Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Javadian Saraf, Aida
Master of Applied Science - MASc
University of British Columbia
Over the past decade, ammonia gas sensors have received significant attention as robust platforms for many applications, including emission control, food safety, and monitoring patients' exhaled breath for the early detection of metabolic disorders. Particularly, to diagnose diseases such as dysfunction of the kidney and the liver, ammonia gas is a key biomarker present in human breath. This thesis explores the development of a microwave-based split-ring resonator (SRR) sensor with enhanced sensitivity to detect ammonia gas at low concentrations. The sensor is based on a nanocomposite fabricated by incorporating carbon-based nanomaterial into polyaniline (PANI) via the in-situ polymerization of aniline monomers. The addition of carbon-based materials, as a substrate and dopant to PANI, results in high sensitivity for low concentrations of ammonia gas, in a 150-400 s time interval at room temperature. The selectivity of the prepared sensor toward ammonia gas was tested in the presence of other higher concentrations of hazardous gases and a wide range of relative humidity levels (3 to 90%). The developed low-cost and robust sensor has the potential to monitor ammonia gas in various applications, including medical, environmental, food, and agricultural sectors. Although several studies have been carried out on the application of graphene oxide-polyaniline (GO-PANI) nanocomposites no scientific understanding of the interaction mechanism of GO-PANI and ammonia gas molecules has been investigated. Therefore, in the next step of this research, we monitored and analyzed the microwave gas sensor's response to ammonia gas to gain a better understanding of the interactions and mechanism. The time-dependent sensor response revealed that the Interactions of ammonia gas with polymer surface occurred via chemisorption or physisorption through the Langmuir adsorption mechanism. According to the designed thickness experiment, the thickness of sensitive elements affects the sensitivity of microwave gas sensors toward ammonia gas at the constant surface area, temperature, and humidity, supporting the role of absorption in the sensor's response.
MICROWAVE GAS SENSOR TECHNOLOGY: DEVELOPING GRAPHENE OXIDE-POLYANILINE NANOCOMPOSITES FOR IMPROVED SENSING AND DETECTION OF AMMONIA GAS by Aida Javadian Saraf M.Sc, Ferdowsi University of Mashhad, 2013 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) November 2021 © Aida Javadian Saraf, 2021 ii The following individuals certify that they have read, and recommend to The College of Graduate Studies for acceptance, a thesis/dissertation entitled: Microwave Gas Sensor Technology: Developing Graphene Oxide-Polyaniline Nanocomposites For Improved Sensing And Detection Of Ammonia Gas submitted by Aida Javadian Saraf in partial fulfillment of the requirements for the degree of Master of Applied Science Examining Committee: Dr. Mohammad Arjmand, School of Engineering Supervisor Dr. Mohammad Hossein Zarifi, School of Engineering Co-supervisor Dr. Jian Liu, School of Engineering Supervisory Committee Member Dr. Kevin Golovin, Faculty of Applied Science & Engineering Supervisory Committee Member Dr. Yasmine Abdin, Faculty of Applied Science University Examiner iii Abstract Over the past decade, ammonia gas sensors have received significant attention as robust platforms for many applications, including emission control, food safety, and monitoring patients' exhaled breath for the early detection of metabolic disorders. Particularly, to diagnose diseases such as dysfunction of the kidney and the liver, ammonia gas is a key biomarker present in human breath. This thesis explores the development of a microwave-based split-ring resonator (SRR) sensor with enhanced sensitivity to detect ammonia gas at low concentrations. The sensor is based on a nanocomposite fabricated by incorporating carbon-based nanomaterial into polyaniline (PANI) via the in-situ polymerization of aniline monomers. The addition of carbon-based materials, as a substrate and dopant to PANI, results in high sensitivity for low concentrations of ammonia gas, in a 150-400 s time interval at room temperature. The selectivity of the prepared sensor toward ammonia gas was tested in the presence of other higher concentrations of hazardous gases and a wide range of relative humidity levels (3 to 90%). The developed low-cost and robust sensor has the potential to monitor ammonia gas in various applications, including medical, environmental, food, and agricultural sectors. Although several studies have been carried out on the application of graphene oxide-polyaniline (GO-PANI) nanocomposites no scientific understanding of the interaction mechanism of GO-PANI and ammonia gas molecules has been investigated. Therefore, in the next step of this research, we monitored and analyzed the microwave gas sensor's response to ammonia gas to gain a better understanding of the interactions and mechanism. The time-dependent sensor response revealed that the Interactions of ammonia gas with polymer surface occurred via chemisorption or physisorption through the Langmuir adsorption mechanism. According to the designed thickness experiment, the thickness of sensitive elements affects the sensitivity of iv microwave gas sensors toward ammonia gas at the constant surface area, temperature, and humidity, supporting the role of absorption in the sensor's response. v Lay Summary Ammonia (NH3) is among the toxic, flammable gases that its concentration and duration of exposure need to be monitored in the workplace. Depending on the intended application of ammonia sensors, the proposed gas sensor needs to be able to perform at desired ammonia gas concentrations to avoid health threats and explosions. This thesis presents an innovative approach using microwave sensors for ammonia gas detection due to their compact size and low manufacturing cost, low power consumption, real-time sensing, and design flexibility. This study concludes that the integration of carbon-based nanocomposite enables microwave sensors to achieve high sensitivity, selectivity in monitoring a low concentration of ammonia gas. The proposed low-cost and robust microwave sensor has the potential to monitor ammonia gas in various applications, including medical, environmental, food, and agricultural sectors. vi Preface Surface area and porosity analysis (BET) were performed by Somi Doja (Dr. Lukas Bichler`s lab) School of Engineering, University of British Columbia, Okanagan). All chapters in the thesis are based on work conducted at the University of British Columbia in Nanomaterial and Polymer Nanocomposite Laboratory (NPNL) and Okanagan Microelectronics and Gigahertz Applications (OMEGA) Laboratory by Aida Javadian Saraf under the supervision of Dr. Mohammad Arjmand and Dr. Mohammad H. Zarifi. During the whole experiments in the thesis, I was responsible for all of the sample preparation and data analysis. My supervisors, Dr. Mohammad Arjmand and Dr. Mohammad H. Zarifi, and the committee member were mentoring and leading me through the thesis direction. A version of Chapter 4 has been published as the following paper: Aida Javadian-Saraf, Ehsan Hosseini, Benjamin Daniel Wiltshire, Mohammad H. Zarifi, Mohammad Arjmand. Graphene Oxide-Polyaniline-based Microwave Split-Ring Resonator: A Versatile Platform towards Ammonia Sensing. Journal of Hazardous Materials, 2021; 418:, 126283. I conducted all the testing and wrote most of the manuscript. Microwave resonator design was done by Benjamin Daniel Wiltshire. vii Table of Contents Abstract .................................................................................................................................... iii Lay Summary ............................................................................................................................ v Preface ...................................................................................................................................... vi Table of Contents .................................................................................................................... vii List of Tables ............................................................................................................................. x List of Figures .......................................................................................................................... xi List of Symbols ....................................................................................................................... xv List of Abbreviations ............................................................................................................ xvii Acknowledgments ............................................................................................................... xviii Dedication .............................................................................................................................. xix Chapter 1: Introduction ............................................................................................................ 1 1.1 General Background ................................................................................................... 1 1.2 Motivation ................................................................................................................... 1 1.3 Thesis Objectives ....................................................................................................... 2 1.4 Thesis Outline............................................................................................................. 4 Chapter 2: Literature Review .................................................................................................... 6 2.1 Ammonia Gas ─ what you need to know about it ........................................................ 6 2.2 Principle of gas sensing and important features .........................................................10 2.3 Working principles of microwave transducer ..............................................................12 2.4 Gas Sensing Materials ...............................................................................................16 2.4.1 Metal Oxide ...................................................................................................16 2.4.2 Carbon-based Nanomaterials ........................................................................19 2.4.3 Conducting Polymers ....................................................................................21 2.4.4 Hybrid of sensitive materials ..........................................................................24 viii 2.5 Gas sensing mechanisms of polymer nanocomposites ..............................................26 2.6 Section summary .......................................................................................................34 Chapter 3: Materials and Methods ......................................................................................... 35 3.1 Materials ....................................................................................................................35 3.2 Synthesis of graphene oxide ......................................................................................35 3.3 Synthesis of GO-PANI nanocomposites ....................................................................36 3.4 Materials characterization ..........................................................................................37 3.4.1 Spectroscopy Techniques .............................................................................37 3.4.1.1 Fourier Transform Infrared (FTIR) Spectroscopy ....................................37 3.4.1.2 Ultraviolet-visible (UV-Vis) Spectroscopy ................................................38 3.4.2 Microscopy Techniques .................................................................................40 3.4.2.1 Scanning Electron Microscopy (SEM) ....................................................40 3.4.2.2 Vector Network Analyzer (VNA) .............................................................41 3.4.2.3 Profilometer ............................................................................................42 Chapter 4: Graphene Oxide-Polyaniline-based Microwave Split-Ring Resonator .............. 44 4.1 Experimental setup and procedure ............................................................................44 4.2 Results and discussion ..............................................................................................49 4.2.1 Characterizations of GO-PANI .......................................................................49 4.2.2 Gas sensing mechanism ...............................................................................56 4.2.3 Gas sensing measurement ............................................................................59 4.3 Summary and conclusion ..........................................................................................66 Chapter 5: Gas Sensing Behavior of Graphene Oxide-Polyaniline Nanocomposite Implemented in SRR Resonator ............................................................................................. 68 5.1 Results and discussion ..............................................................................................68 5.2 Summary and conclusion ..........................................................................................78 ix Chapter 6: Conclusion and future works .............................................................................. 80 6.1 Results summary .......................................................................................................80 6.2 Future works ..............................................................................................................81 Bibliography ............................................................................................................................ 83 Appendices ............................................................................................................................. 96 Appendix A Design of split-ring resonator using in Chapter 4 ..............................................96 Appendix B Design of split-ring resonator using in Chapter 5 ..............................................98 x List of Tables Table 2.1 Summary of employing metal oxide in microwave sensors ............................19 Table 2.2 Summary of research studies employing carbon-based materials in microwave sensors ..........................................................................................................21 Table 2.3 Summary of employing hybrid in microwave sensors .....................................26 Table 4.1 Water vapor content in different concentrations of ammonia gas ...................47 Table 4.2 Variation in response in the presence of the clean and GO-PANI coated glass slide ...............................................................................................................48 Table 4.3 Fitting curve parameters of the sensor's time-based response to study stability in the adsorption and desorption cycles .........................................................63 Table 5.1 Fitting constant of GO-PANI sensor response ...............................................70 xi List of Figures Figure 2.1 Ammonia generation process in the nitrogen cycle. All rights reserved [5] ..... 