Applied Science, Faculty of
Mechanical Engineering, Department of
DSpace
UBCO
Mohaghegh Montazeri, Mahyar
Master of Applied Science - MASc
University of British Columbia
Nuisance compounds such as hydrogen sulfide (HâS) are the main cause of unpleasant odors and corrosion in sewer pipes. The available chemical treatment methods are expensive and have negative side effects. Hotspot monitoring, identifying specific locations for targeted treatment, minimizes the use of chemicals and their negative impacts. This thesis presents microfluidic-based artificial olfaction detectors for hotspot monitoring of nuisance sewer compounds. The detector is fabricated using a highly-selective microchannel coupled with a sensitive metal oxide semiconductor (MOS) sensor. To study the role of the detectorâs dimension and the behavior of different analytes in the detector, a comprehensive 3D numerical model is developed. In essence the model simulates the effects of diffusion, surface adsorption/desorption, chemical reactions on the MOS sensor, and heat and momentum transfer due to the MOS heating element. After verification/validation of the numerical model, it is used to compare the effect of the above-mentioned phenomena on the detector response. The results show that diffusion is the main parameter affecting the response, followed by surface adsorption/desorption; heat and momentum transfer has a minimum effect on the response. The model is also used to investigate the effect of the detectorâs dimension (including microchannel length, microchannel height, and MOS sensor housing volume) on its performance in terms of sensitivity, selectivity and the recovery time. The results show that there is a trade-off between selectivity and sensitivity; thus, a sum indicator is defined to investigate the overall performance for various conditions. To obtain the experimental responses for different concentrations of HâS, a setup (consisting of a custom-built syringe pump, automated sample delivery, vaporization chamber, and detector) is developed. The detector responses are calibrated using gas chromatography and analyzed by feature extraction methods. To study the effect of humidity on the response, a gaseous-based setup is developed. The results of these two setups reveal that humidity damps the responses, indicating the importance of calibrating the detector against liquid samples. The model and experimental setup developed in this thesis can be used for generating a prodigious number of responses in the most cost-effective manner for distinguishing HâS from mixture of several sewer gases.
i DEVELOPMENT OF MICROFLUIDIC-BASED OLFACTION SYSTEM FOR NUISANCE SEWER GAS MONITORING by Mahyar Mohaghegh Montazeri B.Sc., University of Tehran, 2015 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) September 2018 © Mahyar Mohaghegh Montazeri, 2018 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: DEVELOPMENT OF MICROFLUIDIC-BASED OLFACTION SYSTEM FOR NUISANCE SEWER GAS MONITORING submitted by Mahyar Mohaghegh Montazeri in partial fulfillment of the requirements of the degree of Master of Applied Science . Dr. Mina Hoorfar, School of Engineering Supervisor Dr. Homayoun Najjaran, School of Engineering Supervisory Committee Member Dr. Jonathan F. Holzman, School of Engineering Supervisory Committee Member Dr. Chen Feng, School of Engineering University Examiner iii Abstract Nuisance compounds such as hydrogen sulfide (H2S) are the main cause of unpleasant odors and corrosion in sewer pipes. The available chemical treatment methods are expensive and have negative side effects. Hotspot monitoring, identifying specific locations for targeted treatment, minimizes the use of chemicals and their negative impacts. This thesis presents microfluidic-based artificial olfaction detectors for hotspot monitoring of nuisance sewer compounds. The detector is fabricated using a highly-selective microchannel coupled with a sensitive metal oxide semiconductor (MOS) sensor. To study the role of the detectorâs dimension and the behavior of different analytes in the detector, a comprehensive 3D numerical model is developed. In essence the model simulates the effects of diffusion, surface adsorption/desorption, chemical reactions on the MOS sensor, and heat and momentum transfer due to the MOS heating element. After verification/validation of the numerical model, it is used to compare the effect of the above-mentioned phenomena on the detector response. The results show that diffusion is the main parameter affecting the response, followed by surface adsorption/desorption; heat and momentum transfer has a minimum effect on the response. The model is also used to investigate the effect of the detectorâs dimension (including microchannel length, microchannel height, and MOS sensor housing volume) on its performance in terms of sensitivity, selectivity and the recovery time. The results show that there is a trade-off between selectivity and sensitivity; thus, a sum indicator is defined to investigate the overall performance for various conditions. To obtain the experimental responses for different concentrations of H2S, a setup (consisting of a custom-built syringe pump, automated sample delivery, vaporization chamber, and detector) is developed. The detector responses are calibrated using gas chromatography and analyzed by feature extraction methods. To study the effect of humidity on the response, a gaseous-based setup is developed. The results iv of these two setups reveal that humidity damps the responses, indicating the importance of calibrating the detector against liquid samples. The model and experimental setup developed in this thesis can be used for generating a prodigious number of responses in the most cost-effective manner for distinguishing H2S from mixture of several sewer gases. v Lay Summary In this study, a device for hotspot monitoring of toxic and corrosive compounds in the sewer system is developed which intends to provide information to minimize the use of chemicals which are being used for treatment. This ultimately results to lower the costs and the environmental impact of the current treatment methods. The proposed device is employing microfluidic-based gas detectors for measuring the target compounds. Also, a multiphysics numerical simulation of the microfluidic-based gas detector is developed to decrease the number of experimental tests required to calibrate the detector with regards to different concentrations, temperatures, pressures, etc., therefore saving time and reducing costs significantly. The simulation also improves our understanding of the measurement process and significance of each related phenomenon, and also through the dimensional analysis, presents a tool for other researchers to choose the best geometry for different applications. vi Preface The research presented in this thesis is the original work performed by the author. This thesis was supervised by Dr. Mina Hoorfar at the Advanced Thermo-Fluidic Laboratory (ATFL) in the School of Engineering, University of British Columbia. Parts of this thesis have been published in different journals and presented at conferences. The details of the publications and the authorâs contributions to them are explained below: a. Refereed journal publications [1] M. Mohaghegh Montazeri, N. De Vries, A. Afantchao, A. Oâbrien, P. Kadota, M. Hoorfar, âDevelopment of a sensing platform for nuisance sewer gas monitoring: hydrogen sulfide detection in aqueous versus gaseous samplesâ, IEEE Sensors Journal. (August 2018) - Used with permission Contribution: My contribution in this paper included the following: (i) conducting the experiments, (ii) developing the setup, and (iii) writing the paper. [2] M. Mohaghegh Montazeri, Allen OâBrien, Mina Hoorfar, âDevelopment of numerical modeling of microfluidic-based gas detectors: dimension effect on sensitivity/selectivity/recoveryâ, Analytical chemistry. (Submitted â August 2018) - Used with permission Contribution: My contribution in this paper included the following: (i) the development of the numerical simulation, (ii) conducting the experiment, and (iii) writing the paper. vii Conference proceedings and presentations [1] Matthew Barriault, Mahyar Mohaghegh Montazeri, Allen OâBrien, Homayoun Najjaran and Mina Hoorfar, âQuantitative Natural Gas Discrimination For Pipeline Leak Detection Through Time-Series Analysis of an MOS Sensor Responseâ, CSME 2018 Conference, Toronto, Canada. (May 2018). Contribution: My contribution in this paper included the following: (i) developing the numerical simulation, (ii) providing the simulation results, and (iii) editing of the paper. [2] P. Mehrabi, J. Hui, M. Mohaghegh Montazeri, K. Tien Nguyen, A. Logel, A. O'Brien and M. Hoorfar, âSmelling through microfluidic olfaction technologyâ, CSME 2018 Conference, Toronto, Canada. (May 2018) Contribution: My contribution in this paper included the following: (i) defining the strategies, (ii) conducting the experiment, and (iii) editing of the paper. [3] M. Mohaghegh Montazeri, N. De Vries,A. Afantchao, P. Mehrabi, E. Kim,A. Oâbrien, P. Kadota, H. Najjaran, M. Hoorfar, âA Sensor for Nuisance Sewer Gas Monitoringâ, IEEE Sensors 2017 conference, Glasgow, UK. (Oct 2017) â Selected as one of the top papers. Contribution: My contribution in this paper included the following: (i) conducting the experiments, (ii) developing the setup, and (iii) writing the paper. viii Table of Contents Abstract .......................................................................................................................................... iii Lay Summary .................................................................................................................................. v Preface............................................................................................................................................ vi Table of Contents ......................................................................................................................... viii List of figures ................................................................................................................................. xi List of symbols ............................................................................................................................. xiii List of abbreviations ..................................................................................................................... xv Acknowledgments....................................................................................................................... xvii Dedication .................................................................................................................................. xviii Chapter 1: Introduction ................................................................................................................... 1 1.1 Background ...................................................................................................................... 1 1.1.1 Nuisance sewer gas monitoring ................................................................................ 1 1.1.2 Simulation of microfluidic-based gas detector ......................................................... 2 1.2 Literature review .............................................................................................................. 3 1.2.1 H2S formation in wastewater .................................................................................... 3 1.2.2 Treatment processes .................................................................................................. 6 1.2.3 Methods of measuring H2S ....................................................................................... 8 1.3 Motivation ...................................................................................................................... 10 ix 1.4 Objectives ....................................................................................................................... 11 1.5 Organization of thesis..................................................................................................... 