saltname{n} = 'Cobalt'; elseif strcmp(Salt{n},'Zn') MW1{n} = 65.39; Saltname{n} = 'Zinc'; elseif strcmp(Salt{n},'Cd') 223

MW1{n} = 112.411; Saltname{n} = 'Cadmium'; elseif strcmp(Salt{n},'Pb') MW1{n} = 207.2; Saltname{n} = 'Lead'; elseif strcmp(Salt{n},'') MW1{n} = 'UNKNOWN - Please Try Again'; Saltname{n} = ''; end cprintf('k', '\n The molecular weight of %s is %f g/mol',Saltname{n},MW1{n}) cprintf('\n'); end cprintf('\n'); MW1; pause(3) %% %Step 2 Calculate average MW cprintf('-err','STEP 2: AVERAGE MOLECULAR WEIGHT PREDICTION \n') cprintf('\n'); MW1A = cell2mat(MW1); AMW = mean(MW1A); cprintf('k','The average molecular weight of salts is %f g/mol \n',AMW) pause(3) %% %Step 3 - Calculating weight of salt in solvent used cprintf('\n'); cprintf('-err','STEP 3: CALCULATING WEIGHT OF SALT IN SOLVENT USED \n') cprintf('\n'); Solventvolume = input('Enter the volume of solvent used for preparation of solution (L) : '); cprintf('\n'); Saltweight = input('Enter the weight of salt added to the solvent (mg) : '); cprintf('\n'); ppm = Saltweight/Solventvolume; Appm = ppm*Numbmber_of_salts_used; cprintf('k','The average ppm of salts is %f ppm \n',Appm) cprintf('\n'); pause(2) ionsinppm = Appm*(Solution_amount/1000); cprintf('k','The weight of metal ions is %f mg \n',ionsinppm) cprintf('\n'); pause(3) %% %Step 4 - Convert Weight of salt to equivalence cprintf('-err','STEP 4: CONVERTING WEIGHT OF SALT TO EQUIVALENCE \n') cprintf('\n'); cprintf('[1,0,1]','HEAVY METAL IONS DISPLAY VARYING VALENCIES ACCORDING TO THE SOLUTION PREPARED \n') pause(3) for n = 1:Numbmber_of_salts_used cprintf('\n'); Valency{n} = input('Enter the Valency of the Salts used in order provided in STEP 1 (eq/mol): '); end 224

Valencymatrix = cell2mat(Valency); Avgval = mean(Valencymatrix); cprintf('\n'); cprintf('k','The average valency of metal ions is %f eq/mol \n',Avgval); cprintf('\n'); Eqweight = AMW/Avgval; pause(2) cprintf('k','The equivalent weight of metal ions in the solution is %f g/eq \n',Eqweight); Equivalence = ionsinppm/Eqweight; cprintf('\n'); pause(2) cprintf('k','The equivalence of metal ions in the solution is %f meq\n',Equivalence); cprintf('\n'); pause(3)

%% %Step 5 - Calculating the volume of IER required cprintf('-err','STEP 5: CALCULATING THE VOLUME OF IER REQUIRED \n') cprintf('\n'); pause(2) cprintf('[1,0,1]',' THIS CODE ASSUMES DOWEX G26 RESIN IS USED \n IF ANY OTHER RESIN IS USED, PLEASE ENTER THE CAPACITY OF WETTED BED VOLUME OF THE RESIN IN (meq/mL)\n'); pause(5) cprintf('\n'); CapacityIER = input('Enter the capacity of IER used (For DOWEX H26, leave blank)'); if isempty(CapacityIER) Capacity = 2; else Capacity = CapacityIER; end pause(1) cprintf('\n'); cprintf('k','The capacity of IER is %f meq/mL\n',Capacity); cprintf('\n'); pause(2) VolIER = Equivalence/Capacity; cprintf('k','The volume of IER required for complete ion exchange is %f mL\n',VolIER); cprintf('\n');