7 Figure 2.2 Schematic diagram of the Haber-Bosch process, All rights reserved [24] ....... 8 Figure 2.3 Ammonia gas sensor applications in (a) food quality control, Copyright © 2021 Thermo Fisher Scientific, (b) early detection of diseases, Copyright © 2021 Nanalyze, (c) emission control, Copyright © [2017] [Eco-Logic Environmental Engineering Inc.], and (d) manufacturing, Copyright © 2020 The Royal Society. All rights reserved. .......................................................................................... 9 Figure 2.4 The electromagnetic spectrum, making emphasis on the microwave region and its different frequency bands. All rights reserved [50] .....................................12 Figure 2.5 Real and imaginary parts of permittivity over the range of frequencies. All rights reserved [8] ...................................................................................................13 Figure 2.6 A schematic of the detection principle utilizing a microwave transduction ......14 Figure 2.7 Various propagative structures used in ammonia gas sensing, (a) Antenna. All rights reserved [55], (b) disk resonators. All rights reserved [56] (c) planar microstrip ring resonator. All rights reserved [57], (d) interdigital resonator. All rights reserved [58], (e) concentric-etched square pattern RF complementary split-ring resonator, (f) meander RF complementary split-ring resonator, (g) conventional complementary split-ring resonator. All rights reserved [59]. .....15 Figure 2.8 The gas sensing mechanism of Co-doped ZnO in the presence of ammonia gas. All rights reserved [42] ...................................................................................17 Figure 2.9 Representation of the imaginary part of the sensor response as a function of the real part of the sensor response. All rights reserved [65] ...............................18 xii Figure 2.10 Schematic and TEM images of (a) multi-walled carbon nanotube (MWCNT) with different layers of 5, 2, and 7, (b) graphene oxide nanosheets and (c) reduced graphene nanosheets. All rights reserved [70-72] ..........................................20 Figure 2.11 Some of the common conducting polymers ...................................................22 Figure 2.12 PANI interconversion forms [84] ....................................................................24 Figure 2.13 Schematic of adsorption and absorption process [90] ....................................27 Figure 2.14 Schematic of Langmuir adsorption model ......................................................29 Figure 2.15 Schematic plot of Langmuir and Freundlich adsorption isotherms [90] ..........31 Figure 2.16 Schematic of gas sensing film on SRR ..........................................................33 Figure 3.1 Graphene oxide synthesis through modified Hummers’ Method ....................36 Figure 3.2 In-situ Polymerization through Chemical Oxidative Process ..........................37 Figure 3.3 Graphic demonstration of an ATR-FTIR. All rights reserved [128] .................38 Figure 3.4 Illustration of a double beam UV-vis spectroscopy instrument. All rights reserved [133] ..............................................................................................................39 Figure 3.5 Schematic of Scanning Electron Microscopy (SEM). All rights reserved [134] .. ......................................................................................................................40 Figure 3.6 (a) and (b) The basic concept of a vector network analyzer ..........................42 Figure 3.7 DektakXT Stylus profilometer used for thickness measurement ....................43 Figure 4.1 The schematic of the microwave resonator preparation steps .......................45 Figure 4.2 The experimental gas sensing setup .............................................................46 Figure 4.3 (a) The drop-casted GO-PANI suspension onto the ring gap of the SRR, and (b) dried suspension at room temperature after 24 h. (c) Response of the developed SRR before and after drop-casting of PANI and GO-PANI. ...........................48 Figure 4.4 FTIR spectra of GO, and GO-PANI nanocomposites in different GO weight ratios .............................................................................................................50 xiii Figure 4.5 UV-Vis adsorption of PANI and GO-PANI in different GO weight ratios .........51 Figure 4.6 SEM images of (a) GO, (b) PANI, and (c, d) GO-PANI nanocomposites .......52 Figure 4.7 Transformation of (a) PANI’s coil structure to (b) the expanded coil when pinned to the GO sheets ...........................................................................................53 Figure 4.8 Thermogravimetry graphs of GO and GO (10%)-PANI ..................................54 Figure 4.9 (a) BET nitrogen adsorption-desorption isotherms, and (b) BJH pore size distributions for PANI and GO (10%)-PANI samples ......................................55 Figure 4.10 Change in S21 response of the microwave sensor coated with GO (10%)-PANI before and after exposure to 150 ppm ammonia gas .....................................56 Figure 4.11 The mechanisms of GO-PANI exposure to ammonia gas where doping and de-doping of PANI occurred ................................................................................57 Figure 4.12 Microwave response of pure PANI and GO-PANI nanocomposites with different GO weight ratios when exposed to 50 ppm ammonia gas .............................58 Figure 4.13 The sensor measuring resonant amplitude in different concentrations of ammonia gas; (a) 20—250 ppm, (b) 1-20 ppm ..............................................59 Figure 4.14 The resonant amplitude variations of the GO (10%)-PANI sensor for low (a) and high (b) concentrations of ammonia gas ........................................................60 Figure 4.15 The response time of the sensor for different ammonia gas concentrations ...61 Figure 4.16 The investigation on the repeatability and stability of the GO (10%)-PANI sensor ......................................................................................................................62 Figure 4.17 The selectivity comparison of the GO (10%)-PANI sensor when exposed to different gases in separate experiments ........................................................64 Figure 4.18 a) sensor behavior in the various relative humidity (0-100%), and (b) The measured temperatures at which the relative humidity values were set .........65 xiv Figure 4.19 Effect of different relative humidity on the sensor`s response in the presence of 25 ppm ammonia gas ....................................................................................66 Figure 5.1 The sensor response to different concentrations of ammonia gas. ................70 Figure 5.2 Correlation of time constants vs. NH3 gas concentrations in GO-PANI nanocomposite ..............................................................................................71 Figure 5.3 Plotting the fitting parameter as a function of ammonia concentration fits Langmuir and Freundlich ...............................................................................72 Figure 5.4 Drop casting GO-PANI on the surface of the glass slide. The area of GO-PANI film is a surface of 5 mm x 5 mm ...................................................................73 Figure 5.5 The schematic of the microwave resonator preparation steps .......................74 Figure 5.6 Experimental results comparing the response of the resonator when GO-PANI on a glass slide lies on the ring gap of SRR ...................................................75 Figure 5.7 (a) and (b) Response of GO-PANI toward ammonia gas in different thicknesses ......................................................................................................................77 Figure 5.8 A schematic illustrating how extending a surface of sensing film away from the transmission line affects the fringing E-field waves` intensity .........................78 Figure A.1 The measured topological values of the designed microwave split-ring resonant...........................................................................................................96 Figure A.2 (a) the simulated and experimentally measured S21 response of the bare resonator, (b) HFSS simulations of electric field intensity of the bare resonator..........................................................................................................97 Figure B.1 Top view of the microstrip ring resonator structure modeled in HFSS....................98 Figure B.2 Examine different inner and outer ring gap sizes on electric field distribution on the resonator model simulated using HFSS................................................................................................................99 Figure B.3 Simulation and measurement results for S21 response of the bare resonator……..100 xv List of Symbols RA Resonant amplitude f Electromagnetic wave frequency S11 Ratio of reflected power to incident power in port 1 S12 Ratio of transmitted power from port 1 to port 2 to incident power in port 1 S21 Ratio of transmitted power from port 2 to port 1 to incident power in port 1 S22 Ratio of transmitted power from port 2 to port 1 to incident power in port 1 ε Complex permittivity ε′ Dielectric constant ε" Dielectric loss τ Time constant θads Amount of adsorbed gas. θmax Maximum surface coverage DK Knudsen diffusion constant r Pore radius M Molecular weight R Gas constant CA Concentration of target gas x Distance from the top of the sensing film k Rate constant KF Freundlich constant xvi KL Langmuir constant A Absorbance c Concentration ε Molar absorptivity coefficient l Light penetration length through the sample xvii List of Abbreviations NIOSH National Institute for Occupational Safety and Health IOT Internet of Things WHO World Health Organization IEEE Institute of Electrical and Electronics Engineers VOC Volatile organic compound ICPs Conducting polymers PANI Polyaniline PEDOT: PSS Poly (3,4- ethylene dioxythiophene) polystyrene sulfonate PABS Poly (m-aminobenzene sulfonic acid) PDDAC Poly-(diallyl dimethyl ammonium chloride GO Graphene oxide rGO Reduced graphene oxide P-rGO Phosphate-functionalized reduced graphene oxide CNTs Carbon nanotubes MWCNT Multi-walled carbon nanotube Ag-ink Nanosilver ink ATR-FTIR Attenuated Total Reflection Fourier-Transform Infrared UV-Vis Ultraviolet-Visible SEM Scanning Electron Microscopy VNA Vector Network Analyzer DUT Device Under Test HFSS Ansys High-Frequency Structure Simulator xviii Acknowledgments I would like to thank the Syilx Okanagan Nation for the use of their unceded territory, the land on which the research was conducted. I also acknowledge the Department of National Defense and UBC Okanagan for providing financial support. I would like to express my gratitude towards my supervisors, Dr. Mohammad Arjmand and, Dr. Mohammad Hossein Zarifi, for their support and guidance throughout this challenging process. I value the help you've given me. I am thankful to Dr. Jian Liu and Dr. Kevin Golovin for their wonderful guidance throughout the master’s program. I would also like to thank all my labmates in NPNL and OMEGA labs for their support and for making my research days memorable. I am so appreciative to have the supportive, and caring family that anyone could ever wish for. Your support gives me the strength to weather any storm that comes my way. My gratitude to you is infinite and endless. xix Dedication To Ehsan For all his love and support along the way 1 Chapter 1: Introduction 1.1 General Background A gas sensor is a sensing device that monitors quantitatively the presence and concentration of hazardous gases, volatile organic compounds (VOCs), humidity, and odors [1]. Gas sensing technology has become a part of our everyday life. In homes, it detects the level of carbon monoxide (CO) to prevent CO poisoning; in the workplace, it measures the levels of toxic gases and odors to avoid health issues and property damage; and in hospitals, it measures the levels of respiratory gases in exhaled breath for early detection of diseases [2]. Ammonia (NH3) is among the toxic, flammable gases that its concentration and duration of exposure need to be monitored in workplaces. According to the National Institute for Occupational Safety and Health (NIOSH), exposure to more than 300 ppm concentration of ammonia gas can be immediately fatal and the highest recommended exposure limit to ammonia gas is 25 ppm for an eight-hour period [4]. Moreover, ammonia gas concentration is a health index for two critical organs, i.e., kidney and liver [3, 4]. Previous clinical research studies have established that ammonia gas concentrations beyond 1.5 ppm in exhaled breath are considered a sign of kidney and liver diseases [5, 6]. Therefore, depending on the intended application of ammonia sensors, the proposed gas sensor needs to be able to perform at desired ammonia gas concentrations to avoid health threats and explosions. 1.2 Motivation As mentioned before, due to the high-risk level of ammonia gas and the fact that the concentration of ammonia gas in exhaled breath is considered to be a human health index, there is a great interest in developing ammonia gas sensors. The most common technologies for detecting ammonia gas rely on optical sensors and electrochemical sensors. Although these techniques are reliable, these techniques suffer from some drawbacks. To mention a few, most 2 optical sensors are based on spectroscopy, which is limited by its high operating and maintenance costs, making them inaccessible for remote sensing and point-of-care device applications. Additionally, electrochemical sensors are susceptible to leakage and temperature fluctuations, declining the lifespan of sensors. Microwave sensors have recently gained attention as a promising tool for several applications, including gas detection [7, 8], liquids characterization [9], ice detection [10], light and UV detection [10], and wearable RFID and biomedical technology [11], due to their compact size and low manufacturing cost, low power consumption, real-time sensing, and design flexibility [12]. Despite all the promising features of microwave sensors, increasing the sensitivity of microwave sensors should be considered. To improve the capability of microwave sensors to detect ammonia gas at low concentrations, they need to be integrated with sensitive materials. This combination opens up new possibilities in real-time ammonia gas sensing and its integration with the internet of things (IOT) [13-15]. To date, little attention has been paid to incorporating sensitive materials into the structure of microwave sensors, which indicates a necessity to understand different factors that affect microwave gas sensing capabilities. This study aims to contribute to this growing field of research by developing and integrating sensitive materials on microwave sensor technology toward ammonia gas that can improve efficiency or eliminate the drawbacks of the earlier proposed microwave gas sensors. 