11 Chapter 2: Experimental setup ...................................................................................................... 13 2.1 Microfluidic-based artificial olfaction ........................................................................... 13 2.2 Benchtop setups.............................................................................................................. 15 2.2.1 H2S aqueous-based setup ........................................................................................ 15 2.2.2 Development of auxiliary equipment: base platform and testing chamber ............ 18 2.2.3 Development of auxiliary equipment: heater design .............................................. 21 2.2.4 Development of auxiliary equipment: automated gas testing ................................. 26 2.2.5 Gas chromatography sample preparation method................................................... 30 2.2.6 Development of auxiliary equipment: other components ....................................... 31 2.2.7 H2S gaseous-based setup......................................................................................... 32 2.2.8 Static setups ............................................................................................................ 33 Chapter 3: Theory ......................................................................................................................... 36 3.1 Diffusion model.............................................................................................................. 36 3.2 Adsorption isotherm ....................................................................................................... 37 3.3 MOS sensor reaction modeling ...................................................................................... 38 3.4 Heat transfer and momentum ......................................................................................... 40 3.5 Normalization ................................................................................................................. 41 3.6 Selectivity ....................................................................................................................... 41 x 3.7 Sensitivity ....................................................................................................................... 41 Chapter 4: Microfluidic-based gas detector simulation ................................................................ 43 4.1 Verification and validation ............................................................................................. 44 4.2 Effect of different phenomena on detector response ...................................................... 46 4.3 Dimensional study .......................................................................................................... 49 4.4 Effect of geometry on detector sensitivity and selectivity ............................................. 53 Chapter 5: Gas sensing results ...................................................................................................... 55 5.1 Response curve analysis ................................................................................................. 55 5.2 Feature space analysis .................................................................................................... 62 Chapter 6: Summary, contributions and future works .................................................................. 65 6.1 Summary ........................................................................................................................ 65 6.2 Contributions .................................................................................................................. 66 6.3 Future works ................................................................................................................... 66 References ..................................................................................................................................... 68 xi List of figures Figure 1.1 Corrosion effect on the concrete sewer pipes ................................................................ 4 Figure 1.2 A summary of processes and effects related to H2S in the sewer system ..................... 5 Figure 1.3 The H2S concentration profile in a cross-sectional view of a sewer pipe ..................... 6 Figure 1.4 H2S mitigation technologies in sewer systems .............................................................. 7 Figure 1.5 A monitoring device is shown being tested in the field .............................................. 10 Figure 2.1 Microfluidic-based artificial olfaction system for nuisance gas monitoring ............... 13 Figure 2.2 Aqueous sample monitoring and detection platform with supporting systems ........... 17 Figure 2.3 Benchtop setup and the auxiliary instruments ............................................................. 18 Figure 2.4 The picture of the third iteration of benchtop setup and auxiliary components .......... 19 Figure 2.5 Benchtop setup: the first iteration ................................................................................ 19 Figure 2.6 Benchtop setup: the second iteration ........................................................................... 20 Figure 2.7 Testing chamber third iteration with mounted automated gas testing valve ............... 21 Figure 2.8 First heater design........................................................................................................ 22 Figure 2.9 The first heater design simulation result ..................................................................... 23 Figure 2.10 The second heater design simulation result ............................................................... 23 Figure 2.11 The second heater design, the fabricated heater on a glass substrate ........................ 24 Figure 2.12 The second heater design fabricated on polyamide ................................................... 25 Figure 2.13 A porcelain crucible wrapped by a nickel-chromium heating element ..................... 26 Figure 2.14 An initial design for the automated gas testing mechanism ...................................... 27 Figure 2.15 Two initial designs of gas testing valves and their fabricated prototype .................. 28 Figure 2.16 Automated gas testing valve with the microfluidic-based gas detector .................... 29 Figure 2.17 The gas testing valve inside an enclosed chamber for in-situ applications ............... 29 xii Figure 2.18 GC sampling setup .................................................................................................... 30 Figure 2.19 Auxiliary sensors ....................................................................................................... 31 Figure 2.20 Gaseous-based setup .................................................................................................. 33 Figure 2.21 Static setup................................................................................................................. 34 Figure 2.22 The setup used for the volatile compounds present in a liquid form......................... 35 Figure 4.1 Visualization of concentration profiles at the exposure and recovery steps ............... 44 Figure 4.2 Comparison of experimental and simulation results for methane and ethane ............. 45 Figure 4.3 Comparison of experimental and simulated results for methanol and ethanol ........... 45 Figure 4.4 Comparison of experimental and simulated results for H2S at 230 ppm .................... 46 Figure 4.5 Effect of the temperature of the MOS heating element on the detector response ....... 47 Figure 4.6 Temperature and velocity profiles ............................................................................... 48 Figure 4.7 Comparison of the effect of different phenomena on the detectorâs response ............ 48 Figure 4.8 Dimensional study of the detector on the response and selectivity ............................. 51 Figure 4.9 Visualization of concentration profile of different geometries at time 35s ................. 52 Figure 4.10 Relative performance, selectivity and sensitivity of various geometries .................. 54 Figure 4.11 Relative performance of various geometries ............................................................. 54 Figure 5.1 Correlation between H2S in the aqueous sample and H2S after vaporization ............. 56 Figure 5.2 Response of different concentrations of H2S using aqueous-based setup ................... 58 Figure 5.3 Response of different concentrations of H2S using gaseous-based setup ................... 60 Figure 5.4 Simulation results of gas diffusion in the detectorâs microfluidic channel ................. 61 Figure 5.5 Feature extraction comparison of aqueous-based and gaseous-based setups .............. 61 Figure 5.6 Comparison of the smell-prints of methane and H2S .................................................. 63 Figure 5.7 Comparison of H2S and methane in a 3D feature space .............................................. 64 xiii List of symbols nm Nano meters Dxy Diffusion rate of component x in component y Cx Concentration of component x ppm part per million ppb part per billion ppt part per trillion Ï Density cp Heat capacity T Temperature t Time h Heat transfer coefficient k Thermal diffusivity G acceleration of gravity g gram u velocity mg milligram L liter xiv min minute cc cubic centimeter RA reaction rate with regard to component A kA reaction rate constant K Equilibrium reaction constant xv List of abbreviations AWG American wire gauge UHMW Ultra-high-molecular-weight polyethylene PP Polypropylene PVDF Polyvinylidene fluoride HDPE High-Density Polyethylene VOC Volatile organic compound GC Gas chromatography MS Mass spectrometry MOS Metal oxide semiconductor SOB Sulfide-oxidizing bacteria SRB Sulfate-reducing bacteria ORP Oxidation reduction potential; DO Dissolved oxygen ICP-AES Inductively coupled plasma-atomic emission spectrometry NaOH Sodium hydroxide H2S Hydrogen sulfide LOD limit of detection xvi SO2 Sulfur dioxide BET BrunauerâEmmettâTeller IUPAC International Union of Pure and Applied Chemistry Cr Chrome Au Gold RO Reverse Osmosis THC Tetrahydrocannabinol ICP Inductively coupled plasma AES Atomic emission spectrometry xvii Acknowledgments Above all, none of this could have happened without the strong support from my family. My parents, Dr. Mohammad Mohaghegh Montazeri and Dr. Shahla Farzipour, who are making sacrifices for their children, every day, are the main reason for all of these achievements. Special gratitude goes to my beloved wife, Bita Nazyab, for being along my side during every step of my life and for being passionate with all the difficulties. Great thanks to my brother, Mehdi, for providing me continuous support and encouragement. I would like to express my sincere gratitude to my great supervisor, Dr. Mina Hoorfar, for her great support in every step of my studies and for providing unlimited resources to make all these researches possible. She was always open toward new ideas, motivating innovation and encouraging toward making ideas happen. She taught me that nothing is impossible. I would like to thank our lab manager, Allen OâBrien, for managing projects and the lab environment in a well-ordered manner. Special thanks to my friends and lab-mates, Hossein Montazerian, Arash Dalili and Matthew Barriault for the help and companionship during my research, and providing a collaborative research environment. I would also like to appreciate our talented undergrad students, David Afantchao and Niels de Vries, for their great work in the design and development of the setups used in the current work. xviii Dedication For my beloved family for all their support and sacrifices to make all these achievements possibleâ¦ 1 Chapter 1: Introduction 1.1 Background 1.1.1 Nuisance sewer gas monitoring Nuisance gases produced in a sewer environment, such as hydrogen sulfide (H2S), can cause serious health problems, and are the main cause of corrosion which can ultimately