%% %Step 6 Calculating the weight of IER required cprintf('-err','STEP 6: CALCULATING THE WEIGHT OF IER REQUIRED \n') cprintf('\n'); pause(2) cprintf('[1,0,1]',' THIS CODE ASSUMES DOWEX G26 RESIN IS USED \n IF ANY OTHER RESIN IS USED, PLEASE ENTER THE DENSITY OF THE RESIN IN (g/mL)\n'); pause(5) cprintf('\n'); DensityIER = input('Enter the density of IER used (For DOWEX H26, leave blank)'); 225 if isempty(DensityIER) Density = 1.22; else Density = DensityIER; end pause(1) cprintf('\n'); cprintf('k','The density of IER is %f g/mL\n',Density); cprintf('\n'); pause(2) WeightIER = Density * VolIER; cprintf('-comment','The weight of IER required for complete ion exchange is %f g',WeightIER); cprintf('\n');

226

Appendix J The following publications were made during the course of this PhD work that include contributed projects that are not part of this dissertation.

[1] P. Rajan, J. Wright, A. Rohit, T. Cai, S. Kaya, "Study of Printed Interdigitated Capacitors as Thermometers for Microfluidic Devices" - SPIE's Journal of NanoPhotonics (Accepted) [2] R. J. Przybyla, J. Wright, R. Parthiban, S. Nazemidashtarjandi, S. Kaya, and A. M. Farnoud, “Electronic cigarette vapor alters the lateral structure but not tensiometric properties of calf lung surfactant,” Respir. Res., vol. 18, no. 1, p. 193, Dec. 2017. [3] D. J. Carbaugh, F. Rahman, J. T. Wright, P. Rajan, A. Rohit, and S. Kaya, “Photochemical Modification of Polymethyl Methacrylate (PMMA) for Producing Topographic and Refractive Index Contrast for Device Fabrication,” in Conference on Lasers and Electro-Optics, 2016, p. JTh2A.68. [4] D. J. Carbaugh, J. T. Wright, P. Rajan, S. Kaya, and F. Rahman, “Development- less deep ultraviolet positive tone photolithography with polymethyl methacrylate,” J. Vac. Sci. Technol. B, Nanotechnol. Microelectron. Mater. Process. Meas. Phenom., vol. 34, no. 4, p. 41609, Jul. 2016. [5] D. J. Carbaugh, J. T. Wright, P. Rajan, S. Kaya, and F. Rahman, “Dry photolithography through ultraviolet radiation-induced photo-etching of polymethyl methacrylate,” Thin Solid Films, vol. 615, pp. 423–426, Sep. 2016. [6] D. J. Carbaugh, J. T. Wright, R. Parthiban, and F. Rahman, “Photolithography with polymethyl methacrylate (PMMA),” Semicond. Sci. Technol., vol. 31, no. 2, p. 25010, Feb. 2016. [7] N. B. Whitticar, E. W. Strahler, P. Rajan, S. Kaya, and C. S. Nunemaker, “An Automated Perifusion System for Modifying Cell Culture Conditions over Time,” Biol. Proced. Online, vol. 18, no. 1, p. 19, Dec. 2016. [8] S. Kaya, P. Rajan, H. Dasari, D. C. Ingram, W. Jadwisienczak, and F. Rahman, “A Systematic Study of Plasma Activation of Silicon Surfaces for Self-Assembly,” ACS Appl. Mater. Interfaces, vol. 7, no. 45, pp. 25024–25031, Nov. 2015. 227

[9] P. Rajan, H. Dasari, W. Jadwisienczak, S. Kaya, “Plasma Activation of Si surfaces: An Easier and Safer Approach for Microsphere Lithography,” Bethesda, MD: 7th International Semiconductor Device Research Symposium – ISDRS, 2013. [10] P. Rajan, J. Wright, A. Rohit, T. Cai, S. Kaya, “Capacitive sensor enriched microfluidic devices for ionic sensing” (Under Preparation). [11] P. Rajan, J. Wright, A. Rohit, T. Cai, S. Kaya, “Capacitive-physical sensors for flow-rate and pH detection” (Under Preparation).

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