1.3 Thesis Objectives The main objective of this thesis is to develop a microwave-based ammonia gas sensor with enhanced sensitivity and selectivity and a low detection limit at room temperature. The following sub-objectives are considered to be fulfilled: 3 Sub-objective 1: Control the morphology of the polyaniline (PANI) to create the required conductive network for ammonia gas sensing. Hypothesis: The addition of graphene oxide (GO) to PANI renders a multifunctional template featuring PANI with engineered conformation, i.e., higher alignment and ordered structure. Tasks Performed: ❖ Employ an eco-friendly procedure to synthesize PANI as a conducting polymer and GO as a template and dopant. ❖ Modify the procedure of synthesizing GO-PANI nanocomposites to achieve a homogenous distribution of polymer chains on GO surfaces in order to increase sensitivity. ❖ Characterize properties of PANI and GO-PANI nanocomposites. Sub-objective 2: Development of microwave-based sensors offering a high sensitivity towards ammonia gas. Hypothesis: By applying GO-PANI nanocomposites on the surface of microwave sensors, we can monitor low concetrations of ammonia gas in the atmosphere. Tasks Performed: ❖ Design a gas-sensing measurement setup to analyze the response of the sensor for the detection of ammonia gas at a constant room temperature. ❖ Analyze the data and plot the measured results. Evaluate the impact of GO on the response of the sensor towards ammonia gas. 4 ❖ Assess the selectivity and performance of the proposed sensor in the presence of high concentrations of other VOCs and different humidity levels. Sub-objective 3: Investigation of the interactions between the GO-PANI nanocomposite with ammonia gas molecules during the detection performance of the microwave sensor. Hypothesis: Adsorption isotherm models and studying effect of thickness can be used to improve the detection performance of microwave sensors. Tasks Performed: ❖ Analyze the time-based response of microwave sensors toward ammonia gas. ❖ Produce the best-fitting curve to sensor data in order to understand the interaction between gas molecules and the surface. ❖ Evaluate the effect of thickness on the gas sensing performance of a microwave sensor to understand the role of absorption in response of a microwave sensor. 1.4 Thesis Outline This thesis is focused on using polyaniline, a conductive polymer, and graphene oxide as a carbon-based nanomaterial for ammonia gas sensing. To fulfill this, the following chapter is compiled with an introduction providing background on gas sensing fundamentals, technologies, and applications. Also, Chapter 2 encompasses a literature review on microwave sensor principles in addition to the state-of-the-art materials and technologies that are applied to microwave gas sensing and their associated pros and cons. Chapter 3 covers the methodology and experimental procedures used for this study, including synthesis methods, characterization techniques, and gas sensing measurement technique for a microwave-based SRR sensor. In Chapter 4, the design and fabrication of a microwave split-ring resonator using GO-PANI 5 nanocomposite as an ammonia gas sensor are discussed in detail. Chapter 5 provides a detailed understanding of the gas sensing mechanism inside the implemented GO-PANI nanocomposite in the fabricated SRR. Furthermore, the role of the film thickness of GO-PANI on the performance of the sensor is analyzed. Finally, in Chapter 6, the achievements of the current research work and the obtained results are summarized, and new case studies are proposed for future work. 6 Chapter 2: Literature Review This chapter aims to review the literature on gas sensors and understand their structure and important features. It is followed by describing the working principle of the microwave gas sensor. As this thesis focuses on microwave sensors, the literature review is followed by a description of a range of sensing elements used for detecting ammonia gas in microwave sensors and a review of the operation principle of these elements. The last section of Chapter 2 presents a brief overview of the adsorption mechanism on polymer composites. 2.1 Ammonia Gas ─ what you need to know about it Ammonia gas (NH3) is one of the most hazardous gases in industrial applications and an environmental pollutant with a sharp odor, which is flammable at volumetric concentrations of approximately 15% to 28% in the air [16]. Ammonia is produced from several sources. First, it is a natural breakdown called ammonification, defined as the decomposition of organic compounds (including proteins or amino acids in manure, plants, and animal waste) into inorganic materials such as ammonia, which move back into the ecosystem [17]. This natural way of ammonia production in the nitrogen cycle is carried out by bacteria and fungi species in the soil. Second, species called nitrogen-fixing bacteria also enable converting nitrogen from the atmosphere to ammonia in the nitrogen fixation process. Since ammonia forms in nature regularly, human beings are exposed to the range of ppb concentrations of ammonia in the air, soil, and water (Figure 2.1) [18]. Another source of ammonia comes from fossil fuel combustion. Coal gasification plants and all fuel vehicles, especially those with a three-way catalytic converter [19] and those equipped with NOx reduction technologies, produce a significant amount of ammonia in municipalities [20]. 7 Particulate matter formation happens in urban areas when ammonia emissions react with nitrogen oxides and sulfur dioxide, leading to decreased visibility and severely affecting human health [21]. Figure 2.1 Ammonia generation process in the nitrogen cycle. All rights reserved [5] Industrial ammonia production is one of the largest chemical industries in energy consumption and carbon footprint in the world. A Haber-Bosch process is almost exclusively used to produce ammonia due to its energy efficiency and capacity to scale up (Figure 2.2). Each year, almost 80% of produced ammonia uses to synthesize the nitrogen-containing fertilizer [22]. Additionally, ammonia is used as a refrigerant gas, in the purification of water supplies, in the production of plastics, explosives, textiles, pesticides, dyes, and cleaning chemicals [23]. The following section is a brief overview of ammonia gas sensors in some applications, showing how ammonia gas sensors can improve these sectors. 8 Figure 2.2 Schematic diagram of the Haber-Bosch process, All rights reserved [24] Food Quality Control. The lack of safe food availability has been one of the leading causes of more than 200 diseases, from diarrhea to cancers [25]. Based on the World Health Organization’s (WHO) report, in April 2020, annually, almost one-tenth of the US population (33 million) lost their healthy life because of consuming contaminated food with bacteria, viruses, parasites, and chemical substances, with 420,000 deaths every year [25]. In Canada, one-eighth of Canadians suffer from foodborne diseases, resulting in 11,600 hospitalizations and 238 deaths annually [26]. Changes in the concentration of the gaseous compounds caused by metabolism or microbial growth can be utilized to check the freshness or decomposition of foods [27]. Sensors can act as a reliable indicator to estimate the expiry time and prevent the consumption of contaminated foods. Among different biomarkers of spoilage, ammonia gas is also released from spoiled protein in meat (Figure 2.3a). At the initial level of degradation, 5-8 ppm ammonia gas is generated, whereas spoiled meat releases 17-186 ppm [28]. Therefore, cost-effective, fast, reliable food quality indicators can contribute to preventing foodborne diseases resulting from harmful gases. Early Detection of Diseases. Health care systems have paid particular attention to early disease diagnosis to render a higher chance of effective treatment, lower cost of care, and 9 reduced morbidity [29]. One of the recognized methods of early detection is analyzing the biomarkers in human exhaled breath. Metabolic processes in the human body are associated with a concentration of volatile compounds in exhaled breath [30]. Among different biomarkers, ammonia gas concentration is a significant health index for two critical organs, i.e., the kidney and the liver (Figure 2.3b) [3]. Ammonia concentrations greater than 1.5 ppm in breath are considered signs of kidney and liver diseases in clinical studies. [5, 31, 32]. This fact has triggered abundant research studies to design sensitive, selective, cost-effective, and non-invasive measurement tools to perform at such low ammonia gas concentrations [4, 33, 34]. Figure 2.3 Ammonia gas sensor applications in (a) food quality control, Copyright © 2021 Thermo Fisher Scientific, (b) early detection of diseases, Copyright © 2021 Nanalyze, (c) emission control, Copyright © [2017] [Eco-Logic Environmental Engineering Inc.], and (d) manufacturing, Copyright © 2020 The Royal Society. All rights reserved. (c) (d) (b) (a) (c) (d) 10 Emission Control. The source of nitrogen pollution from agriculture and fossil fuels is based on the NH3 and NH4+ (ammonium) compounds. Ammonium is produced from the reaction of ammonia gas and acidic gas such as sulfuric acid (H2SO4) and nitric acid (H2NO3). Converting ammonia to ammonium increases the lifetime of the nitrogen pollutants from a few hours to 15 days, allowing pollution to move through greater distances [35]. Nitrogen pollution can deposit in ecosystems, resulting in eutrophication of water surfaces, preventing biodiversity, and increasing acidity of the soil [36]. Due to the risks and hazards of excess nitrogen loading in the ecosystem, a reliable ammonia sensing system is of considerable significance to avoid health threats within the environment (Figure 2.3c). Manufacturing. Ammonia gas is highly flammable, which poses a dramatic explosion risk and health threat to chemical industries, like agricultural fertilizer manufacturers. Consequently, a temperature-resistant gas sensor is urgently needed to precisely detect an ammonia leakage above 20 ppm in high working temperature (Figure 2.3d) [8]. 2.2 Principle of gas sensing and important features It is now well established from the previous section that ammonia gas sensors are an increasingly important area in the environment and health sectors. It is now necessary to explain the structure of the gas sensors. Generally, a gas sensor's structure comprises two parts: (i) the sensitive element and (ii) the transducer element. The sensitive element includes material or mixture of materials, that interacts with the gas of interest physically or chemically, leading to a change in a particular property of the material and thus resulting in altering a signal in the sensing system. Then, the transducer converts these signal changes to quantifiable numbers related to gas concentration [37]. The main challenge is fabricating gas sensors with high selectivity, sensitivity, rapid response, and stability under high humidity conditions [38]. Some of the most critical parameters for gas sensors are listed below: 11 Sensitivity is one of the important features of a gas sensor which is determined by the slope of the calibration curve. Thus, If the gas sensor has a high sensitivity, even a small concentration of gas will produce a noticeable difference in the response of the sensor. The use of materials with a high surface-to-volume ratio results in increased sensitivity through enhancing the interfaces between the analytes and the sensitive element [39]. Moreover, surface modifications can increase the number or reactivity of the active centers on the surface of the sensitive element, which significantly alters the sensitivity of the sensor [40, 41]. Selectivity of a sensor is one of the challenging parameters of sensors. Selectivity has been used to describe the tendency of a material to detect a target gas, specifically in a gas mixture. Selectivity of gas sensors has an essential role in reducing false alarms in many industries [42]. To improve the selectivity of gas sensors, there is a need to understand how to manipulate the properties of a material to respond specifically to a target gas. Selectivity can be improved by doping sensitive elements with catalyst materials [43] and employing sensor arrays [44]. Stability. Chemically and thermally stable gas sensors over a period of time (at least 17,000 h–26,000 h of operation) are essential for a wide range of scientific and industrial applications [45]. Pieces of evidence suggest that low working temperatures help achieve a durable gas sensor [46]. Resolution is determined by the smallest amount of gas that can cause a change in the property of the sensitive element in the gas sensor which is detectable via a transducer. A higher sensor resolution can be achieved by reducing the noise and improving sensitivity [47]. Besides the importance of the above parameters, cost-effectiveness and easy processing in routine sampling remain significant concerns for commercial gas sensors [48]. 12 To date, several transducers have been carried out to detect ammonia gas. In this thesis, we are working on a microwave transducer due to its simplicity, cost-effectiveness, and passive operations, which makes microwave sensors a promising candidate for ammonia gas sensing. 