lead to sewer pipe collapse costing hundreds of millions of dollars to repair every year. Structural damages and biogenic gas generation also cause leakage of unpleasant odors from channels, manholes, vents, and pump stations, which is a great concern in metropolitan areas (Jiang, Sun, Sharma, & Yuan, 2015). Although various strategies have been implemented to mitigate biogenic gases, they are more effective if âhotspotsâ of gas generation are identified for targeted treatment (Park et al., 2014). Also, for different levels of biogenic gas concentrations, different mitigation methods must be applied to a sewer system. Thus, there is an urgent need in the wastewater industry for an automated, real-time, in-situ detection of multiple target gases that are dissolved in the liquid phase. Technologies currently available have certain limitations: they are often bulky, inaccurate, unreliable, incapable of examining samples in a liquid phase, and can only detect a limited number of compounds (Lim et al., 2013; Y. Liu et al., 2014; Y. Liu, Sharma, et al., 2015; Y. Liu, Ni, Sharma, & Yuan, 2015). Detection of target compounds within a liquid phase is specifically important because (i) the headspace within a sewer pipe can vary, and (ii) it is necessary to detect nuisance gas generation (e.g., H2S) near the hotspots of biogenic activity in anaerobic biofilms (i.e., where the compound has not yet entered the gaseous headspace). This is especially important 2 since small concentrations of H2S dissolved in liquid can result in very high ppm concentrations (>100) in the headspace (Park et al., 2014). One of the goals of this study is to develop a suitable technology capable of measuring concentrations of target compounds in the liquid-phase of sewer lines. We present a benchtop model system which is independent, and capable of real-time in-situ monitoring of liquid-phase target compounds, especially H2S. The setup is a preliminary device which will be further developed for installation inside sewer systems and providing online data communication for threshold monitoring by operators and automated systems. The proposed setup includes a highly-selective microfluidic-based artificial olfaction detector integrated into a supporting platform used for liquid-sample vaporization and extraction. Since the vaporization process can affect the microfluidic detector response, the proposed setup is compared against gaseous samples (for which another setup is presented in this thesis). This comparison provides a better understanding of the vaporization effects on the detector response which will be used to identify the best strategy for detector calibration. Finally, the calibrated setup is used to compare the detector responses against H2S and another component of interest in the sewer system (i.e., methane). The results show that the proposed technology can differentiate between different compounds. 1.1.2 Simulation of microfluidic-based gas detector Similar to any other gas sensing methods, one of the main challenges of the microfluidic-based gas detector is its calibration (training) for a wide range of parameters including gas types and concentrations, temperature, pressure, humidity, to name a few. This requires numerous detectors and a prodigious number of experiments and which are both costly and time consuming. Another goal of this thesis is to present an accurate simulation of this type of detectors, encompassing all 3 the phenomena occurring in the device. The simulation will not only solve the problems of the costly and time-consuming calibration process but also remove human and instrumental errors. It can also help with the design and experimental limitations such as the concentration change which can only be observed in the experimental results through the response curve where the sensor is located; whereas in the simulation such a change can be monitored at every point of the device. Another benefit of the numerical simulation is optimization of the sensor performance (expressed generally in terms of sensitivity, selectivity and response time (X. Liu et al., 2012)), with respect to the design and operational/environmental factors. For this goal, the microfluidic-based gas detector and the experimental setups used to obtain the experimental data for model verification/validation are presented. Then, the details of the phenomena occurring inside the detector and their theories are explained. The simulation results, after verification/validation, are obtained to show the effect of each phenomenon separately for an analyte at a certain concentration. Then, the model is used to perform dimensional analysis and the effect of different parameters on the detector performance for two analytes. Finally, this study is extended to different concentrations of the target analytes. 1.2 Literature review 1.2.1 H2S formation in wastewater Microbial activities in the sewer system is the main source of nuisance gasses (such as H2S), which are hazardous and can cause corrosion to the sewer pipes which may ultimately cause the infrastructure failure. H2S is an odorous and toxic compound (Eghbal, Pennefather, & OâBrien, 2004; Malone Rubright, Pearce, & Peterson, 2017). Studies have shown life threatening effects of H2S; as an example, a concentration of 1000ppm H2S in the air can kill rats in about 15 to 30 4 minutes (Beauchamp et al., 1984). Other than being toxic and causing unpleasant odor, H2S is associated with biogenic corrosion of concrete. As shown in Figure 1.1, the concrete corrosion is minor in the range of 0.1 mg/L of total sulfide and severe in the concentrations of more than 2 mg/L (Hvitved-Jacobsen, Vollertsen, & Matos, 2002). This is a serious problem in the sewer pipeline which can ultimately cause infrastructure failure; the cost associated with such infrastructure failures in cities like Los Angeles, Flanders (Belgium) and cities in Germany is estimated to be in the magnitude of billions of dollars (Hvitved-Jacobsen et al., 2002; Kaempfer & Berndt, 1998; Zhang et al., 2008). Figure 1.1 Corrosion effect on the concrete sewer pipes. Left photo: a minor corrosion can be seen on the concrete sewer pipe at the top left of the picture. Middle photo: Stronger corrosion, covering the whole pipe wall. Right photo: A closer look at the corrosion over the concrete sewer pipe. (reprinted from (Vollertsen, Nielsen, Jensen, Wium-Andersen, & Hvitved-Jacobsen, 2008) with permission from Elsevier) H2S is generated by the bacterial reduction of the sulfate in an anaerobic condition (Figure 1.2). Therefore, sulfate concentration, pH level and oxygen availability seem to effect H2S production (Chen et al., 2011; J. Arogo, R. H. Zhang, G. L. Riskowski, & D. L. Day, 2000). In an anaerobic condition, many species of proteolytic bacteria such as E. coli (Escherichia coli), Proteus vulgaris and Pseudomonas pycocyanea can form H2S through hydrolysis of protein from the compounds containing organic sulfur. Another source of H2S is bacterial reduction of sulfates to sulfides by Sporovibrio desulphuricans in a similar anaerobic condition. In the sewer system, the corrosion is happening above the water level, so it does not happen in the pipes which are running full (Parker, 5 1951). Figure 1.3 shows the H2S profile inside the sewer pipes; H2S concentration is the highest in the bottom of the pipe (in the liquid phase), and its concentration increases near the generation points, which shows the importance of developing a monitoring solution which can take the samples from the bottom point of the pipes and measure H2S in the liquid phase. Figure 1.2 A summary of processes and effects related to H2S in the sewer system. (reprinted from (Zhang et al., 2008) with permission from Elsevier) SOB: sulfide-oxidizing bacteria; SRB: sulfate-reducing bacteria; ORP: oxidation reduction potential; DO: dissolved oxygen. 6 Figure 1.3 The H2S concentration profile in a cross-sectional view of a sewer pipe. (reprinted from (Parker, 1951) with permission from the Water Environment Federation) 1.2.2 Treatment processes To solve the corrosion issue caused by H2S, a few methods have been studied, which can be classified into: (i) the change in the sewer pipeline design, (ii) source pretreatment technologies, (iii) replacing pipes with a more resistant materials, and (iv) mitigating H2S formation and corrosion (Figure 1.4) (De Belie et al., 2004; Estrada, Kraakman, Muñoz, & Lebrero, 2011; Kiliswa, Alexander, & Beushausen, 2015; Nielsen, Hvitved-Jacobsen, & Vollertsen, 2006; Zhang et al., 2008). However, due to the high cost associated with the sewer pipeline reconstruction, the best approach is mitigation of H2S generation/corrosion (U.S. EPA., 1991). In the existing systems, numerous methods are currently available for preventing corrosion due to H2S. For example, the low redox potential is believed to be the main cause of H2S production; thus, the addition of oxidants (like hydrogen peroxide, chlorine, potassium permanganate, nitrate, nitrite or O2) can be used to mitigate H2S generation (Chen et al., 2011; U.S. EPA., 1991; Yang, Vollertsen, & Hvitved-Jacobsen, 2005). 7 Precipitants are other solutions to reduce H2S concentration in the gas phase. In this method, metal salts (such as ferrous chloride, ferrous sulfate or zinc salts) are used. Nielsen et al. showed that the precipitation can happen quickly and remove dissolved sulfides from the wastewater (Haaning Nielsen, Lens, Vollertsen, & Hvitved-Jacobsen, 2005; U.S. EPA., 1991). Another approach is related to the change in the pH levels using chemicals such as sodium hydroxide (NaOH). This can prevent H2S from being released into the gas phase when pH is above 9, or it can inhibit the sulfide reducing bacterial (SRB) activity when the pH is about 12.5-13 (U.S. EPA., 1991; Zhang et al., 2008). Figure 1.4 H2S mitigation technologies in sewer systems. (reprinted from (Zhang et al., 2008) with permission from Elsevier)1 1 Reprinted from Water Research, Volume 42, Issues 1â2, Lehua Zhang, Peter De Schryver, Bart De Gusseme, Willem De Muynck, Nico Boon, Willy Verstraete, Chemical and biological technologies for hydrogen sulfide emission control in sewer systems: A review, Pages 1-12, 2008, with permission from Elsevier 8 There are negative side effects associated with any of the methods mentioned above: e.g., the high cost for the long time use, environmental hazards, handling, and safety concerns regarding the usage of some of the additives (U.S. EPA., 1991). Therefore, it is desirable to decrease the amount of chemicals being used in the waste water system. Using a monitoring system that can provide information regarding the H2S concentration in the sewer and consequently SRB activity can help increasing the efficiency of the treatment methods, and hence, reducing the amount of chemical needed to mitigate H2S generation. 1.2.3 Methods of measuring H2S A few methods are available for measuring H2S in the sewer systems. The electronic nose (e-nose) is a well-known method to measure H2S using feature extraction methods which can recognize and measure the concentration of H2S (Stuetz, Fenner, & Engin, 1999). There are commercial devices available based on pulsed fluorescence techniques which can be used to measure H2S and SO2 in air. These are not meant to measure H2S in a liquid phase and their responses are affected by humidity (Bluhme, Ingemar, Meusinger, & Johnson, 2016). Toda et al. developed a diffusion scrubber-based portable device for continuous atmospheric H2S measurement. This device shows an excellent sensitivity with a limit of detection (LOD) of less than 100 ppt. However, there are a few limitations associated with the integration of this device into the sewer system including inability to measure H2S in the liquid phase, use of toxic reagents and the short maintenance period (Toda, Dasgupta, Li, Tarver, & Zarus, 2001). For this method, selectivity can also be challenging as it uses different membranes for separation of compounds (Toda, Ohira, Tanaka, Nishimura, & Dasgupta, 2004). It worth mentioning that the structure of the 9 scrubber seems to also affect the efficiency of gas collection in a similar method (Ohira & Toda, 2005, 2008). In another study, Petruci et al. proposed a paper-based sensor for measuring H2S in the gas phase. This is achieved by using a palladium complex which contains a fluorescent ligand. The ligand is released when exposed to H2S and intensifies the fluorescence. Although this method has a high sensitivity, LOD of 2ppb, it suffers from low throughput, sensitivity to humidity, results inconsistency, and inability to be used for in-situ applications (Petruci & Cardoso, 2015). Gas chromatography (GC) and Gas chromatography coupled with Mass spectrometry (GC-MS) which are the well-established gas analysis techniques, are commonly used for H2S measurement, however, are very limited to the laboratory use as they are bulky and require skilled personnel for their operation (Maebashi et al., 2011; Vitvitsky & Banerjee, 2015). There are various methods for analysis of H2S in a liquid sample. Inductively coupled plasma-atomic emission spectrometry (ICP-AES) (Ämelík, Machát, Otruba, & Kanický, 2010; Colon, Todolí, Hidalgo, & Iglesias, 2008) is one of the methods which has a satisfactory LOD for measuring sulfide in water; however, this technique has limitations including the use of chemicals, and interferences that form compounds such as sulfur-containing species and heavy metals (Ämelík et al., 2010). Another potential method which is able to conduct online measurement of dissolved methane and sulfide is proposed by Liu et al. (Figure 1.5). In this system, the dissolved compounds are stripped from the liquid phase into the gas phase according to Henryâs law. Then, a commercial infrared methane detector and the commercial ODASL-H2S-2000 sensor are employed for detection of methane and H2S in the gas phase, respectively. This system needs further improvements (such as miniaturization, increasing energy efficiency, increasing the 10 maintenance period, and reducing the sample size and chemical usage) to be able to be used for in-situ monitoring applications (Y. Liu, Sharma, et al., 2015). Figure 1.5 A monitoring device is shown being tested in the field. ( reprinted from (Y. Liu, Sharma, et al., 2015) with permission from Elsevier) 1.3 Motivation As mentioned earlier, the current available nuisance compound detection techniques are limited with respect to their size, liquid sample analysis, automation, ease of use, on-line communication etc. Thus, we intend to develop an artificial olfaction setup which can measure nuisance compounds inside the sewer pipeline network and has the mentioned features. One of the major challenges of such a setup is calibration of the sensor based on wide ranges of different parameters (such as effect of mixture change, temperature, pressure and humidity) which demands a prodigious number of experiments. Simulation of the artificial olfaction system can help solving the problem as it 1) reduces the number of experiments needed for calibrating of the system, 2) reduces the effect of human and instrumental errors, 3) reduces the time required to study new designs, and 4) removes the limitations in terms of what can be observed in the experiments. 11 1.4 Objectives The main objective of this work is to develop a monitoring system based on microfluidic-based artificial olfaction detector, which has the following features: 1) measuring nuisance compounds (such as H2S) in a liquid phase, 3) recognizing the type of compounds, 4) measuring the compoundâs concentrations, 5) functioning autonomously, 6) being energy efficient, 7) ability to be integrated along the sewer pipeline, and 8) tolerating sewerâs harsh conditions. The mentioned setup must be calibrated against H2S and other mixtures. Another objective of this study is to develop a numerical simulation that can generate detector response to different mixtures, different concentrations and different environmental conditions. Such a simulation will eliminate the number of experiments required to predict detector behavior to changes mentioned above as well as the changes to the geometry for the optimization purposes. For this subjective, the work here intends to 1) simulate the microfluidic-based gas detector and the experimental process, 2) conduct the verification and validation of the simulation, 3) apply the model to the nuisance sewer gas monitoring system developed in the first objective, and 4) generate data based on a range of parameters such as humidity and different gas mixtures. 1.5 Organization of thesis The mentioned objectives are presented in the following chapters: In the next chapter (Chapter 2), the experimental setups and their fabrication methods are presented which is followed by the theory chapter where the theory behind the numerical simulation is explained in detail (Chapter 3). In Chapter 4, the results of the numerical simulation are presented and used toward dimensional studies and understanding different phenomena occurring in the microfluidic-based olfaction detector. In Chapter 5, the experimental results of the microfluidic 12 sensor toward nuisance sewer compounds are presented and discussed, which provides insights regarding the future approach on developing the hotspot monitoring system. In the last chapter, the thesis main achievements and results are summarized, and potential future works for the current research are presented. 13 Chapter 2: Experimental setup 2.1 Microfluidic-based artificial olfaction The core sensing technology is a highly-sensitive and selective microfluidic-based gas detector which is specially designed and calibrated for nuisance gases inside the sewers (Figure 2.1). Figure 2.1 Microfluidic-based artificial olfaction system for nuisance gas monitoring. The schematic shows a metal oxide semiconductor (MOS) sensor and a microchannel coated with different metallic and polymeric layers. In Figure 2.1, a schematic of the detector is shown. The detector is fabricated using a previously reported process (Paknahad, Bachhal, Ahmadi, & Hoorfar, 2017): a 3D-printed microfluidic platform is coated with several layers (including metals and polymers), and a Figaro TGS2602 14 metal oxide semiconductor (MOS) sensor is embedded at the terminal-end of the microfluidic channel. A typical coating around the channel consists of Parylene C, Au, and Cr, and a typical dimension of the channel is 3 cm length and 500 µm height (Paknahad et al., 2017). The detector provides a powerful tool for analyzing the gas compounds according to their diffusion rate inside the channel and the adsorption/desorption phenomena occurring over the channel walls. Based upon the adsorption/desorption properties between the gaseous compound and selected coatings, selectivity can be optimized. The adsorption isotherm in this system can be predicted using BrunauerâEmmettâTeller (BET) theory as it is in agreement with type III adsorption isotherms classification defined by International Union of Pure and Applied Chemistry (IUPAC) (Brunauer, Emmett, & Teller, 1938; Sing, 1985). The diffusion phenomenon can also be modeled using Maxwell-Stefan multicomponent mass transfer equation (Krishna & Wesselingh, 1997). The detector response (i.e., the voltage vs. time which is also referred to as a âsmell-printâ) is further processed using a feature extraction method which allows us to identify specific smell-prints of a sample including components of a gas mixture and their respective concentrations (Paknahad et al., 2017). Compared to other conventional technologies such as gas chromatography (Spanjers, Beach, Jones, & Dauenhauer, 2017), mass spectrometry (Leahy-Hoppa et al., 2010), and Raman (Eichmann et al., 2014), the proposed microfluidic-based gas detector is more compact, portable, low cost and easy to use. Although portable devices have been developed using the above-mentioned conventional technologies, they still suffer from inability to detect small molecules, low sensitivity in certain environmental conditions, and inability to capture sulfide-based gases (Chupka, 1972; Spanjers et al., 2017; Zimmermann, Krippner, Vogel, & Müller, 2002). 15 2.2 Benchtop setups For the experiments with H2S, two types of samples and methods were used for sensor calibration: (a) aqueous samples using the main benchtop setup which is designed to be used as a detection device inside the sewer system, and (b) standard gas samples using a flow-based calibration setup. Outputs from these setups were compared to gain a better understanding of the vaporization effect (occurring in the aqueous-based setup) on the sensor response and future compensation for the changes in the temperature, humidity, and pressure. Two other setups were also used for collecting data for the simulation validation which are referred to as static setups in this thesis. 2.2.1 H2S aqueous-based setup The aqueous-based setup is designed to extract a liquid sample, vaporize the sample, and then expose the vapor portion to the microfluidic-based gas detector (Figure 2.2 and Figure 2.3). The apparatus consists of a custom-built syringe pump (with dispensing accuracy of 0.4 µl), embedded sensors (for monitoring temperature, humidity, and pressure), a chemically-resistant vaporization chamber, a custom-built heater, and an air pump. All components are connected with a quarter-inch polyurethane tubing and solenoid valves. To improve the setup resistance to the sewer effluent and nuisance gases, the vaporization chamber is made of ultra-high-molecular-weight polyethylene and coated with Parylene C. The custom-built heater is fabricated using nickel-chromium wires affixed around a porcelain crucible with thermal conductive cement. A microcontroller connected to the sensors, syringe pump, valves and heater is implemented in the platform to control the sample delivery and vaporization process, and perform data logging for the microfluidic-based gas sensor. Sensor parameters and outputs are displayed on a LCD. Further developments can be performed on this setup to make it self-sufficient by integrating all auxiliary 16 components on the platform, similar to the studies reviewed in (Boyd-Moss, Baratchi, Di Venere, & Khoshmanesh, 2016). The testing process of the aqueous-based setup consists of 3 steps: sample extraction, vaporization, and exposure to the microfluidic-based gas detector. 1. Sample extraction: during sample extraction air bubbles must be prevented within the tubing to ensure that H2S does not escape prematurely into the gaseous phase. Our custom-made syringe pump works in conjunction with a 3-way solenoid valve to pump the sample into the tubing leading to the vaporization chamber. This process occurs in a repeated 2-stage process including the following steps: a. The gas-tight syringe is extended to pump in the sample from the container through the 3-way valve. b. The valve closes to connect the syringe to the tube leading to the vaporization chamber, and the syringe retracts to push the sample into the tubing. 2. Vaporization: the setup delivers samples, in the range of 50 µl to 1 ml, to the vaporization chamber. To make sure there are no droplets of sample remaining in the tubing, an air pump pushes air from the vaporization chamber through the tubing. This chamber contains the custom-made heater to vaporize the entire liquid sample, releasing all H2S in the sample into the headspace. The air pump ensures that the vaporized sample is evenly distributed throughout the chamber prior to exposure to the microfluidic-based gas detector. The pump also recovers the chamber after each test. 3. Microfluidic detector exposure: after the sample is injected into the vaporization chamber and heated until fully vaporized, the sensor is exposed to the sample 10 seconds after data 17 logging starts. Then, the detector is exposed to the fresh air for 40 seconds. The response of the detector is recorded for a total of 250 s. Figure 2.2 Aqueous sample monitoring and detection platform with supporting systems. The setup includes 1) a syringe and custom-built syringe pump, 2) an automated delivery mechanism of a network of tubes and valves, 3) a microfluidic-based gas sensor, 4) a testing chamber 18 Figure 2.3 Benchtop setup and the auxiliary instruments For the aqueous-sample testing, a hydrogen sulfide water solution (LabChem) containing 0.4% H2S and 99.6% water is used. However, as the H2S concentration is unstable in water, the sample needs to be characterized for each round of testing. For this purpose, gas chromatography (GC) is used. Different concentrations of H2S in the aqueous samples were obtained by diluting the original sample with reverse osmosis (RO) water. 