2.3 Working principles of microwave transducer A microwave is a wide frequency band (300 MHz up to 300 GHz) in the electromagnetic spectrum suitable for communication in remote sensing applications, shown in Figure 2.4. Based on the Institute of Electrical and Electronics Engineers (IEEE), microwave regions are designated to different frequency bands suitable for various applications ranging from paging and low-speed data transmission to high-speed data and video transmission [49]. Figure 2.4 The electromagnetic spectrum, making emphasis on the microwave region and its different frequency bands. All rights reserved [50] Microwave transducer is capable of characterizing and sensing materials by measuring their dielectric properties such as permittivity and conductivity. In the presence of an external electric field, atoms, and molecules of a dielectric material form dipoles along the direction of the applied 13 electric field. Dielectric permittivity is a dimensionless quantity that describes how effectively the dipoles are aligned in the direction of the electric field. This polarization comes from a number of different sources, including electric, atomic, dipolar, space charge, and ionic which occurs at a different frequency (Figure 2.5). Figure 2.5 Real and imaginary parts of permittivity over the range of frequencies. All rights reserved [8] According to equation 2.1, the complex permittivity (𝜀 ) of materials consist of real part known as dielectric constant (𝜀′) and imaginary part known as a dielectric loss (𝜀") which is described as follow: 𝜀 = 𝜀′ + 𝑖𝜀" (2.1) The dielectric constant is related to the amount of energy stored in a material, while the imaginary part is related to the conductivity of a material. Both parts of complex permittivity change 14 with the temperature and frequency of the electric field. Additionally, permittivity depends on the material's composition and lattice flexibility. Microwave sensors need to be integrated with sensitive materials in order to be used as an effective gas sensor microwave transducers, responding to the propagation and reflection of incident electromagnetic waves through sensitive elements. While the sensor is exposed to a gas of interest, the dielectric properties of the sensing layer vary due to interaction with gas molecules, resulting in a variation of the permittivity of material under the test as a function of frequency [51]. At each frequency, a Vector Network Analyzer (VNA) compares the incident wave with the reflected/transmitted wave, which is attenuated and out of phase, to generate a microwave signal (Figure 2.6). Figure 2.6 A schematic of the detection principle utilizing a microwave transduction Variation in microwave signal shows specific interactions between the gas molecules and the sensitive material, and its magnitude corresponds to the gas concentration [52]. Propagation structures adapted to the microwave range are necessary to transmit electromagnetic waves through the sensitive element. Various geometries of the propagative structure, including microstrip line, coplanar waveguide, coaxial line, resonant cavity, and split ring resonator, have been examined in microwave gas sensors (see Figure 2.7) [8, 51]. Depending on how electromagnetic waves propagate, the propagation structure can be categorized as a non-resonator or resonator. Non-resonators have a simple structure and provide 15 a broad bandwidth, whereas resonators which stores energy at the resonant frequency, have narrower bandwidths and stable, resonant frequencies during the measurement, thus providing higher resolution, accuracy, and sensitivity [53]. Figure 2.7 Various propagative structures used in ammonia gas sensing, (a) Antenna. All rights reserved [55], (b) disk resonators. All rights reserved [56] (c) planar microstrip ring resonator. All rights reserved [57], (d) interdigital resonator. All rights reserved [58], (e) concentric-etched square pattern RF complementary split-ring resonator, (f) meander RF complementary split-ring resonator, (g) conventional complementary split-ring resonator. All rights reserved [59]. Microwave sensors provide several advantages through gas sensing measurements. First, working at ambient temperature leads to low energy consumption and low cost. Second, variation of the dielectric properties of the sensing material as a function of frequency enables the (a) (b) (c) (d) (e) (f) (g) 16 microwave sensors to function at a wide range of frequencies suitable for various applications [54]. Furthermore, microwave sensors can be employed in wireless and non-contact modes, suitable for some medical and industrial applications. 2.4 Gas Sensing Materials As mentioned in Section 2.2, two effective characteristics of the sensitive element are the large surface area and the potential for amplifying the reactivity of active centers. Nanomaterials — as a class of materials with the ability to achieve these characteristics — are widely employed in gas sensors to maximize sensitivity and selectivity and minimize power consumption. Nanomaterial-based sensors have indicated a huge potential to decrease response time and production costs [60]. Following is a summary of the most common nanostructured materials that have been applied in microwave gas sensors to date. These nanostructures are metal oxides, carbon-based nanomaterials, and conducting polymers. 2.4.1 Metal Oxide Metal oxides, as semiconductors, are widely used in ammonia gas sensing due to their cost-effectiveness, ease of fabrication, and compatibility [61, 62]. They offer good electrical properties at elevated temperatures [63]. The gas sensing mechanism of metal oxides, as shown in Figure 2.8, contains a two-step redox reaction: (1) the interaction of oxygen molecules in air with the surface of metal oxides to produce electrons (anions), reducing the conductivity of the sensing layer, (2) ammonia gas reacts with negative charge species, resulting in increased conductivity of the sensing layer. These variations are transduced to resistance variations and correspond to target gas concentration [64]. O2(Atmosphere) + e-(Metal Oxide surface) → O2-(Metal oxide surface) (1) 17 4NH3 + 3O2- (ad) → 2N2 + 6H2O + 6e- (2) Figure 2.8 The gas sensing mechanism of Co-doped ZnO in the presence of ammonia gas. All rights reserved [42] The main limitations of the metal oxides are the lack of selectivity and repeatability, high detection limit, and short lifetime due to high working temperature [61]. However, doping during synthesis and the deposition process has been suggested to improve selectivity, response time, and sensor stability of metal oxides [42, 63]. To address metal oxide's high working temperature issue, one solution would be gas sensing at room temperature using microwave sensors. Few studies have explored the incorporation of metal oxides in microwave sensors for ammonia gas sensing. In microwave sensors, metal oxides can be used since their permittivity changes when exposed to ammonia gas due to the generation of electrons on their surface to fill the conduction band, which results in a change in the signal received by the microwave sensor [8]. Among available studies, Bailly et al. examined hematite (α-Fe2O3) on the coplanar waveguide for ammonia gas sensing at room temperature. In this study, relatively high ammonia gas concentrations between 100 and 500 ppm were successfully measured; however, no data were reported for response time or selectivity tests [65]. 18 To manipulate the characteristics of metal oxide nanoparticles, one well-established method is changing the morphology of nanomaterials. Based on the literature, a variety of morphologies ranging from nanorods, nanospheres, nanoflowers, nanoflakes, nanoplatelets, and nanobelts have been used to increase the surface area of metal oxide for gas sensing [42, 62, 66]. Bailly et al. also investigated the morphology effect on microwave gas sensing using spindles, rhombohedra, and pseudocubic structures of hematite [65]. As shown in Figure 2.9, they observed that the sensor's behavior demonstrated a significant difference between various morphologies. The high crystallinity and large surface area provide a promising platform for active sites to interact with gas molecules [65, 67]. Another metal oxide that is used in microwave sensors is titanium oxides (TiO2). Bailly et al. examined this sensing material on a microstrip capacitor [68] and trapezoidal spiral resonator [54]. Comparing these microwave propagation structures, they observed that the resonator had a higher sensitivity to detect lower concentrations of ammonia gas due to narrower bandwidths and more stability in measurements. Figure 2.9 Representation of the imaginary part of the sensor response as a function of the real part of the sensor response. All rights reserved [65] 19 A summary of available works on metal oxides in microwave ammonia gas sensors is presented in Table 2.1. Table 2.1 Summary of employing metal oxide in microwave sensors Sensor structure Sensing element Sensing range (ppm) Response time Selectivity testing Ref. Conductor backed coplanar waveguide α-Fe2O3 100-500 - No [65] Microstrip interdigital capacitor TiO2 100-500 25 s No [68] Trapezoidal spiral resonator TiO2 50-300 Less than 1 min No [54] As mentioned in Section 1.1, gas concentrations lower than 25 ppm must be detected in health and safety applications. Thus, metal oxide-based microwave sensors still require extensive research to create high-performance, selective, and reliable gas sensors. 2.4.2 Carbon-based Nanomaterials Carbon-based nanomaterials, including carbon nanotubes (CNTs), graphene oxide (GO), and reduced graphene oxide (rGO) (see Figure 2.10), has attracted tremendous interest in toxic gas sensors because of their large surface areas and unique electrical properties [69]. 20 Figure 2.10 Schematic and TEM images of (a) multi-walled carbon nanotube (MWCNT) with different layers of 5, 2, and 7, (b) graphene oxide nanosheets and (c) reduced graphene nanosheets. All rights reserved [70-72] Although these nanomaterials have specific characteristics to enhance gas sensing performance, their gas detection mechanisms are almost similar. By exposing these nanomaterials toward ammonia, their resistance increases through the reduction of charge carriers. The performance of the carbon-based gas sensors can also be engineered by generating surface functional groups or defects in a controlled fashion to enhance their sensitivity and selectivity. Moreover, the mechanical properties of these nanomaterials make them suitable for flexible and reusable sensors to lower the cost of preparation [73]. A literature search revealed that a few studies used carbon-based materials as a sensing layer at microwave sensors. Carbon nanotube (CNT) is mostly used at different propagative structures. Chopra et al. examined CNT on a microstrip circular disk resonator to sense ammonia gas. They reported acceptable response times over a narrow range of concentrations [74]. Pardue et al. and Occhiuzzi et al. used CNT on RFID / antenna and triple complementary split-ring resonator, 21 respectively [75, 76]. However, they did not report a wide range of ammonia gas concentrations and selectivity. In microwave sensors, the amplitude of resonant frequency changes due to conductivity variations, while frequency shift shows permittivity change. The results indicated that exposing CNT to ammonia causes a permittivity variation [8]. Table 2.2 summarizes a few available studies on carbon-based microwave ammonia gas sensors. Table 2.2 Summary of research studies employing carbon-based materials in microwave sensors Sensor structure Sensing element Sensing range (ppm) Response time Selectivity testing Ref. Microstrip circular disk resonator CNT 834 10 min No [74] RFID / antenna CNT - 90 s No [75] Triple complementary split-ring resonator CNT Low/High - No [76] Waveguide cavity resonator Graphene - 12-16 min No [77] 2.4.3 Conducting Polymers Intrinsically conducting polymers (ICPs), which have been widely introduced as a substitution for metals, are a class of organic substances with a conjugated structure that shows both metals’ and semiconductors' electrical and optical properties [78]. The delocalized π-system in ICPs, generated from p-orbitals of the double bond, provides the path for charge carriers' movement. A higher conductivity is achieved by forming long sequences of conjugated chains, which decreases the band gap between the highest occupied and lowest unoccupied molecular orbitals [79]. 22 ICPs are involved in many applications because of their low cost, ease of synthesis, and tunable conductivity. In the synthesis of ICPs, morphological control enhances polymer's processability, whereas doping changes their electrical conductivity through an increased number of charge carriers in the polymer backbone [48, 80]. In the case of using microwave transduction, Kudpun et al. investigated the influence of using poly (3,4- ethylene dioxythiophene) polystyrene sulfonate (PEDOT:PSS) as a sensing layer. They observed the high capability of the prepared sensor in sensing a wide range of ammonia gas concentrations from 50-2000 ppm [58]. More than 25 different ICPs (Figure 2.11) have been applied in sensing research studies [81], among which PANI is one of the most powerful platforms for ammonia gas detection owing to its easy synthesis, doping process, and unique interaction with ammonia gas molecules [82]. As a result, in this thesis, we have chosen to employ PANI to design ammonia sensors. Figure 2.11 Some of the common conducting polymers 23 With respect to PANI synthesis, chemical oxidative polymerization is one of the common synthesis methods. Polymerization is initiated by reacting an oxidating agent with aniline molecules in an acidic solution at temperatures close to 0°C. PANI polymerization requires large amounts of oxidizing agents to proceed with polymerization until PANI formation is reached. During the polymerization, different oxidation states of PANI can be synthesized, ranging from fully oxidated to fully reduced forms. Pernigraniline salt is the fully oxidized form of PANI synthesized through controlled oxidation. Despite showing higher conductivity than the other types of PANI, this form is unstable at ambient temperature [83]. Under basic treatment, Pernigraniline salt can be converted to a nonconductive violet pernigraniline base (Figure 2.12) [84]. Emeraldine, as a partially oxidized form, has been attracting a lot of interest due to its stability and conversion ability through a conductive and non-conductive form. This conversion takes place in the doping process in an acidic medium. During the doping process, charges transfer from acid anion to PANI, resulting in the enhancement of charge carriers (polarons) within the polymer structure. The more the polarons are generated in the PANI backbone, the higher the conductivity is achieved [48, 112]. For the oxidative polymerization of PANI, employing different hard templates, such as anodic aluminum oxide [113], cuprous oxide [114], and polymer fiber [115], or soft templates, such as block copolymer [116], DNA [117], and sodium dodecyl sulfate [118], has been an efficient method to synthesize PANI with higher dimensional control, conductivity, electrochemical capacitance, and stability compared to the conventional PANI synthesis. However, the hard template must be removed through a post-process, which may destroy nanomaterial structures or introduce impurities to them [117]. On the other hand, using a soft template cannot control the morphology and diameter of nanomaterials compared to the hard templates [119]. 24 Figure 2.12 PANI interconversion forms [84] Despite the fact that PANI is a promising candidate for ammonia gas sensing, PANI is not able to detect ammonia gas in a broad range of concentrations. The next section discusses hybridization as a method to increase sensitivity. 2.4.4 Hybrid of sensitive materials In general, hybridization, in which two or more sensitive materials are combined to provide better conductivity, stability, and lower working temperature, can lead to superior gas sensors. Host–guest chemistry in hybrid composites can offer improvement in gas sensing devices in many applications, resulting from synergistic/complementary effects [85]. Similar to other types of transduction, hybrid composites have been examined to improve the performance of microwave sensors. Lee et al. functionalized carbon nanotubes with poly (m-H HBlue Pemigraniline SaltH HGreen Emeraldine SaltH HColorless LeucoemeraldineHBlue Emeraldine BaseViolet Pemigraniline Base+ 2H++ H++ e+ e+ e+ H++ e+ H+25 aminobenzene sulfonic acid) (PABS) and deposited them on a patch antenna substrate. In this study, PABS sensed ammonia by interacting with ammonia gas molecules via its NH functional groups [86]. In another study, Aloisio and coworkers investigated gas sensing properties of poly-(diallyl dimethyl ammonium chloride) (PDDAC) and MWCNTs mixture via depositing on a microstrip disk resonator substrate by inkjet-printing. The proposed sensor measured ammonia gas ranging from 0 to 25% [56]. Reduced graphene oxide (rGO) has received significant attention as a result of its large surface area and various functional groups on its surface [87]. Song et al. explored using rGO/ nanosilver ink (Ag-ink) on a patch antenna as a microwave sensor. This study also investigated the sensitivity of the patch antenna in wired and wireless experiments. The proposed sensors showed high sensitivity toward ammonia gas concentration ranging from 100 to 200 ppm in both experiments [88]. Singh et al. observed that ammonia gas can be detected by ZnO/nitrogen-doped graphene nanohybrid loaded on RF complementary SRR in a short interval of time; however, this sensor was not able to sense a low ppm concentration of ammonia gas [89]. Recently, phosphate-functionalized reduced graphene oxide (P-rGO) / PANI nanocomposite has been reported as a sensitive material for detecting ammonia gas in an SRR sensor. P-rGO and PANI could act as n-type and p-type semiconductors and contribute to the formation of localized heterojunction to improve gas sensing performance due to good electron-donating capability and extra lone electron pairs in p-rGO. Despite the notable sensitivity, the prolonged response time of the proposed sensor hinders its further development [7]. Current demands aim toward designing a robust sensor with a fast response along with low cost, high sensitivity, and precise ammonia gas detection. Furthermore, the sensor's selectivity as a performance factor in reducing false alarms needs to be studied in different atmospheric conditions and humidity levels. Table 2.3 presents research studies dealing with materials hybrid in microwave gas sensors. 