2.2.2 Development of auxiliary equipment: base platform and testing chamber For building a setup, one of the main aspects is to have a base platform which can support other components of the setup. In this work, the setup and its base platform went through numerous development iterations (Figure 2.4). The first iteration, as it is shown in Figure 2.5, was a simple approach to the problem. It consisted of a 3D printed cubic testing chamber where vaporization and detector exposure took place. It had another smaller 3D-printed cubic chamber for the liquid sample storage. The sample flow through the system was by gravity, and paper filters were used 19 for sample filtration. This design had a few limitations: (i) the process control was totally manual causing errors and affecting the reproducibility of the results, (ii) the sample was in contact with headspace, (iii) the filter performance was not satisfactory, and (iv) the materials used did not have resistance properties towards heat and chemicals. Figure 2.4 The picture of the third iteration of benchtop setup and auxiliary components. The picture is showing the development steps of each component. Figure 2.5 Benchtop setup: the first iteration 20 The second iteration of the platform, which is shown in Figure 2.6, was the first try to automate the sampling process. In this iteration, the setup was equipped with an in-house designed syringe pump which could extract the sample from the sample container and push it through different parts of the setup. Also, a metallic base is made, so other components such as syringe pump, valves, air pumps, can be mounted on it. The main testing chamber is redesigned to reduce the sample needed for each test. Also, Ultra High Molecular Weight Polyethylene (UHMWPE) was used to fabricate the chamber to increase the chemical resistance of the setup. Figure 2.6 Benchtop setup: the second iteration To add more capabilities and develop a fully automated device, the third iteration of the setup was developed which was equipped with numerous sensors to monitor humidity, temperature and pressure (Figure 2.7). Arduino Mega microcontroller was employed in the system to increase the 21 device capacity to run different parts. Also, a special sampling system was designed to automate the gas detector testing procedure and remove the human error in the sampling process. Figure 2.7 Testing chamber third iteration with mounted automated gas testing valve 2.2.3 Development of auxiliary equipment: heater design The heater is one of the challenging parts of the setup. It must vaporize the sample in a reasonable amount of time, has a compact design, must be chemically resistant, and do not release any gas inside the testing chamber. Similar to other components, the heater went through a number of development iterations. The first heater design was fabricated using a microfabrication process. In the first step, a mask was designed and printed (using an office printer) on a transparency film. Then, a 65-nm layer of chromium and a 40-nm layer of gold were sputtered on a glass slide; the photoresist S1805 was spin coated on the glass which was placed on the hotplate at 115 ºC for 1 min. In the next step, the 22 glass slide was covered by the printed mask and exposed to UV-light for 8 sec. Then, the chip was fabricated using in the following fabrication process: 1) the exposed parts of photoresist were removed using the photoresist developer, 2) the uncovered gold was removed using the gold etchant, 3) the uncovered chromium was removed using the chromium etchant, and 4) the rest of photoresists was removed using the photoresist remover. Between each step, RO water was used to wash the chemicals from the glass. The fabricated heater and the mask used for the fabrication can be seen in Figure 2.8. Figure 2.8 First heater design. The fabricated heater is shown on the left side and the mask is shown on the right side Although the first design had a great chemical resistance property, it was not able to vaporize the sample in a timely manner. Therefore, a numerical simulation was performed to increase the design efficiency by taking into account the power source limitation. The heater was modeled as a thin layer of gold on a glass material, placed on another plastic material which was kept at 25 ºC at the boundaries. The top surface transfers heat to the air through natural heat convection. A 12- was applied at the two ends of the heater design. Thus, the applied current generates heat which is dispersed in the air by natural convection and through conduction in the glass and the plastic. Using 23 these assumptions, the simulation was run for two designs shown in Figure 2.9 and Figure 2.10. As it is evident, the first design (which was used as the first heater design iteration) is not producing a significant amount of heat; whereas in the second design, there is a significant amount of heat produced by the heater. This shows that the second design is a potential design to be used as the heater in the current application. Figure 2.9 The first heater design simulation result. (temperature profile â) Figure 2.10 The second heater design simulation result. (temperature profile â) 24 Based on the results of the simulation, the new design was fabricated using the same fabrication method explained before. The second heater was able to produce a significant amount of heat. However, although the glass slide used for the fabrication has very good thermal resistance properties, it was not able to stand the heat from the fabricated heater and fractured after a few times of use (Figure 2.11). In the next step, the second design was fabricated on a different material. Polyamide is widely used for microfabrication applications especially for fabrication of heaters (Han, Tan, Sato, & Shikida, 2005; Suganuma, Sasaki, Nakayama, Muroyama, & Nonomura, 2017). Using polyamide and following similar fabrication procedure, the new heater was fabricated (Figure 2.12). However, it was not able to withstand the high temperature needed to vaporize water within a reasonable time (less than a minute). Figure 2.11 The second heater design, the fabricated heater on a glass substrate. The real picture of the fabricated heater is shown on the left side and the heater design is shown on the right side. 1mm 5mm 25 Figure 2.12 The second heater design fabricated on polyamide The final iteration of the heater design involved the use of the Omega nickel-chromium 60 resistance heating wire of 30 AWG. Nickel-Chromium 60 is one of the most common metallic resistance wires used for heating applications; it is an alloy of 60% nickel and 16% chromium. It can tolerate operating temperatures up to 1000 ºC and has excellent corrosion resistance properties which makes it a suitable choice for the current work. The resistance wire was wrapped in a spiral pattern around a Chemglass porcelain crucible which was chosen because for its great chemical and heat resistance properties. To fix the resistance wire around the crucible and also increase its heat conductivity, OMEGABOND 600 (high-temperature cement which is a heat and chemical resistant material) was used. Figure 2.13 shows the picture of the in-house made heater. 26 Figure 2.13 A porcelain crucible wrapped by a nickel-chromium heating element. The heater element is fixed by the heat conductive cement. 2.2.4 Development of auxiliary equipment: automated gas testing One of the main objectives in the development of the current setup is automation. The core technology, the microfluidic-based gas detector, follows a series of states. In the initial state, the detector is exposed to fresh air for 10 sec. At the onset of data recording, the sensor is exposed to the sample gas for 40 sec., and then it is exposed immediately to fresh air again. To automate this process, different ideas were tested which mostly had issues such as leakage, time delay and sample dilution. Some of the ideas and fabricated designs are shown in Figure 2.14 and Figure 2.15. The design shown in Figure 2.14 caused changes in the pressure in the system which affects the detector response. The design in Figure 2.15 (a) has a small chamber and two separate actuators for the chamberâs inlet to the sample and fresh air. This design also had issues such as leakage and delays in the response. The design in Figure 2.15 (b), which has one actuator for switching the detector between the exposure and recovery states, had sealing issues. 27 Figure 2.14 An initial design for the automated gas testing mechanism The final design was a 3D-printed device equipped with an Actuonix PQ12 actuator, controlled by an Arduino Uno microcontroller, pushing the device to a sealed outlet and expose the gas detector to the sample, and then pull the sensor back to the recovery position (Figure 2.16). The automated gas testing valve is designed to be used in the room condition where fresh air is present. It can also be placed inside an enclosed chamber for in-situ applications where fresh air is not present. In that case, the enclosed chamber will be filled with air using a gas cylinder. The proposed design is shown in Figure 2.17. . 28 (a) [i] [ii] (b) [i] [ii] Figure 2.15 Two initial designs of gas testing valves and their fabricated prototype 29 Figure 2.16 Automated gas testing valve with the microfluidic-based gas detector. (for the testing environments with available fresh air for sensor recovery) Figure 2.17 The gas testing valve inside an enclosed chamber for in-situ applications. (in absence of fresh air for sensor recovery) 30 2.2.5 Gas chromatography sample preparation method To extract the sample from the aqueous-based setup and deliver it to GC, a setup was developed using a 60-ml glass syringe and another 60-ml plastic syringe. The two syringes were connected to the main testing chamber by tubes (see Figure 2.18). At the beginning of testing, one of the syringes was fully expanded and the other one was fully closed. The sample was extracted a few minutes after the liquid sample vaporization was completed and when the chamber temperature reaches the room temperature. Using the plastic syringe, the gas sample was then exchanged between the two syringes by pushing and pulling it for several times, which created a homogenous sample in the system. Thereafter, the plastic syringe was fully expanded and disconnected from the system. The syringe was sealed using a luer-lock plug (providing great sealing) to prevent any mass transfer to and from the syringe. Figure 2.18 GC sampling setup 31 2.2.6 Development of auxiliary equipment: other components There are many other components which are included in the setup: micro controllers, valves, air pumps, displays, tubing and fittings. The setup is controlled with a programmed Arduino Mega which controls all the valves, syringe pump and air pumps, and also receives and shows the humidity, temperature and pressure sensors responses (Figure 2.19). Figure 2.19 Auxiliary sensors. An Arduino microcontroller and the display shown before being assembled on the main platform Cole-Parmer three-way solenoid pinch valves were used which were totally separated from the liquid sample, so there will be no chemical damage toward the valves. Consequently, all the tubing are replaced by Cole-Parmer Peroxide-cured silicone tubing in the final setup. The fittings/connectors used in the setup are luer-lock and barbed fittings made of polypropylene (PP), polyvinylidene fluoride (PVDF), and High-Density Polyethylene (HDPE) which have excellent chemical resistance properties. 32 In order to recover the chamber after each round of testing, an air pump was used to flow the fresh air inside the chamber for a few minutes. For this purpose, the air pump model L008C-11 from Hargraves was used and controlled by the microcontroller. Several sensors were also used at different points of the setup. These included two Adafruit BME280 temperature, humidity and pressure sensors and a thermocouple type-k. 2.2.7 H2S gaseous-based setup For calibration of the microfluidic-based gas detector against standard gas samples, a custom setup was designed (Figure 2.20) to mix two gasses (making a desired concentration) based on the flow ratio of the gases. This gaseous-based setup consists of two rotameters, a chamber, an automated in-house designed testing valve, and a microcontroller. The detector response was recorded using a data logger and a laptop computer. Two gaseous samples were used for testing: (a) a standard mixture of 100 ppm H2S and nitrogen, and (b) ambient air from an air compressor. The flowmeters enabled the flow of each gas and generate different concentrations based on the flow ratio. The sum of the two flowrates was always kept at 0.6 cm3/min: as one flowrate was changed, the other was adjusted accordingly to keep the sum at a constant level during all experiments. The outlets of the flowmeters were connected through a Y-connection where the two gasses were mixed. The mixture was then introduced to the testing chamber. By keeping the flowrates constant during the experiments, the steady state condition was achieved after about 3 min. This steady-state condition has also been tested using the microfluidic-based gas detector when it was exposed to the target gas. The results of this test showed no change in the detector response after 3 min. of adjusting the flow rates. 33 Figure 2.20 Gaseous-based setup. The setup includes 1) two rotameters, 2) a Y-connection where the samples are mixed, and 3) a costume valve and testing chamber. After reaching the steady-state condition, the test started by exposing the microfluidic-based gas detector to the sample inside the chamber for 40 s and then recovering it for 200 s. This process was automated using the custom-built valve controlled with the Arduino microcontroller. This valve was used to push the detector toward a sealed inlet on the chamber to expose it to the gas and then to remote the detector and expose it to fresh air for recovery. 2.2.8 Static setups The experimental results for the simulation validation were obtained using the setup shown in Figure 2.21, which included a 40-L chamber connected to a mass flow controller (MKS GM50A). The mass flow controller connected to the gas sample cylinder was able to control the amount of gas transferred to the chamber using a totalizer function. Based on the ideal gas law, the ppm concentration relation to the gas sample volume can then be calculated. The process involving the data collection in this setup includes four steps: 1) recording the sensor response starts at  =0 s 34 when the baseline is checked to make sure that the sensor has recovered from the previous experiments; 2) the microfluidic detector is exposed to the target gas at  = 10 s; 3) the microfluidic-based gas detector is exposed to the fresh air for recovery at  = 50 s; and 4) the recording is stopped at  = 200 s. The experimental results presented here can also be obtained for volatile compounds in a liquid form (such as ethanol and methanol). The setup used for these types of targets is shown in Figure 2.22, which included a 40-L chamber, where a certain amount of the volatile compound was deposited using a pipette; the sample evaporated and filled the chamber. Based on the sample density, molecular weight and the ideal gas law, the ppm concentration relation to the liquid sample volume can be calculated (Paknahad et al., 2017). Figure 2.21 Static setup. (a) An image of the microfluidic-based gas detector (right), (b) the setup used for gaseous compounds (left) 35 Figure 2.22 The setup used for the volatile compounds present in a liquid form 36 Chapter 3: Theory To model the microfluidic-based gas detector, a 3D Multiphysics model was developed simulating different phenomena including mass diffusion, surface adsorption, chemical reactions, and heat and momentum transport. In addition to modeling these phenomena, the four-step process (mentioned in the previous Chapter) involving the operation of the setup needs to be implemented in the simulation by defining and changing the boundary conditions of the microfluidic detectorâs inlet according to each step. For instance, the boundary concentration at the inlet equals to the target gas concentration at  = 0 s (exposure) and it changes to zero at  = 40 s (recovery). It has to be mentioned that the first 10 seconds of the operating procedure is not included in the simulation as it does not affect the results. The following subsections explain the theories behind the phenomena mentioned above. Although diffusion caused by the concentration gradient in the microfluidic channel is the main parameter, other phenomena (even the effect of heat transfer due to the heating element of the metal oxide semiconductor (MOS) sensor) were included to make the model comprehensive. In Chapter 4, the impact of each phenomenon on the detector response curve (âsmell-printâ) will be demonstrated. The COMSOL Multiphysics was used to solve these equations in a 3D-space geometry. The heat and momentum transfer were solved in a steady-state condition with no relation to the time points. The other equations were solved in an un-steady condition for the period of 200 seconds. 3.1 Diffusion model Diffusion is caused by the concentration gradient: the target gas diffuses inside the microfluidic channel during the exposure and diffuses back during the recovery period. It worth mentioning 37 that if the detector is exposed to a compound for a long time, the molecular transport due to diffusion ultimately seizes as the concentration gradient along the channel vanishes (Treybal, 1980). In this simulation, the Maxwell-Stefan multicomponent mass transfer equation (Eq. (3.1)) has been used.  = â1 â ,  = 1,2,3, â¦ ,  (3.1) where xi is the mole fraction, Ni is the flux, C presents the total concentration and Dij is the diffusion coefficient of component i in component j (Krishna & Wesselingh, 1997). Diffusion coefficients were obtained from the literature (Cowie & Watts, 1971; Elliott & Watts, 1972; Lugg, 1968) and also using Wilke-Lee estimation model (Wilke & Lee, 1955). 3.2 Adsorption isotherm Adsorption of adsorbate A on an adsorbent (surface) can be shown using the following chemical reaction:  +  â  (3.2)  represents the vacant sites on the absorbent, and  represents an adsorbed atom or molecule. Adsorption of a gas over a specific surface is commonly reported in the form of the amount of gas adsorbed in different pressures but at a constant temperature. This results in a curve called the adsorption isotherm. The experimental data will be compared with an isotherm model, and if they match, the model can be used for computational modeling of adsorption of a target gas over that 38 specific adsorbent. The rate of adsorption and desorption can be shown using a first-order reaction form (Eqs. (3.3)-(3.5)).   =  (3.3)   = . (3.4)    ()=  â . (3.5) where  and  are the forward (adsorption) and backward (desorption) reaction constants. The net rate of adsorption, , can be shown as (Adamson & Gast, n.d.; Fogler, 2006):  =   â. (3.6) where  = is the adsorption equilibrium constant. 3.3 MOS sensor reaction modeling Another parameter affecting the detector response is the reaction rate of the target gas with the sensing layer of the MOS sensor. This reaction can also be affected by environmental conditions such as oxygen partial pressure which changes the conductivity of the sensor through an equilibrium reaction. When a -type MOS sensor is exposed to oxygen, the forward reaction reduces sensor conductivity and the backward reaction increases it (Fine, Cavanagh, Afonja, & Binions, 2010; Kohl, 1989).  â  (3.7) [][]= [ ] (3.8)   =   39 In the case of sensor exposure to volatile organic compounds (VOCs), a series of reactions with the oxygen occurs resulting in an increase in the sensor conductivity. As an example, ethane reaction with O- can be shown as (Garton, Minton, Hu, & Schatz, n.d.; Kohl, 1989; Miyoshi, Tsuchiya, Yamauchi, & Matsui, 1994):  +  â  +  (3.9) where a second-order reaction occurring over the MOS sensor frees the electrons and hence reduces the resistance of the sensor. As the byproductsâ concentration of this reaction are much less than the ethaneâs concentration, their reactions over the sensor can be neglected. Based on this, the sensing, recovery and overall reaction can be written as: Sensing reaction: [ ] or  â  (3.10) Recovery reaction: Oxygen forward reaction â backward reaction = â [][]â  â  (3.11) And overall reaction: =  â â â [][]+  â  (3.12) where  is the target gas concentration,  is the adsorbed oxygen concentration,  is the surface molar capacity,  is the empty sites concentration (free electrons) over the sensor,  is the oxygen concentration, and , â , and  are the reaction rate constants. The oxygen concentration over the MOS surface can be assumed constant. Thus Eq. (3.12) can be rewritten as: 40 =  â â â []+  â  (3.13) 3.4 Heat transfer and momentum The heating element of the MOS sensor can create a temperature gradient between the MOS sensor housing and the inlet of the channel. To study heat and momentum transfer due to this gradient, the following unsteady-state thermal diffusivity equation is used: = â.(â) (3.14) where  is the density,  is the heat capacity,  is temperature, and  is the thermal conductivity of fluid. At the boundaries, convective heat transfer can be modeled as,  =Ì â( â ) (3.15) where  Ìis the rate of heat transfer, â is the heat transfer coefficient,  and  are the surface and fluid temperatures, respectively, and  is the surface area. For simulating the flow caused by natural convection, Navier-Stokes equations were employed. ()+ â.( Ã )= ââ. +  (3.16) where  is velocity and  is gravity (Bird, Stewart, & Lightfoot, 2007). As the concentration of the target gas is in a ppm range, we can neglect the effect of the target gas diffusion in the air inside the channel on the heat and momentum profile. Therefore, we can consider the heat and momentum equations in a steady-state condition and remove the unsteady terms from the above equations. 41 3.5 Normalization According to (Paknahad et al., 2017), different types of compounds can be distinguished by comparing their normalized smell-prints. Normalization eliminates the effect of the gas concentration and baseline variations. The following formula is used to normalize the simulation and the experimental data. ()=()() ()() (3.17) where (), min(), and max() are the normalized response at each time point, the minimum response of all time points (typically for 0 <  < 200 s), and the maximum response of all time points, respectively (Paknahad et al., 2017). 3.6 Selectivity Selectivity of the microfluidic-based gas detector presents its ability to distinguish between the responses of two different types of gases. In this study, the normalized response is employed as a unique smell-print of each type of gas; then, the following equation can be used to effectively indicate the difference between the two smell prints:  = â ( ()â ()) (3.18) where  and  are the normalized responses of gases  and , respectively, and  is the selectivity indicator. 3.7 Sensitivity Sensitivity, , of a sensor toward a gas is defined as, 42  =  (3.19) where  is the resistance of the sensor when it is in contact with the target gas (Paknahad et al., 2017). In the current study, the responseâs peak is considered as the data point for calculating sensitivity. 