26 As previously mentioned, in this thesis, PANI is a conductive polymer used for ammonia gas sensing. The next section describes a summary of the gas sensing mechanism based on the interactions between polymer and gas molecules. Table 2.3 Summary of employing hybrid in microwave sensors Sensor structure Sensing element Sensing range (ppm) Response time Selectivity testing Ref. Patch antenna PABS/SWCNT 50-100 5-20 min No [86] Microstrip disk resonators PDDAC/MWCNTs - - No [56] Patch antenna rGO/Ag-ink 100-200 - No [88] RF complementary split-ring resonator ZnO/N-doped graphene 100-500 66 s Yes [89] Split ring resonator P-rGO-Polyaniline 1-20 30 min No [7] 2.5 Gas sensing mechanisms of polymer nanocomposites Throughout this section, the sorption phenomenon and important factors affecting sorption have been discussed. Besides, two well-known adsorption isotherms of the process, and their curve respective were briefly presented. Generally speaking, the term sorption can be defined as the accumulation of gas species in the bulk or on the surface of the solid phase. Sorption can, therefore, be classified as either absorption or adsorption. The gas adsorption and absorption process are shown in Figure 2.13. The adsorbent refers to the solid surface where the sorption occurs, while the adsorbate is considered gas molecules adsorbing on the surface, respectively [90]. 27 Figure 2.13 Schematic of adsorption and absorption process [90] Absorption involves transferring gas components into the solid phase, which is an important process for separation purposes. It may also represent the begin.ning of the adsorption process. On the other hand, surface adsorption is the process in which the solid surface is able to interact with gas species to bind them to the surface. Thus, the adsorbent surface and gas molecules are both involved in adsorption. Gas sensing applications mainly utilize porous materials that have a large surface area to bind gas molecules not only on the outer surface but also via the pores, which play a critical role in the adsorption mechanism. Before explaining the factors that affect adsorption, we must understand how the process of adsorption works. The adsorption mechanism consists of three following steps, (1) gas molecular transfer from the bulk solution to the adsorbent (2) the penetration of gas molecules into the adsorbent and reaching the adsorption active sites; (3) fast binding of the adsorbate to the surface of the pores and channels which is described by adsorption isotherms. The reaction rate is governed by mass transfer (step 1) and intrinsic penetration (step 2) whereas step 3 proceeds rapidly [91]. Either physical or chemical interactions can dominate the interaction between adsorbent and gas molecules. Physisorption is a process where gas molecules attach freely on the surface with weak Van der Waals forces and desorb in the reverse direction without affecting the structure of the absorbent during the adsorption process. Conversely, chemisorption would refer to a process AdsorbentAdsorbateAdsorption Absorption28 that involves the chemical interactions of functional moieties on the surface of adsorbent with adsorbates through covalent, ionic, and hydrogen bonding. Desorption is the challenging part of this process [92]. This section has reviewed the three key aspects that mostly control the adsorption process. The first and most important attribute of an adsorbent is its surface area. Adsorbents with a larger surface area could interact more rapidly with gas molecules, resulting in a higher rate of adsorption and transferring more gas molecules onto the surface. Secondly, the temperature can affect the adsorption equilibrium based on the endothermic or exothermic nature of the adsorption process. A third consideration is the amount of time it takes to reach equilibrium. This is critical to making sure the adsorption process is complete. As time passes, gas adsorption remains constant leading to a decrease amount of adsorption sites [90, 93]. As an absorbent material, polymer nanocomposite exhibits the most promising interactions with adsorbate molecules due to the active sites and high surface area of the materials. Therefore, in the analysis of gas adsorption, this process is studied based on physiochemical properties of gas and surface at given temperatures, called adsorption isotherms. The term adsorption isotherm describes the quantity of adsorbate as a function of the concentration of gas molecules on the absorbent's surface at a given temperature [90, 94]. Adsorption isotherms illustrate the interaction between adsorbate and adsorbent. Through monolayer or multilayer adsorption interactions, we can identify the ways to improve the adsorption mechanism, determine surface properties and capacities of adsorbents, and design an effective adsorption system. Moreover, it is possible to predict the type of adsorption interactions between adsorbate and adsorbent in the system under study [95, 96]. In 1918 [97], Langmuir derived the rate of gas adsorption and desorption process on a solid surface separately for the first time. The schematic of the Langmuir adsorption model is shown in Figure 2.14. It is assumed that there are certain accessible gas adsorption sites on the surface of 29 a solid, called S, in the unit of mol/m2. During the adsorption process, a part of S is occupied by gas molecules, S1, and the rest are vacant, S0, i.e., S = S1 + S0 [90]. Thus, the process is governed by the following reaction (2.2) [98]. ka S + A(g) ⇄ S1 (2.2) kd where A(g) is gas of interest and ka and kd are the rate-constants for adsorption and desorption, respectively. The adsorption rate in Langmuir isotherm depends on the number of vacant sites and gas concentration, while the rate of desorption is proportional to the number of occupied sites. Thus, adsorption rate in equilibrium state for the reaction (2.2) [90], kd S1 = ka c S0 = ka c (S-S1) ➔ 𝑆1𝑆=𝑘𝑎c𝑘𝑑+𝑘𝑎c (2.3) where c is the concentration of A(g) and S1/S is the surface coverage, θ. Finally, the Langmuir equation is written as: θ =𝑘𝑎𝑐𝑘𝑎𝑐+𝑘𝑑 (2.4) Figure 2.14 Schematic of Langmuir adsorption model Occupied sites Vacant sites 30 It is worth mentioning that Langmuir's theory is generally based on some assumptions, including i) adsorption happens in a monolayer fashion, ii) the surface is homogenous, iii) the binding energy between molecules and sites is independent of the presence of other adsorbed molecules (independent of θ). In the real world, all the adsorption sites are not available. Therefore, a maximum surface coverage, θmax, is proposed for monolayer adsorption [99]. It means θ would change from 0 to θmax. Eq. (2.5), proposed by some researchers [99-101], describes the equilibrium adsorption process over time, where θads refers to the amount of adsorbed gas at a given temperature. Similarly, Eq. (2.6) has been proposed for the desorption process. 𝜃𝑎𝑑𝑠 (𝑡) =𝜃𝑚𝑎𝑥 𝑘𝑎 𝑐𝑘𝑎 𝑐 + 𝑘𝑑(1 − 𝑒−(𝑘𝑎 𝑐+𝑘𝑑)𝑡) (2.5) 𝜃𝑑𝑒𝑠(𝑡) = 𝜃𝑚𝑎𝑥(1 − 𝑒−𝑘𝑎 𝑡) (2.6) 𝜃𝑎𝑑𝑠 = 𝐾𝐿𝑐1+𝐾𝐿 𝑐 (2.7) where 𝐾𝐿 =𝑘𝑎𝑘𝑑 (2.8) These equations will be used later in Chapter 5 of this thesis to explain the adsorption/desorption process of ammonia gas sensing by GO-PANI. Despite the wide usage of the Langmuir theory, it fails to explain the adsorption process in some cases [102]. Accordingly, some other adsorption behaviors have been proposed in an attempt to make a better experimental fitting of gas adsorption on solids. Freundlich is one of the well-known isotherms that can be applied for multilayer adsorption, unequal distribution of affinity for adsorption sites, in heterogeneous adsorbents [90] (see Figure 2.15, comparing Langmuir and 31 Freundlich isotherms). In this non-ideal adsorption, sites with higher affinity are occupied first. Logarithmic change of the Freundlich isotherm can be expressed as follows, θ = 𝐾𝐹𝐶𝑒 𝑛 (2.9) where KF and n (n<1) are empirical constants defined by measurements. Figure 2.15 Schematic plot of Langmuir and Freundlich adsorption isotherms [90] To the best of our knowledge, there are only two studies on the gas adsorption mechanism by PANI composites. Hu et al. studied the adsorption and desorption patterns of PANI after exposure to ammonia as a result of PANI's optical properties [102]. It was observed that the kinetic curves were fitted satisfactorily by the Freundlich model, as opposed to the Langmuir isotherm. It was suggested that two different positive charge values related to an imine nitrogen atom and an amine one are available to adsorb ammonia on the PANI backbone [102, 103]. In another study, Li et al. explored the adsorption model for polyaniline/Ag nanocomposites in gas sensing. Langmuir's model was able to predict the time-dependent response of the sensor toward triethylamine and toluene gas, indicating that the PANI nanocomposite had a homogenous surface structure [99]. Of note, we could not find any study in the literature on the kinetic adsorption of ammonia by GO-PANI. 32 To conclude, the sensor response is a reflection of the morphological characteristics of the adsorbent as it is fitted to different isotherm models. We reviewed these two models in Chapter 5 considering our proposed sensor. Additionally, developing, designing, and optimizing sorption processes requires a good understanding of diffusional behavior in porous media. Generally, the diffusing molecules and the way they interact with their surroundings will determine the mechanism. The diffusion process in porous adsorbents is typically assumed to be an activated process in which molecules jump between sites. Absorption rates can be affected by a number of factors, including surface concentration, operating temperature, gas species, and pore structure. It has been shown that the relation between pore diameter and the effective diameter of diffusing molecules can influence gas transport regimes within adsorbents. Molecular diffusion and viscous flow dominate transport in large pores, with decreasing pore sizes resulting in an increasing contribution from Knudsen diffusion [104]. According to pore size, porous materials can be categorized as micropores d < 20 nm; mesopores 20nm < d < 500nm; macropores 500 nm< d. An analysis of BET results (Chapter 4.3) indicates GO-PANI is mesoporous (2-50 nm), resulting in dominating Knudsen diffusion. According to the following formula, the Knudsen diffusion constant (DK) of diffusing gas molecules depends on the temperature (T), the pore radius (r), the molecular weight (M), and gas constant (R). 𝐷𝑘 =4𝑟3√2𝑅𝑇𝜋𝑀 (2.10) Here, we reasonably assumed that the gas diffusion into a GO-PANI film follows the Knudsen diffusion process and a first-order surface reaction with ammonia gas concentration. Under these assumptions, it is allowed to rewrite an equation for the gas diffusion in porous materials, modeled by Fick’s law with a chemical reaction term [105, 106], as follows: 33 𝜕𝐶𝐴𝜕𝑡= 𝐷𝑘𝜕2𝐶𝐴𝜕𝑥2− 𝑘𝐶𝐴 (2.11) Here, CA shows the concentration of target gas, DK is the Knudsen coefficient, x is the distance from the top of the sensing film, and k is the rate constant. Figure 2.16 Schematic of gas sensing film on SRR At the steady-state condition, for the problem depicted in Eq. 2.12 with the boundary condition of CA=CA/out at x=0 and 𝜕𝐶 𝜕𝑥⁄ = 0 at x=L, L is the thickness of the sensing layer, the following solution can be obtained (Figure 2.16) [106]. 𝐶𝐴 = 𝐶𝐴/𝑜𝑢𝑡cosh((𝐿−𝑥)√𝑘 𝐷𝐾⁄ )cosh(𝐿√𝑘 𝐷𝐾⁄ ) (2.12) Considering Eq 2.12, it becomes clear that the sensing characteristics of the gas sensor are influenced by the thickness of the sensing element. Most research has focused on studying the properties of sensors with a fixed thickness (as discussed in Chapter 4). The literature holds no study investigating the effect of thickness in PANI gas sensing properties. Therefore, in Chapter 5 of this thesis, we explored the GO-PANI's interactions with ammonia gas on the SRR sensor by looking at both adsorption isotherms and their thickness variation effects. Substrate Sensitive film L Gas X=0 34 2.6 Section summary The purpose of this chapter was to introduce the present progress of microwave gas sensors. This literature review has identified that the integration of microwave transducers with sensitive elements can open new horizons for gas detection in different applications. Currently, the focus is on developing a robust ammonia gas sensor that has a fast response time, high sensitivity, and selectivity. Although several studies have been done to develop microwave sensor characteristics, the results were not satisfactory. Therefore, it is now necessary to improve our fundamental knowledge of microwave gas sensors along with material interactions to improve efficiency or eliminate the drawbacks of the earlier proposed microwave gas sensors. 35 Chapter 3: Materials and Methods 3.1 Materials All chemicals, including graphite powder, potassium permanganate (99.0%), sodium nitrate (99.0%), sulfuric acid (95-98%), hydrochloric acid (37%), hydrogen peroxide (30%), aniline (99.5%), ammonium persulfate (98%), phytic acid (50% w/w in H2O), potassium hydroxide (85%), and ammonium chloride (99.99%), were purchased from Sigma Aldrich and used without further purification. 3.2 Synthesis of graphene oxide The modified Hummers’ method was employed for the synthesis of GO nanosheets [107]. As shown in Figure 3.1, a mixture of 500 mg graphite powder and 500 mg sodium nitrate was poured into the Erlenmeyer flask while being mixed with a stir bar and enclosed in an ice bath, followed by the addition of 23 mL of sulfuric acid. Then, 3 g of potassium permanganate was gradually added while stirring the mixture and keeping its temperature below 20°C using a cold-water bath. To increase the reaction rate, the suspension was sonicated in an ultrasonic bath for 20 min at room temperature. Next, 40 mL deionized (DI) water was added to increase the oxidation degree of GO. To remove the unreacted potassium permanganate and unwanted water-soluble by-products, a mixture of 3 mL H2O2 and 100 mL DI water was added to the suspension. The suspension was then centrifuged and washed with HCl (10%) to remove metal ions, followed by a DI water wash to remove the remaining acid. At neutral pH, the suspension was centrifuged at 4000 rpm to remove any unexfoliated GO sheets. Prepared exfoliated GO sheets were maintained inside the water at room temperature to prevent any potential re-stacking. 