43 Chapter 4: Microfluidic-based gas detector simulation The comprehensive model developed here can be used to investigate the effect of various parameters such as the environmental conditions (temperature and humidity), device geometry, various mixtures, to name a few. However, this study focuses on the use of the model to investigate the effect of different phenomena on the detector response and conduct a dimensional study with the goal of identifying an optimal design. A typical concentration profile (at different time points) obtained from the model can be observed in Figure 4.1. At the beginning of each testing (Figure 4.1 (a),  = 0 s), the channel is exposed to the sample, and hence, there is a high concentration of target gas at the inlet of the channel. At  = 25 s (Figure 4.1 (b), during the exposure period), the target gas diffuses along the channel resulting in a change in the concentration profile, which causes a rather sudden increase in the amplitude of the response (see Figure 4.2). Exposure to fresh air starts at  = 40 s (Figure 4.1 (c)) when the gas concentration at the inlet becomes zero. However, the target gas is still in the process of diffusion toward both the sensor housing (causing a further increase in the amplitude of the response, see Figure 4.2) and the inlet (where it is exposed to fresh air). At  = 50 s (Figure 4.1 (e)), the diffusion direction is only from the sensor housing toward the inlet (causing a drop in the response, see Figure 4.2), which continues until there is no concentration gradient inside the channel (Figure 4.1 (f)). 44 (a) (b) (c) (d) (e) (f) Figure 4.1 Visualization of concentration profiles at the exposure and recovery steps. The results are shown at different time points, (a) 0 s, (b) 25 s, (c) 40 s, (d) 45 s, (e) 50 s, and (f) 100 s. 4.1 Verification and validation The gas concentration profiles presented in the previous section show an indirect verification of the model. To validate the model, the concentration in the middle of the MOS sensor at each time point is used to generate smell-prints for different gases. To compare the model results with those obtained in experiments, these concentration values are normalized and compared against the normalized voltage responses obtained experimentally. Figure 4.2 shows the comparison between the experimental and simulation results for methane and ethane at 700 ppm. The results are in great agreement. For volatile liquid samples (such as ethanol and methanol) and hydrogen sulfide, the model also agrees well with the normalized experimental responses (see Figure 4.3 and Figure 4.4). 45 Figure 4.2 Comparison of experimental and simulation results for methane and ethane Figure 4.3 Comparison of experimental and simulated results for methanol and ethanol 46 Figure 4.4 Comparison of experimental and simulated results for H2S at 230 ppm 4.2 Effect of different phenomena on detector response Diffusion is the main phenomenon occurring in the microfluidic channel of the proposed detector. In this section, the effect of other phenomena (including surface adsorption, chemical reactions, and heat and momentum transport) on the detector response is studied. For instance, for the heat and mass transport in the microchannel due to the heating element of the MOS sensor two scenarios were modeled: (i) heating element at 400ï° C, and (ii) heating element at room temperature. The results in Figure 4.5 show that the high temperature of the MOS heating element slightly affects the mass transfer by increasing the recovery slope, but its effect on the response shape is negligible. Figure 4.6 (a) shows that the high temperature on the MOS sensor heating element increases the temperature inside the sensor housing in the vicinity of the sensor. However, the channel is not affected and thereby there is no flow caused by convection in the channel (Figure 4.6 (b)). The heat also increases the overall mass transfer rate inside the sensor housing, which results in a more homogenous mixture. 47 In addition to the effect of the MOS sensor heating element, the effects of adsorption (along the channelâs walls) and reaction (on the surface of the MOS sensor) on the detectorâs response are studied by running the simulation without each of these phenomena at a time. Figure 4.7 shows the results of these simulations compared to the experimental results for 700 ppm ethane. The results show that without including the effect of analyte adsorption (the curves labeled as âDiffusionâ and âDiffusion + MOS reactionâ) the shape of the response is significantly affected. Since the forward reaction rate is fast, it does not affect the curve during the exposure stage. However, the slow backward reaction causes a delay in the recovery portion of the curve. Figure 4.5 Effect of the temperature of the MOS heating element on the detector response. (700 ppm of ethane) 48 (a) (b) Figure 4.6 Temperature and velocity profiles. (a) Temperature profile in the MOS sensor housing and along the channel, (b) Velocity profile caused by heat convection due to the sensor heating element Figure 4.7 Comparison of the effect of different phenomena on the detectorâs response. (700 ppm of ethane) 49 4.3 Dimensional study The proposed model is used to study the effect of different dimensions of the microfluidic-based gas detector on the response and selectivity between two gases. The dimensions chosen here are the MOS sensor housing radius, the channel length, and channel depth. For each case, the particular dimension is either increases or decrease by a factor of 2. Sensor housing radius - Figure 4.8 (a) shows the simulated detectorâs responses towards 700 ppm ethane for three dimensions of the sensor housing (i.e., the reference (original) radius, half and twice of the reference radius). Assuming the same mass flux towards the sensor housing for all three cases at the beginning of the exposure (when there is the same concentration gradient among different cases), an increase in the volume of the housing (by increasing its radius) requires more exposure time for reaching a certain concentration in the sensor housing. As a result, considering the same exposure time for all cases, the detector sensitivity decreases as the sensor housing volume increases and vice versa. It can also be seen in Figure 4.9 (a) (showing the concentration profiles for different sensor housing volumes) that during the exposure time (at  = 35 s) the concentration is slightly higher in the vicinity of the sensor housing for the geometry with the smaller radius. The effect on selectivity, however, is the opposite (Figure 4.8 (b)): the detectorâs responses towards methane and ethane are more distinguishable as the sensor housing radius increases. This is due the fact that the mass flux is not constant during the exposure or the recovery. Also, the difference between the mass fluxes of two compounds is more significant at a higher concentration gradient and their mass fluxes approach to zero as the gradient decreases (less difference). As explained earlier, as the volume of the sensor housing increases the concentration change becomes slower (higher concentration gradient during exposure), thus, the selectivity (indicated by the difference between the smell-prints of two gases) increases. The radius of the 50 sensor also significantly affects the detectorâs response/recovery time: the smaller the volume of the housing the faster the response and recovery time (due to faster diffusion); e.g. for the smallest housing volume the response recovers at 150 s which is almost 50 s faster than the other two cases. Channel length - The channel length directly affects the overall diffusion rate: the longer the channel the longer time needed for the molecules to reach to the sensor. Thus, during the 40-second exposure, the longer channel shows a smaller response and sensitivity (Figure 4.8 (c)). This can also be observed in the concentration profiles shown in Figure 4.9 (b): the longer length results in a slower mass transfer along the channel. However, selectivity of the sensor increases as the channel length increases (Figure 4.8 (d)). In addition to an increase in adsorption of the molecules to the channelâs walls, the molecules of two gases (with different diffusion rates) become more segregated as the channel length increases. These effects enhance selectivity as the length increases. The channel length seems to have an insignificant effect on the response/recovery time of the detector. The studied geometries are visualized in Figure 4.9 (b). Channel height â The channel height (changing the channelâs cross-sectional area perpendicular to the mass transport) affects the diffusion rate (Bird et al., 2007); therefore an increase in the channel height increases the response magnitude and sensitivity of the microfluidic sensor (Figure 4.8 (e)). The concentration profiles in Figure 4.9 (c) also shows smaller overall diffusion rates in the smaller channel heights. On the other hand, the effect on selectivity is the opposite (Figure 4.8 (f)). Increasing the channel height increases the transfer rate and reduces the concentration gradient which results in a smaller difference in the compounds transfer rate (diffusion rate). Thus, selectivity reduces as the height increases. Similar to the channel length, the change in the channel height has a negligible effect on the response/recovery time. In Figure 4.9 (c), the three studied geometries are visualized. 51 (a) (b) (c) (d) (e) (f) Figure 4.8 Dimensional study of the detector on the response and selectivity. (towards ethane and methane at 700 ppm) For all cases, the dimensions were increased or decreased by a factor of 2 compared to the reference dimensions. The effect of the MOS sensor housing radius on the (a) detector response, and (b) detector selectivity. The effect of the channel length on the (c) detector response, and (d) detector selectivity. The effect of the channel height on the (e) detector response, and (f) detector selectivity. 52 (a) [i] [ii] [iii] (b) [i] [ii] [iii] (c) [i] [ii] [iii] Figure 4.9 Visualization of concentration profile of different geometries at time 35s. This is studied with regard to (a) Sensor housing radius multiplied by [i] 0.5, [ii] 1, [iii] 1.5, (b) channel length multiplied by [i] 0.5, [ii] 1, [iii] 2, and (c) channel height multiplied by [i] 0.5, [ii] 1, [iii] 1.5. 53 4.4 Effect of geometry on detector sensitivity and selectivity As it was discussed earlier, there is a trade-off between detectorâs selectivity and sensitivity. However, an optimum design can be found to achieve the highest performance considering both. In this section, the effect of each geometrical parameter on both selectivity and sensitivity is investigated and compared to the reference geometry. Also, to be able to compare the sensitivity and selectivity between different cases, these values are normalized by the reference geometry selectivity and sensitivity. A sum indicator is defined based on the geometrical average of the sensitivity and selectivity values to show the overall performance of each geometry. The larger the sum indicator the higher the performance of the detector. The results in Figure 4.10 show that the change in the channel length has the most pronounced impact on the sum indicator: any deviation from the reference geometry reduces the performance of the detector (Figure 4.10 (b)); whereas for the sensor housing and channel height (Figure 4.10 (a) and (c) respectively) the performance decreases only when these dimensions are reduced from the reference geometry. To study the effect of the analyte concentration on the detectorâs sensitivity and selectivity, the normalized sensitivity/selectivity values and the sum indicators for different geometrical changes were obtained for different concentrations of ethane and methane (ranging from 100 ppm to 1000 ppm). Figure 4.11 shows that the deviation of the sum indicator (indicating the overall detectorâs performance) from its value at the reference geometry is more significant for lower analyte concentrations. 54 (a) (b) (c) Figure 4.10 Relative performance, selectivity and sensitivity of various geometries. The results are compared to the reference geometry for 700ppm of ethane and methane. (a) (b) (c) Figure 4.11 Relative performance of various geometries. The results are compared to the reference geometry for various concentrations of ethane and methane. 55 Chapter 5: Gas sensing results 5.1 Response curve analysis Since the aqueous sample concentration changes over time, a GC device was used to characterize the sample concentration for every study (Figure 5.1). Based on these results, the concentration of H2S after vaporization in the sealed chamber (~600 ml volume) is 230 ppm, which is equivalent to 176 mg/L H2S dissolved in water. In addition to the original sample, the concentrations of H2S in different diluted samples were also obtained using GC. A linear correlation between the concentration of H2S after vaporization and the aqueous samples (with different dilution volumetric values) was observed. Figure 5.1 shows that the vaporization process in the setup is efficient and can extract all of the H2S from the aqueous sample. These results are also used to label the microfluidic-based gas detector response in Figure 5.2 (obtained from the aqueous-based setup) with the concentrations of H2S. The detector response was examined across a range of different H2S concentrations using the aqueous-based setup (Figure 5.2). The detector response increases when exposed to the sample and decreases when exposed to fresh air (sensor recovery). However, due to the adsorption and desorption phenomena, a delay exists between the air exposure start time and when a drop occurs in the response. The results show that the detector response magnitude and the sample concentration are correlated. This is in agreement with the Fickâs law, as the higher concentration gradient increases the diffusion rate and leads to transfer of more molecules towards the MOS sensor, thereby increasing the detector response (Bird et al., 2007). 56 Figure 5.1 Correlation between H2S in the aqueous sample and H2S after vaporization. The vaporized sample concentrations were obtained using GC. Different concentrations of H2S in the aqueous sample were obtained by diluting the original sample with RO water. The percentage refers to the volume fraction of the original sample in the mixture (e.g., 100% means no dilution and 80% refers to 0.8 ml of the original sample diluted in 0.2 ml RO). After obtaining the detector responses, the data is normalized to remove the effect of the concentration on the magnitude of the responses. By normalizing the data, different concentrations of one compound show a similar smell-print (i.e. shape and features) which helps us to differentiate between different compounds. Examining normalized responses from the aqueous-based setup, different concentrations of H2S displayed similar smell-prints (Figure 5.2); this suggests the detector has potential for recognition of H2S in the aqueous phase. Noise is present in a few data points which can be caused by high relative humidity in the system. However, this does not change the overall shape of the responses. The same process was repeated for the gaseous-based setup and similar smell-prints for different H2S concentrations were observed (Figure 5.3 (a) and (b)). The results also show a correlation between the concentration of H2S and the magnitude of the response. In addition to the magnitude, 57 the detector responses drop below the baseline when recovering in clean air for most of the concentrations tested. For the 5-ppm sample there is a different shape during sensor recovery, possibly caused by the noise present at the lower voltage range. This indicates the detectorâs limitation when measuring H2S concentrations below 5 ppm. The difference in the smell-prints obtained from the gaseous and aqueous setups due to high humidity in the latter setup (during sample vaporization) can have a strong influence on the characteristics of the detector response. A previous study has evaluated the effect of humidity on the detector response and provided a solution to mitigate this effect (Paknahad, Bachhal, & Hoorfar, 2018). To better understand the humidity effect, a preliminary simulation was developed and ran under two conditions: (i) H2S gas in the room humidity (20%), representing the condition in the gaseous-sample testing (Figure 5.4 (a) and (b)), and (ii) H2S gas in saturated condition (100% humidity), representing the aqueous-sample testing (Figure 5.4 (c) and (d)). Although the aqueous-based setup, as expected, had a relatively higher humidity inside the detector channel during sample exposure (Figure 5.4 (a) and (c)), the simulation showed no difference in the H2S concentration profile along the channel (40 s exposure time) between these two systems (Figure 5.4 (b) and (d)). Unchanged H2S concentration profiles and an increase in relative humidity from one case to another should only affect the magnitude of the detector response obtained from the two setups. However, the results in Figure 5.2 and Figure 5.3 illustrate the response shape (smell-print) in addition to the magnitude changes when utilizing the gaseous versus aqueous setup. This observation can be explained by the simulation results (Figure 5.4 (a) and (c)): in the gaseous system the humidity is unchanged in the channel (i.e., the humidity effect is constant); whereas in the aqueous system, there is a humidity gradient resulting in a change in the humidity over the MOS sensor during the testing period. This can change the response and hence the smell-print. 58 Figure 5.2 Response of different concentrations of H2S using aqueous-based setup. a) Detector response with a removed baseline, and b) normalized sensor response. (a) (b) 59 Another possible explanation for the difference between the responses obtained from the two setups can be the lack of oxygen in the gaseous H2S sample. As mentioned previously, the gaseous H2S sample is mixed with nitrogen. When the sensor is exposed to the standard sample (100 ppm H2S, balance nitrogen) the oxygen concentration inside the microfluidic channel may drop and prevent detector recovery. The aqueous H2S sample, on the other hand, is vaporized in a chamber filled with air. Lack of oxygen, as a potential reason for the difference between the shape of the response curves obtained from the two setups, was examined by fabricating a similar condition in the aqueous-based setup. The results showed no difference between the responses obtained from the oxygen-deprived and normal air conditions for both gaseous and aqueous H2S samples. Considering this outcome and the fact that there are no other differences between the two setups except for the high relative humidity condition in the vaporization chamber of the aqueous-based setup, it can be concluded that the difference between the responses obtained from the two setups is due to humidity. To compare the detector response with regards to the H2S concentrations obtained from the two setups, a 1D feature space relating the maximum response to the concentration was used (Figure 5.5). It is evident that the voltages obtained for the gaseous samples are larger than those obtained for the aqueous samples for the same concentration. This confirms the reverse correlation between humidity and the magnitude of the detector response. 60 Figure 5.3 Response of different concentrations of H2S using gaseous-based setup. a) Detector response with a removed baseline, and b) normalized sensor response. (a) (b) 61 Figure 5.4 Simulation results of gas diffusion in the detectorâs microfluidic channel. This figure is showing the humidity and H2S concentration profiles along the channel after 40 s of exposure to 100 ppm H2S sample. (a) and (b) Simulation of the gaseous-based setup. (c) and (d) Simulation of the aqueous-based setup. Figure 5.5 Feature extraction comparison of aqueous-based and gaseous-based setups. 62 5.2 Feature space analysis To show the potential of the microfluidic-based olfaction detector in distinguishing different gases in sewer systems, the gaseous setup was used to compare smell-prints of H2S and methane. By extracting three features (F1: slope in recovery, F2: slope in exposure and F3: response at 200 s) from the sensor responses (Figure 5.6 (a) and (b)), significantly different smell-prints were observed between these two gases (Figure 5.7). These results demonstrate the potential of the detector for detecting different volatile organic compounds (VOCs) in complex mixtures, such as sewage. 63 Figure 5.6 Comparison of the smell-prints of methane and H2S. These are the two main target compounds in the sewer system. These results are obtained using the gaseous-based setup. (a) (b) 64 Figure 5.7 Comparison of H2S and methane in a 3D feature space. The features used in this graph are: F1: slope in recovery, F2: slope in exposure and F3: response at 200 s. 65 Chapter 6: Summary, contributions and future works 6.1 Summary One of the goals of this study was to demonstrate the capability of a liquid-phase H2S detection setup developed for in-situ monitoring of nuisance gases in sewer systems. For this purpose, a microfluidic-based olfaction system, detecting volatile organic compounds (VOCs), was developed by vaporizing the H2S aqueous samples. The H2S concentration in the aqueous samples was measured using GC. Also, a gaseous-based setup was developed for the measurement of H2S in standard gas mixtures. The results show that the platform can measure different concentrations of H2S dissolved in water. Although high relative humidity generated as the result of vaporization dampens the detector response, it can still be used for H2S detection in water after calibration. To further demonstrate the potential of the sensor in detecting multiple gases in the sewer lines, the system was tested for measurement and detection of H2S and methane. The results show a clear distinction between these two gases. As such, the proposed platform shows a great potential to be implemented inside the sewer system for nuisance gas hotspot detection. This will result in reduced treatment costs and environmental side effects of current chemical treatments. The current method can further be improved by implementing more sensitive types of MOS sensors and reducing the headspace volume of the testing chamber. Also, a numerical simulation of the microfluidic-based gas sensor was developed to gain an insight into the phenomena occurring in this platform. The results showed that aside from diffusion (which is the main phenomenon in the microfluidic channel of this olfaction technology) adsorption significantly affects the response of the detector. The simulation was also used to study the effect of different geometrical parameters on the detectorâs sensitivity, selectivity and response/recovery 66 time. The results showed that sensitivity and selectivity are always opposite each other. Also, the change in the channel length has the most pronounced effect on the overall detectorâs performance (obtained based on the average of the sensitivity and selectivity values). The simulation results for different concentrations of analytes also revealed that the effect of the geometrical modifications is more pronounced at the low analyte concentrations. 6.2 Contributions 1) A comprehensive numerical simulation was developed to model the effect of different phenomena occurring in microfluidic-based gas detector. The model was used to (i) obtain smell-prints toward different target gasses at different conditions, and (ii) to identify optimum dimensions. This model can be used to generate response curves of different concentrations of analytes and mixtures, which are required for training of such detectors using techniques requiring an extensive data set such as machine learning. 2) An aqueous-based setup was developed for the detection of compounds, such as H2S, in a liquid sample. The microfluidic-based gas detector was tested and calibrated against H2S; feature extraction methods were used to differentiate H2S from other compounds (such as methane) that are present in sewer systems. 6.3 Future works ï· The proposed aqueous setup needs to be tested against various conditions, such as different mixtures, temperatures, and pressures. 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Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Development of microfluidic-based olfaction system for nuisance sewer gas monitoring
Text