36 Figure 3.1 Graphene oxide synthesis through modified Hummers’ Method 3.3 Synthesis of GO-PANI nanocomposites Using different nanostructures as templates during the oxidative polymerization of PANI has been an efficient method to synthesize PANI-based nanocomposites with higher molecular weight, conductivity, electrochemical capacitance, and stability compared to pure PANI [108-110]. Considering suitable functional groups, easy synthesis, high mechanical properties, and dispersibility in water, GO can provide an opportunity to perform as a multifunctional template for the growth of PANI [111]. To avoid the homogenous polymerization of aniline monomers and reduce the interaction of aniline molecules with themselves, dilute polymerization of PANI was implemented. The polymerization, which occurs mostly at the interface of the surface of the GO sheets and the solution [112], favors heterogeneous nucleation and mitigates the chance of the supersaturation of aniline monomers. To prepare the GO-PANI nanocomposites (Figure 3.2), aniline (40 μL), phytic acid (390 μL), and pre-determined amounts of the already-prepared suspension of GO sheets were combined in 5 mL of DI water. In this study, phytic acid, a natural organic substance found in plant seeds, was used as a protic acid to protonate the nitrogen atoms on PANI chains, increasing its doping level. The protonation of PANI is a significant contributing factor to its conductivity due to its role in generating polarons in the polymer backbone [113]. A H2SO4 NaNO3 KMnO4 H2O2 H2O Oven 80 ⁰C 24 h GO 37 higher protonation leads to a higher doping level, where the mobility of the charge carriers increases [48]. The mixture was then agitated in an ultrasonic bath for 30 min to exfoliate any GO stacking and then stirred vigorously for 1 h at a temperature below 4°C. Later, 64.8 mg of ammonium persulfate was added to the reaction medium. The mixture was then allowed to stir for 4 h at a temperature below 4°C. The polymerization of PANI was confirmed by observing the color change from the yellowish GO-aniline mixture to the dark green GO-PANI suspension. The prepared nanocomposite was washed and centrifuged with DI water several times. To reach the optimum GO quantity, we synthesized nanocomposites in [GO]:[aniline] weight ratios of 0:1 (0% of GO), 0.05:1 (5%), 0.1:1 (10%), and 0.12:1 (12%). Figure 3.2 In-situ Polymerization through Chemical Oxidative Process 3.4 Materials characterization 3.4.1 Spectroscopy Techniques 3.4.1.1 Fourier Transform Infrared (FTIR) Spectroscopy Fourier-transform infrared (FTIR) is a powerful, fast, and straightforward optical spectroscopy. The resulting spectrum represents molecular absorption or emission of a solid, liquid, or gas by IR radiation at a specific wavelength range [114, 115]. This technique provides an opportunity to identify samples qualitatively and quantitatively while assessing samples' quality (purity) [116]. In this thesis, attenuated total reflection Fourier-transform infrared (ATR-FTIR) was performed using a Thermo Scientific Nicolet iS20 FTIR spectrometer to provide information about functional groups' diversity on the surface of GO nanomaterials and PANI nanocomposites. ATR has Aniline Phytic acid APS 4h Centrifugation GO-Polyaniline nanocomposite GO 38 significantly impacted infrared analyses of solid and liquid samples in recent years because it addresses the most challenging aspects of infrared analysis, specifically, sample preparation and spectral reproducibility. As part of ATR FTIR spectroscopy, IR beams are emitted to the interface between the ATR element, a prism of ZnSe or diamond, silicon, or germanium, and a sample on its surface (Figure 3.3). Within the ATR element, the internal reflection generates an evanescent wave that interacts with samples [117]. The depth of penetration into the sample is determined by the angle of incidence and the refractive indices of the prism and the sample [118], which is typically in the range of 0.5–2 μm. Therefore, ATR improves characterization efficiency by allowing it to be faster, more reliable, and precise without requiring complicated sample preparation [119]. Figure 3.3 Graphic demonstration of an ATR-FTIR. All rights reserved [117] 3.4.1.2 Ultraviolet-visible (UV-Vis) Spectroscopy In the field of spectroscopy, UV-visible (UV-Vis) spectroscopy is a powerful technique that provides information on the electronic transitions occurring in a given material through light absorption. Beer-Lambert law (Eq. (3.1)) states that absorbance (A) depends on c, concentration (M), ε, molar absorptivity coefficient (M–1 cm–1), and l, light penetration length through the 39 sample (sample holder size (cm)). Alternatively, absorbance refers to the amount of light transmitted or reflected after interacting with a sample compared to incident light [120, 121]. A = ε l c = log 𝐼0𝐼 = − log 𝑇 (3.1) The double beam UV-Vis instrument Shimadzu UV-2700i was used in this project to characterize PANI coating on the surface of GO sheets. It has a single light source (usually a deuterium or tungsten lamp) and a monochromator in addition to a splitter and a series of mirrors to direct the beam to both the reference and the actual samples under analysis (Figure 3.4). DI water is employed as a reference to eliminate the impact of the medium. The reported optical absorptions for PANI composites are presented in Chapter 4. Figure 3.4 Illustration of a double beam UV-vis spectroscopy instrument. All rights reserved [122] 40 3.4.2 Microscopy Techniques 3.4.2.1 Scanning Electron Microscopy (SEM) Scanning electron microscopes (SEMs) are a type of electron microscope that provide images by scanning a sample with a focused beam of electrons. Depending on the depth of the atoms within the sample, the electrons' interaction with the sample generates different signals that provide insights into the surface topography and composition. Figure 3.5 shows the detailed apparatus used in the SEM device to generate images. In this study, the Tescan Mira 3 XMU device was used for SEM measurements. Figure 3.5 Schematic of Scanning Electron Microscopy (SEM). All rights reserved [123] Gun with filament Schottky field emitter Multihole aperture Linear tube Column chamber valve Scan coil and stigmator Anode Condenser lens Stigmator In-Lens detector Objective lens Vent valve Specimen chamber 41 3.4.2.2 Vector Network Analyzer (VNA) Vector network analyzer (VNA) is one of the main instruments in various phases of product development that verify the device's performance under test, facilitating modern wireless technologies. VNA consists of two elements: source and set of receivers. A stimulus signal generated by the source is altered as it passes through a device under test (DUT). Receivers are used to compare receiving signals to the incident signal to determine the changes. VNAs are able to measure the transmission and reflection of a microwave signal. As shown in Figure 3.6 (a), in the transmission measurement, the signal is injected into DUT, and the receiver on the output side measures signal variations at a single frequency. In contrast, the reflection measurement monitors the signal reflected from DUT. For each signal, magnitude and phase data will be collected. This is followed by switching to the next frequency and repeating the measurement [124, 125]. The results can be represented mathematically by complex scattering (S-) parameters. S-parameters describe the input-output relationship between ports in an electrical system. The Smn parameter is used for measuring in a two-port system, where "m" corresponds to the output port number and "n" to the input port number. Thus, the S-parameters are defined as follows (Figure 3.6 (b)) : | S11 |: The ratio of the voltage amplitude of the signal that reflects from port 1 to the voltage amplitude of the incident signal on port 1. | S12 |: The ratio of the voltage amplitude of the signal that transferred from port 2 to port 1 divided by the voltage magnitude of the incident signal in port 2 | S21 |: The ratio of the voltage amplitude of the signal that transferred from port 1 to port 2 divided by the voltage magnitude of the incident signal in port 1 | S22 |: The ratio of the voltage amplitude of the signal that reflects from port 2 to the voltage amplitude of the incident signal on port 2. 42 Figure 3.6 (a) and (b) The basic concept of a vector network analyzer 3.4.2.3 Profilometer Monitoring the surface characterization of the nanometer-level film is important for many industries, including microelectronics, semiconductors, solar, medical, and material research. A profilometer is an accurate instrument that allows one to measure dimensions and topographical characteristics of fabricated surfaces, such as thickness and surface roughness. In this research, to measure the thickness of the sample, a very low force stylus is moved vertically in contact with a sample, and then moved laterally across the sample for a predefined distance. Figure 3.7 shows a DektakXT apparatus was used in this study for thickness measurements [126]. Incident signal 43 Figure 3.7 DektakXT Stylus profilometer used for thickness measurement 44 Chapter 4: Graphene Oxide-Polyaniline-based Microwave Split-Ring Resonator In this Chapter, we introduce the design of a sensor utilizing a graphene oxide (GO)-polyaniline (PANI) nanocomposite, which is cast on an SRR microwave sensor. PANI was employed to act as a chemi-resistive material due to its outstanding electrical properties, low cost, facile synthesis, and ambient operating temperature [48, 127, 128]. Distinctive protonating-deprotonating interactions inside PANI can change its conductivity drastically, thereby enabling the detection of gas molecules [48]. Likewise, among different carbon-based materials, GO sheets with their large surface areas [129-133] have been used to improve PANI's sensitivity [128, 134, 135]. Leveraging the easy synthesis, tunable conductivity, and porous structure of PANI in conjunction with the environmentally-friendly, low-cost, and large-surface-area GO nanomaterial enabled the fabrication of a microwave SRR sensor with improved sensitivity to detect a broad spectrum of ammonia gas concentrations, as low as 1 ppm, at room temperature. The proposed sensor also enhanced the response amplitude by 45% compared to the pure PANI case, introducing a reliable sensor for potential breath analysis and disease diagnosis applications in a short interval of time (150-400 s). The prepared sensor can frequently sense ammonia gas with high selectivity in the presence of other high concentration hazardous gases and a wide range of relative humidity levels of 15-90%. The proposed ammonia gas microwave SRR sensor provided the elements of high selectivity, high sensitivity, and rapid response altogether in comparison to earlier studies, which could only excel in one or two properties. 4.1 Experimental setup and procedure The preparation method depicted in Figure 4.1 was used for the fabrication of the GO-PANI microwave split-ring resonator. 45 Figure 4.1 The schematic of the microwave resonator preparation steps The experimental setup displayed in Figure 4.2 was designed to analyze the response of the sensor in the detection of ammonia gas at a constant room temperature of 22.5°C. 46 Figure 4.2 The experimental gas sensing setup To produce ammonia gas, ammonium chloride (1M) and potassium hydroxide (40% v/v) were mixed at room temperature at pre-defined stoichiometric ratios (Table 4.1) inside a sealed, clear plastic box based on the following reaction: NH4Cl(aq) + NaOH(aq) → NH3(g) + H2O + NaCl (4.1) This reaction involves a proton transfer from the weak acid (NH4+) to the strong base, hydroxide ion (OH-) to produce ammonia gas. An electric fan was used inside the box to contribute to the evaporation and uniform distribution of the ammonia gas and atmospheric air over the microwave resonator. VNA Electrical Fan Power Supply 35mm Microwave resonator LabVIEW 47 Table 4.1 Water vapor content in different concentrations of ammonia gas Ammonia gas concentration (ppm) Volume of NH4Cl (1 M) + KOH (40% w/w) (μL) Ammonia (w/w%) Water (w/w%) 1 1.3 (NH4Cl 0.1 M) 0.085 99.92 5 6.6 (NH4Cl 0.1 M) 0.085 99.92 10 1.3 0.872 99.12 25 3.5 0.810 99.19 50 6.6 0.850 99.15 100 13 0.872 99.12 150 20 0.850 99.15 200 26.5 0.855 99.14 250 33 0.858 99.14 For gas sensing performance, VNA was used to collect the transmission gain (S21), using 1001 equally spaced data points, 0 dBm input power, and 300 Hz intermediate frequency (IF) bandwidth. The VNA can track the transient response of the resonant frequency, resonant amplitude, and quality factor [136]. The quality factor is a parameter that roughly represents the 'sharpness' of the peak. The quality factor usually tracks most closely with the resonant amplitude and it does not correspond to any single property of the material (permittivity, loss, etc.) [137]. In this experiment, the transmission gain (S21) was recorded at 15 s intervals using LabVIEW software. The sensor structure consisted of an SRR working within a frequency range of 7.4-8 GHz. Once the GO-PANI suspension was prepared, it was drop-cast onto the ring gap of the SRR and allowed to dry at room temperature for 24 h (Figure 4.3 a, b). As expected, the initial drop-casting of the suspension of PANI and GO-PANI on the ring gap resulted in the reduction of the 48 resonant amplitude by 15.74 and 13.46 dB, respectively, due to the increased conductivity and loss of generated charges in the polymer (Table 4.2). It also downshifted the resonant frequency by 210 and 120 MHz, respectively, as a result of the induced variation in the PANI's permittivity. In general, changes in the resonant amplitude and resonant frequency of the circuit are most closely linked to the dielectric loss tangent and permittivity of the polymer, respectively [138] (Figure 4.3 c). Figure 4.3 (a) The drop-casted GO-PANI suspension onto the ring gap of the SRR, and (b) dried suspension at room temperature after 24 h. (c) Response of the developed SRR before and after drop-casting of PANI and GO-PANI. Table 4.2 Variation in response in the presence of the clean and GO-PANI coated glass slide Material fr (GHz) Ar (dB) 7.4 7.6 7.8 8.0 8.2 8.4-40-35-30-25-20-15 Bare SRR PANI SRR GO (10%)-PANI SRRS21 (dB)Frequency (GHz)(a) (b) (c) 49 - 7.89 -14.43 PANI 7.67 -30.17 GO-PANI 7.77 -27.88 4.2 Results and discussion 4.2.1 Characterizations of GO-PANI The structural analysis of GO, PANI, and GO-PANI was probed by ATR-FTIR spectroscopy. This technique presented valuable information on the interaction of PANI with the functional groups on the surface of the GO sheets. As shown in Figure. 4.4, the IR spectrum of GO has the band vibrations of hydroxyl groups originated from the intercalated water and/or carboxylic acid at 3344 cm-1 transmittance peaks [139]. The presence of C=O, C-OH, and C-O-C functional groups on GO was confirmed with 1715, 1385, and 1164 cm-1 stretching vibrations, respectively [140]. The carbonyl, carboxyl, and hydroxyl groups, as nucleation sites on the GO sheets, reduced the interfacial energy barrier, which resulted in more controllable growth of the polymer on the surface of the GO sheets. The transmittance peak at 1621 cm-1 originated from C=C alkene functional groups on the sp2 carbon network of GO [141, 142]. 50 Figure 4.4 FTIR spectra of GO, and GO-PANI nanocomposites in different GO weight ratios PANI and GO-PANI nanocomposite consisted of alternating reduced (benzenoid) and oxidized (quinoid) repeat units [143]. As shown in Figure 4.4, the peaks at 1570 and 1490 cm-1 confirmed the chemical bonds related to the benzenoid (B) and quinoid (Q) forms of PANI units, indicating the emeraldine form (the conductive state of PANI), as an oxidation state of PANI. The vibrations at 1305 and 1235 cm-1 were correspondingly attributed to the C-N stretching in QBQ, QBB, BBQ, and BBB, respectively [144]. The strong characteristic peak at 1140 cm-1 indicates the positive charge distribution between quinoid and benzenoid rings, which is a measurement for the degree of delocalization of electrons [145]. In contrast to the FTIR spectrum of GO, the functional groups on the GO sheets could not be detected in the FTIR spectra of GO-PANI nanocomposite, owing to the coating of the GO sheets surface with PANI via specific interactions, including π–π electron stacking and hydrogen bonding between the amine group of PANI and oxygen group in GO [146, 147]. To confirm the surface coating of GO sheets with a layer of PANI, UV-Vis spectra were obtained. As shown in Figure 4.5, the absorption band at 246 nm is attributed to the π-π transition of the benzenoid rings inside PANI’s backbone [148]. The strong absorption peaks at 464 nm and 100015002000250030003500Wavenumber (cm-1)Graphene OxidePure PANIGO(5%)-PANIGO(10%)-PANIGO(12%)-PANI1200140016001800Wavenumber (cm-1)GO(5%)-PANIGO(10%)-PANIGO(12%)-PANI15701490130512351140Pure PANI156014401297121551 ~750-800 nm revealed the polymer's doping level, owing to the polaron generation and π molecular orbitals [149, 150]. Figure 4.5 UV-Vis adsorption of PANI and GO-PANI in different GO weight ratios Comparing the latter two absorption bands of different GO weight ratios in GO-PANI nanocomposites not only confirmed that the conductive state of PANI (emeraldine salt) was synthesized but also showed that the GO (10%)-PANI nanocomposite had the highest doping level, leading to a more conducive nanocomposite which is desirable for sensing applications. The structural surface morphologies of the GO sheets, PANI, and GO (10%)-PANI were explored using SEM as shown in Figure 4.6(a-d). The SEM images confirmed the successful low-temperature synthesis precedure of the vertically aligned nanodot array structure of the GO-PANI nanocomposite (Figure. 4.6 (d)), indicating the importance of well-dispersed and exfoliated GO sheets as a supporting substance that provided active sites and a template for growing a thin layer of PANI. 300 400 500 600 700Absorbance (a.u.)Wavelength (nm)GO (12%)-PANIGO (10%)-PANIGO (5%)-PANIPure-PANI52 Figure 4.6 SEM images of (a) GO, (b) PANI, and (c, d) GO-PANI nanocomposites The overall conductivity of GO-PANI nanocomposites depends on the conductivity of PANI because the GO structure with a combination of sp2 and sp3 carbon atoms acts as an insulator [151]. The highly ordered structure of PANI accelerates the charge carrier transport in the nanocomposite matrix [112]. Charge carriers transport through the backbone of PANI and hopping between its chains generate intra- and inter-chain conductivity of the polymer [152]. Comparing the SEM images of PANI and GO-PANI nanocomposite revealed that the GO sheets converted the compact and entangled coil form of PANI chains (Figure 4.6 (b)) to the nanodot conformation (Figure 4.6 (d)). The elongation of the PANI’s coil chains resulted in an improved alignment and ordered structure, which provided multiple pathways for charge transport (Figure 4.7 (a) and 4.7(b)). This is due to the strong hydrogen bonding, electrostatic interactions between anilinium ions and oxygen functional groups on the GO sheets, and π–π interactions between PANI’s benzene rings and conjugated bonds of GO’s graphitic lattice (Figure 4.7) [149]. The dispersibility of GO-PANI 5μm (b) 5μm (c) (d) (a) 2μm 1μm 53 in aqueous solutions facilitates the film formation of nanocomposites without the need for using organic solvents. Moreover, utilizing stable GO-PANI suspension results in the formation of smooth and uniform thin films using different deposition techniques. Figure 4.7 Transformation of (a) PANI’s coil structure to (b) the expanded coil when pinned to the GO sheets The thermal stability of GO and GO-PANI was recorded from ambient temperature to 900°C by the thermal gravimetric analysis (TGA) technique. Also, the differential thermal analysis (DTGA) graphs were plotted to identify the temperatures at which the material decomposition would occur, represented by peaks corresponding to the inflection points of the TGA curves (Figure 4.8). In the case of GO, significant weight loss of 52% was observed below 300°C with an inflection point at 190°C, which is mostly associated with the reduction of the least stable oxygen functional groups, including hydroxyl and a carboxylic acid, and the evaporation of the intercalated water, resulting in the generation of CO2 and CO gases, as well as water vapor. At a temperature range between 300 and 900°C, graphitic lattices on the GO sheets were degraded [141]. The weight loss around 240°C in PANI corresponds to the evaporation of the organic phytic acid dopant, whereas the decomposition of PANI itself initiated after 320°C [153, 154]. (a) (b) 54 Figure 4.8 Thermogravimetry graphs of GO and GO (10%)-PANI Comparing the thermogravimetry graphs of GO and PANI with GO (10%)-PANI showed a 25.4% weight loss below 280°C with the inflection point at 277°C in the GO (10%)-PANI curve. This weight loss corresponded to the moisture and the oxygen functional groups of the GO sheets, which confirmed the presence of GO sheets in GO-PANI nanocomposites. It is noteworthy to mention that the onset point of the decomposition of the oxygen functional groups in a GO (10%)-PANI nanocomposite occurred at a higher temperature than those of GO, further supporting the benefits of coating the GO sheets with PANI. The nitrogen adsorption-desorption isotherms (BET) of PANI and GO (10%)-PANI nanocomposite, presented in Figure 4.9 (a), was measured at 77 K. Based on the IUPAC classification, they both demonstrated type-IV isotherms, [155] with a sharp increase in the adsorbed volume of nitrogen and a hysteresis loop at a high relative pressure (P/Po) of 0.9 to 1, all indicating a mesoporous structure (2-50 nm) [156]. The specific surface areas determined by the BET equation were 18.26 and 14.47 m2 g–1 for PANI and GO (10%)-PANI, respectively. The reduction of the surface area in GO (10%)-PANI, compared with PANI, was expected as PANI 55 was formed on the surface of the GO sheets, decreasing both the sorption of nitrogen gas (Figure 4.9 (a)) and the number of pores on PANI. The BJH pore volume distribution as a function of the pore size, depicted in Figure 4.9 (b), confirmed the mesoporous structure and slightly reduced the surface area in GO (10%)-PANI. Figure 4.9 (a) BET nitrogen adsorption-desorption isotherms, and (b) BJH pore size distributions for PANI and GO (10%)-PANI samples According to the BET and BJH results, it could be concluded that pure PANI would be a better material for sensing; however, the dominant mechanism of sensing in GO (10%)-PANI nanocomposites are based on the chemisorption properties of PANI structure inside the nanocomposite rather than the physisorption qualities, which are based on the pore sizes and distributions. The change in the chemical configuration of PANI into a more conductive form controls the sensing mechanism in all GO-PANI nanocomposites. From the obtained characterization data, three different mechanisms for the interaction between the GO sheets and aniline through the in-situ polymerization could be speculated: (i) carboxyl and carbonyl groups of GO generated hydrogen bonding with the N-H of aniline monomer, (ii) functional groups on the GO sheets could act as dopants to protonate the PANI structure, and (iii) the π-π stacking caused (a) (b) 56 by the overlap between the planar graphitic structure of the GO sheets and benzene ring in PANI extended the π electron clouds of PANI. 4.2.2 Gas sensing mechanism Initial gas sensing experiments were performed to investigate the response of the fabricated microwave sensors exposed to ammonia gas [57]. Introduced ammonia gas interacted with the GO-PANI nanocomposite on the SRR and induced changes in its permittivity and conductivity via doping level variation. Since the output profile of the readout circuitry (resonant amplitude and resonant frequency) changes with variations in the conductivity of materials located in the ring gap of the resonator, this alteration can be used to monitor the concentration of the gas of interest. Figure 4.10 shows the variation of the S21 profile in the presence of 150 ppm ammonia gas (0-120 s) and air (120-570 s). The data in Figure 4.10 indicates that the exposure to ammonia gas increased the resonant amplitude by 1.65 dB and resonant frequency by 10 MHz. Beneficially, upon the exposure of the sensor to air, ammonia gas was removed from the setup and the response signal returned to its original position. Thus, the sensor was clearly able to detect the interaction between GO (10%)-PANI nanocomposite and ammonia gas. Figure 4.10 Change in S21 response of the microwave sensor coated with GO (10%)-PANI before and after exposure to 150 ppm ammonia gas 7.6 7.8 8.0-35-30-25 0 s 30 s 120 s 150 s 350 s 570 sS21 (dB)Frequency (GHz)Δ Resonant AmplitudeFrequency Shift7.70 7.75 7.80 7.85-28.0-27.5-27.0-26.5-26.0-25.5S21 (dB)Frequency (GHz)AdsorptionDesorption 0 sec 30 sec 120 sec 150 sec 350 sec 570 sec57 The exposure of GO (10%)-PANI nanocomposite to ammonia gas triggered the transformation of PANI from a conductive to a non-conductive form, owing to the acid-base interaction between PANI and ammonia gas [157]. Figure 4.11 The mechanisms of GO-PANI exposure to ammonia gas where doping and de-doping of PANI occurred As is shown in Figure 4.11, ammonia gas, acting as a base, manifested its strong de-doping effect by deprotonating PANI’s anilinium ions (R-NH+), which resulted in the reduction of charge carriers. The reduction in the number of polarons inside PANI’s backbone limited the mobility of charge carriers and enhanced the resistance inside PANI. When the sensor was exposed to air, PANI could reform itself by adsorbing hydrogen ions from NH4+ and restore its conductive form. In order to investigate the impact of GO in the GO-PANI nanocomposite sensing, three different concentrations of GO (5, 10, and 12 wt.%) were prepared. As Figure 4.12 shows, the addition of GO during the in-situ polymerization of aniline led to a higher response amplitude than pure PANI. GO has demonstrated unique properties as a hard template to provide active sites to generate sheet-like two-dimensional (2D) nanostructures. Simultaneously, it can act as a soft template to control the orientation of PANI's growth on its surface due to the amphiphilic property, oxygen functional groups on edges, and graphitic lattice on the basal plane. Furthermore, the oxygen functional groups on the graphene surface provide it with good dispersibility in liquid media and make it a dispersion-processable precursor for the preparation of graphene-based materials AmmoniaAirEmeraldine SaltA-A-A-Emeraldine BaseA-A-A-A-A-A-A-A-A-GO sheet GO sheet58 [120]. Considering suitable functional groups, easy synthesis, high mechanical properties, and dispersibility in water, GO can provide an opportunity to perform as a multifunctional template for the growth of PANI [121]. Therefore, higher sensing response in all GO-PANI nanocomposites stems from enhancing the charge carriers' mobility inside PANI’s backbone, which is induced by the elongation of PANI chains in response to the addition of GO. The positive effect of incorporating 10% GO inside PANI increased the response by 45% higher than pure PANI. The unfavorable homogenous nucleation of aniline monomers could be responsible for the lower responses of the nanocomposites with 5 and 12% GO. Figure 4.12 Microwave response of pure PANI and GO-PANI nanocomposites with different GO weight ratios when exposed to 50 ppm ammonia gas The obtained results in Figure 4.12 can primarily be explained by differences in the polymerization process caused by either excessive or deficient available GO surface. Whereas a shortage of available GO sheets for aniline monomers in GO (5%)-PANI nanocomposite could have left most polymerized aniline chains intact in their initial coil configuration, increasing the GO sheets concentration to 12% could have resulted in their agglomeration, leading to a reduced number of functional groups on their surfaces. This could have impeded the interactions between 0 2 4 6 8 10-26.0-25.5-25.0-24.5-24.0 PANI GO (5%)-PANI GO (10%)-PANI GO (12%)-PANIResonant Amplitude (dB)Time (min)59 GO and aniline monomers in which the polymer grows sparser and shorter on the surface of the GO sheets [112]. 4.2.3 Gas sensing measurement To quantify the sensor’s response to ammonia gas accurately, the phenomena of adsorption and desorption were defined as follows: complete adsorption was takenwhen resonant amplitude reached its maximum shift, and desorption was defined as the time when it returned to 90% of the steady-state signal [48]. Moreover, response time and recovery time were defined as the time taken for the adsorption and desorption processes, respectively [158]. The sensing performance of the designed microwave SRR gas sensor, which utilized GO (10%)-PANI nanocomposite for a broad concentration range of ammonia gas (1-250 ppm), is demonstrated in Figure 4.13 (a, and b). Figure 4.13 The sensor measuring resonant amplitude in different concentrations of ammonia gas; (a) 20—250 ppm, (b) 1-20 ppm Increasing the ammonia gas concentration reduced the number and mobility of the charge carriers, resulting in a decline in the conductivity of GO-PANI nanocomposite [159]. These 0 2 4 6 8 10-27.5-27.0-26.5-26.0-25.5-25.0Resonant Amplitude (dB)Time (min) 20 ppm 100 ppm 25 ppm 250 ppm 50 ppm 0 5 10 15 20-27.4-27.2-27.0-26.8-26.6 1ppm 15ppm 5ppm 20ppm 10ppm Resonant Amplitude (dB)Time (min)(a) (b) 60 variations, represented by the SRR as an increase in resonant amplitude, could be correlated to the different concentrations of ammonia gas. The results indicate the capability of the proposed microwave sensor to detect as low as 1 ppm concentration of ammonia gas, unlike other studies [54, 86, 89, 160]. The sensor's response for GO (10%)-PANI was fitted to a linear calibration curve to explore the designed sensor's potential in qualitative gas analysis in low concentration (1-25 ppm) and high concentration (50-250 ppm) settings. Figure 4.14 (a) illustrates the effectiveness of the sensor in < 25 ppm concentrations, which is a critical competence in medical applications. The low concentration sensitivity was found to be 0.039 dB ppm-1 with a standard deviation of 0.001 based on the calibration curve slope. Furthermore, the sensor was able to detect the higher concentrations ( > 25 ppm) of ammonia gas with a sensitivity of 0.0046 dB ppm-1 with a standard deviation of 0.0004, desired in emission control applications (Figure 4.14 (b)). (a) (b) Figure 4.14 The resonant amplitude variations of the GO (10%)-PANI sensor for low (a) and high (b) concentrations of ammonia gas 0 5 10 15 20 250.00.20.40.60.81.01.20 50 100 150 200 2501.01.21.41.61.82.0Resonant Amplitude (dB)Ammonia Concentration (ppm) Measurment Fitting liney = 0.039x + 0.093R² = 0.997Measurment Fitting liney= 0.0046x + 1.038R2= 0.98361 Moreover, the average response and recovery times measured for 1 to 250 ppm of ammonia gas were as low as 150 and 400 s, respectively, indicating a rapid performance compared with other microwave gas sensors [77, 160, 161]. Compared with the earlier study with rGO-PANI [7], although rGO could act as an n-type semiconductor and contribute to the gas sensing as a conductive material, the response time was faster when GO was introduced. This is due to the lower number of the functional groups on the rGO sheet, whereas GO provides more functional groups available for PANI to interact with [162]. The van der Waals and electrostatic interactions between GO sheet and PANI would result in the elongation of the PANI coils and create uniform structures of PANI chains. Figure 4.15 shows the reduction of the response time and faster gas detection as the concentration of ammonia gas increased from 1 to 250 ppm. Apparently, higher concentrations of ammonia gas are associated with the higher availability of its molecules to interact with GO (10%)–PANI sensing layer, leading to a faster response time of the sensor. Figure 4.15 The response time of the sensor for different ammonia gas concentrations 5 15 25 10001234Response Time (min)Ammonia Concentration (ppm)1 10 20 50 25062 To assess the reproducibility of the designed sensor, 20 ppm of ammonia gas was used for gas sensing over a 30-day interval. Figure 4.16 shows that the response signal was repeatable during three measurements after 30 days with less than 0.32% variation. Figure 4.16 The investigation on the repeatability and stability of the GO (10%)-PANI sensor Regarding the signals' stability between day 1 and day 30, the same sensor could measure 20 ppm of ammonia gas after one month. The differences between response and recovery times after 30 days in measurements were 25 s. To demonstrate the sensor's capability in the adsorption and desorption behavior of GO (10%)-PANI nanocomposite, the time-based response of the SRR was fitted to an exponential curve with fitting parameters shown in Table 4.2. The time-constant (𝜏) of response was almost constant for the three experiments of day 1 and day 30. Apparently, the proposed sensor showed higher consistency behavior in the adsorption and the desorption processes after 30 days, indicating that the initial GO (10%)-PANI fabrication process might have benefitted from a settling time. The results confirm the repeatability and stability of the prepared microwave sensor – the two characteristics critical for a functional sensor. 0 10 20 30 40 50-25.50-24.750 10 20 30 40 50-25.50-24.75Resonant Amplitude (dB) Sensor Respose- Day 1 Fitting Curve Sensor Response- Day 30 Fitting CurveTime (min)Time (min)63 Table 4.3 Fitting curve parameters of the sensor's time-based response to study stability in the adsorption and desorption cycles Fitting curve, (𝑨𝒆(−𝒕−𝒕𝟎𝝉) + 𝒚𝟎) Round Adsorption Cycle Desorption Cycle 𝜏 (min) A (dB) y0 R2(%) 𝜏 (min) A (dB) y0 R2(%) Day1-Exp.1 2.26 -1.06 -24.59 99.6 3.79 0.83 -25.64 99.3 Day1-Exp.2 4.57 -1.19 -22.60 99.4 4.43 0.74 -25.70 99.4 Day1-Exp.3 3.80 -1.14 -24.62 98.7 4.68 0.84 -25.68 99.3 Day30-Exp.1 2.38 -1.05 -24.70 98.2 4.32 0.75 -24.69 99.3 Day30-Exp.2 2.10 -0.99 -24.69 98.5 4.17 0.77 -25.59 99.4 Day30-Exp.3 2.11 -1.05 -24.50 98.4 3.98 0.78 -24.50 99.5 To evaluate the selectivity of the designed sensor, the response of the sensor was explored in the presence of high concentrations (~1000 ppm) of ethanol, methanol, isopropanol, acetone, n-hexane, chloroform, and tetrahydrofuran (THF) gases compared to 50 ppm of ammonia gas. The results presented in Figure 4.17 indicate that the responses of the microwave sensors of PANI and GO (10%)-PANI nanocomposites to ammonia gas were more than 550 and 726% higher than those of the other gases, respectively. According to Tables 2.1,2.2, and 2.3 in Chapter 2, only a few studies have assessed the selectivity of their microwave gas sensors [89, 160]; however, the measurements were based on the presence of the same concentrations of ammonia and other gases. The selectivity of the proposed sensor revealed that the addition of GO in PANI not only increased the sensitivity toward ammonia gas but also boosted the selectivity of the sensor. This makes GO (10%)-PANI a promising candidate for various ammonia gas sensing applications. 64 Figure 4.17 The selectivity comparison of the GO (10%)-PANI sensor when exposed to different gases in separate experiments Since humidity plays a critical role in the performance of gas sensors, the response of the sensor was studied by changing the relative humidity in the sensor environment from 3 to 90% (Figure 4.18 (a)). Despite the fact that the sensor was slightly sensitive to humidity, especially at high humidity values, the response to the humid environment was in the opposite direction of the response to ammonia gas. This is because, unlike ammonia, humidity improved the conductivity of PANI via acting as a source of protons and increased the doping level of the polymer, which consequently enhanced the charge transfer and reduced the resonant amplitude [163]. Monitoring the temperatures at which the relative humidity values were set is shown in Figure 4.18 (b). 65 Figure 4.18 a) sensor behavior in the various relative humidity (0-100%), and (b) The measured temperatures at which the relative humidity values were set To further explore the effects of humidity, ammonia gas sensing was performed in different relative humidity environments, including 15 to 90% (Figure 4.19). The results showed that the resonant amplitude response to ammonia gas was unaffected by humidity for RH < 40%, showing less than 1.75% deviation. Although higher humidity values caused a noticeable reduction in the resonant amplitude (i.e., 29.8 and 38.6% reduction for RH 60 and 90%, respectively), the sensor was still highly capable of sensing the ammonia gas. To boost the effectiveness of ammonia gas sensing in the particular application of breath analysis, it would be beneficial to install a simple filter in the point-of-care device for humidity reduction or investigate additional surface treatments to maintain ammonia gas sensitivity while removing the undesirable effects of humidity. 0 5 10 15 20 25 30 35020406080100Time (min)% Relative Humidity22.022.222.422.622.8Temperature (⁰C)0 10 20 30 40 50-33-32-31-30-29-28-27-26-25Resonant Amplitude (dB)Time (min)RH 3%RH 20%RH 35%RH 60%RH 75%RH 90%(a) (b) 66 Figure 4.19 Effect of different relative humidity on the sensor`s response in the presence of 25 ppm ammonia gas Overall, the fabricated sensor can be incorporated in breath analyzers for the diagnosis of certain kidney and liver diseases. On the other hand, it can also be used as an air quality control tool for the environment to prevent ammonia gas leakage and excessive nitrogen loading in the ecosystem for agricultural industries. Nitrogen pollution can deposit in ecosystems, resulting in eutrophication of water surfaces, preventing biodiversity, and increasing acidity of the soil [36]. Likewise, among different biomarkers of food spoilage, ammonia gas is also released from spoiled protein in meat. The developed sensor would be a reliable indicator to estimate the expiry time and prevent the consumption of contaminated foods. 4.3 Summary and conclusion Over the past decade, ammonia gas sensors have received significant attention as robust platforms for emission control, food safety, and monitoring human exhaled breath for the early diagnosis of diseases such as dysfunction of the kidney and liver. This study explores the development of a microwave-based split-ring resonator (SRR) sensor with enhanced sensitivity RH 15% RH 40% RH 60% RH 90%0.00.20.40.60.81.01.2Δ Resonant Amplitude (dB)67 to detect ammonia gas at low concentrations. The sensor is based on a nanocomposite fabricated by incorporating 10 wt.% of graphene oxide (GO) into polyaniline (PANI) via the in-situ polymerization of aniline monomers over the surface of the GO sheets. The addition of GO to PANI results in a high sensitivity of 0.038 dB ppm–1 for low concentrations (1-25 ppm) and 0.0045 dB ppm–1 for high concentrations (> 25 ppm) of ammonia gas, in a 150-400 s time interval at room temperature. The prepared sensor can selectively sense ammonia gas in the presence of other higher concentrations of hazardous gases and a wide range of relative humidity levels (15 to 90%). The response signal is repeatable after 30 days with less than 0.32% deviation. The developed low-cost and robust sensor has the potential to monitor ammonia gas in various applications, including medical, environmental, food, and agricultural sectors. 68 Chapter 5: Gas Sensing Behavior of Graphene Oxide-Polyaniline Nanocomposite Implemented in SRR Resonator As was pointed out in Chapter 4, a highly sensitive and selective GO-PANI-based microwave resonator has been developed in this thesis. Our objective in this chapter is to investigate the adsorption and absorption processes of ammonia gas with GO-PANI nanocomposite immobilized on a microwave gas sensor. Although few studies have been carried out on the application of GO-PANI nanocomposites [150, 164-167], no scientific understanding of the interaction mechanism of GO-PANI and ammonia gas molecules has been presented in the literature. The available reports on gas sensing of PANI also indicate that both adsorption and diffusional behavior may participate in the process [99, 101, 102, 168]. Thus, in this chapter, we investigated how GO affects PANI sorption behavior by analyzing the time-dependent response of microwave sensors. It is worth mentioning that the synthesis method of GO-PANI and the gas sensing measurements are similar to the ones discussed in Chapter 4. 5.1 Results and discussion As previously explained in Chapter 2, Eq. (5.1) was proposed to describe the adsorption process over time, where θads refers to the amount of adsorbed gas. Similarly, Eq. (5.2) has been proposed for the desorption process [99-101]. 𝜃𝑎𝑑𝑠 (𝑡) =𝜃𝑚𝑎𝑥 𝑘𝑎 𝑐𝑘𝑎 𝑐 + 𝑘𝑑(1 − 𝑒−(𝑘𝑎 𝑐+𝑘𝑑)𝑡) (5.1) 𝜃𝑑𝑒𝑠(𝑡) = 𝜃𝑚𝑎𝑥(1 − 𝑒−𝑘𝑎 𝑡) (5.2) 69 Ammonia gas acts as a base to deprotonate PANI by taking away a proton from the polymer backbone. Through ammonia gas exposure, acid-base interaction proceeds between charge carriers (holes) within PANI, as a p-type semiconductor, with ammonia, leading to the reduction of PANI's conductivity. Thus, we assume that the signal profile of the sensor depends on the number of active sites left on the surface of GO-PANI and the normalized resonant amplitude (𝑅𝐴𝑛𝑜𝑟𝑚) corresponds to the fraction of unoccupied active sites in GO-PANI [99]. 𝑅𝐴𝑛𝑜𝑟𝑚(𝑡) = 1 − 𝜃𝑎 ⅆ𝑠(𝑡) = 1 - 𝜃𝑚𝑎𝑥 𝑘𝑎 𝑐𝑘𝑎 𝑐 + 𝑘𝑑(1 − 𝑒−(𝑘𝑎 𝑐+𝑘𝑑)𝑡) (5.3) 𝑅𝐴𝑛𝑜𝑟𝑚(𝑡) = 1 − 𝑅𝐴∞ (1 − 𝑒 −𝑡𝜏𝑎𝑏𝑠) (5.4) where, 𝑏 =𝑘𝑎𝑘𝑑 (5.5) 𝑅𝐴∞ =𝜃𝑚𝑎𝑥 𝑏 𝑐𝑏 𝑐 + 1 (5.6) 𝜏𝑎𝑏𝑠 =1𝑘𝑎+𝑘𝑑 (5.7) Similarly, it has been reported that the gas desorption process follows the exponential behavior as follows [99]: 𝑅𝐴𝑛𝑜𝑟𝑚(𝑡) = 1 − 𝑅𝐴∞𝑒 −𝑡𝜏𝑑𝑒𝑠 (5.8) where 𝜏𝑑𝑒𝑠 =1𝑘𝑑 (5.9) 70 We attempted to apply the above-mentioned models to our adsorption-desorption process. Therefore, the exponential curves were fitted to time-dependent microwave sensor response detecting ammonia to find the semi-empirical parameters of 𝑅𝐴∞, 𝜏𝑎𝑑𝑠, 𝜏𝑑𝑒𝑠 in Eqs. (5.4) and (5.8). Figure 5.1. depicts sensor response and fitted curves for both adsorption and desorption processes for each ammonia concentration. Figure 5.1 The sensor response to different concentrations of ammonia gas. Table 5.1 Fitting constant of GO-PANI sensor response 0 1 2 3 4 50.900.920.940.960.981.001.02Normalized RA(t)/RA0Time (min) 5 ppm 50 ppm 15 ppm 100 ppm 25 ppm 250 ppmTime (min)5 6 86 7 9 10 11 12𝒚 = 𝑨𝟏 𝒆𝒙𝒑 (−𝒙𝝉) + 𝒚𝟎 Ammonia Concentration 𝑅𝐴∞ 𝜏ads (min) 𝜏des (min) 5 ppm 0.017 ± 0.002 3.48 ± 0.99 - 15 ppm 0.036 ± 0.002 1.65 ± 0.23 2.69 ± 0.09 25 ppm 0.042 ± 0.001 0.97 ± 0.04 2.61 ± 0.12 50 ppm 0.050 ± 0.002 0.71 ± 0.06 2.39 ± 0.07 100 ppm 0.068 ± 0.004 0.58 ± 0.07 2.03 ± 0.09 250 ppm 0.086 ± 0.004 0.58 ± 0.06 1.87 ± 0.09 71 Fitting constants, 𝑅𝐴∞, 𝜏𝑎𝑑𝑠, 𝜏𝑑𝑒𝑠 extracted from the response and recovery curves, are listed in Table 5.1. To visualize the data in Table 5.1, time constants of adsorption, 𝜏𝑎𝑑𝑠, and desorption, 𝜏𝑑𝑒𝑠, are depicted versus ammonia gas concentrations in Figure 5.2. Adsorption and desorption time constants of PANI composites have a power-law relationship with a concentration of gas which is also observed by other studies [99, 169, 170]. 𝜏𝑎𝑑𝑠, and 𝜏𝑑𝑒𝑠 decrease upon the exposure to increasing ammonia gas concentration due to the dependency of 𝜏 to the rate constant of adsorption and desorption process (see Eqs. (5.7) and (5.9)). Moreover, the time constant is an index showing the time required for the sensing process. Figure 5.2 Correlation of time constants vs. NH3 gas concentrations in GO-PANI nanocomposite As discussed in Chapter 2, there are different isotherm models that the adsorption mechanism can follow, including Langmuir and Freundlich models. In our study, these two models were chosen to evaluate microwave sensor response in the ammonia gas concentration range of 5-250 ppm. 𝑅𝐴∞ is an indicator of the absorbed amount of gas on the surface [99], which is depicted in Figure 5.3 versus ammonia gas concentration, c, to study the Langmuir and Freundlich models 0 50 100 150 200 2500.51.01.52.02.53.03.54.0 Experimental data for adsorption Experimental data for desorption Curve fitting for adsorption Curve fitting for desorptionAmmonia Concentration (ppm)τ adsτ = 10.93 c- 0.72τ = 3.67 c- 0.131.82.02.22.42.62.8τ des72 (see Eqs. (2.6) and (2.8) in the literature section). If the Langmuir model is the governing mechanism, the reciprocal of 𝑅𝐴∞ as a function of the reciprocal of c should be linear, whereas for the Freundlich model, the logarithm of 𝑅𝐴∞ versus the logarithm of c needs to be linear. Figure 5.3 Plotting the fitting parameter as a function of ammonia concentration fits Langmuir and Freundlich As shown in Figure 5.3, the Langmuir model fits well on the sensing data. It is suggested that the monolayer adsorption mechanism controls the interaction between ammonia gas and GO-PANI nanocomposites in the proposed microwave sensor, which is due to the homogenous surface. The homogeneity of the surface was also observed by the SEM images of synthesized GO-PANI (see Section 4.2.1). The reason for the homogeneity of the GO-PANI nanocomposite surface is that GO with its oxygen functional groups increases the polymer chain's linearity via heterogeneous polymerization. As a part of this study, we examined how the thickness of GO-PANI nanocomposites affects the performance of a microwave gas sensor. Changing the thickness of the GO-PANI film while keeping other parameters constant (i.e., temperature, humidity, and surface area), enables us to assess the contribution of gas penetration to the response of the microwave gas sensor. 0.00 0.05 0.10 0.15 0.2010203040506070 Experimental data Langmuir fitting1/RA∞1/Cy = 232.8x + 12.95R² = 0.990(a) (b) 1.0 1.5 2.0 2.5-1.8-1.6-1.4-1.2-1.0-0.8 Experimental data Freundlich fitting Log RA∞Log Cy = 0.40x - 1.98R² = 0.95Equation y = a + b*xPlot LWeight No WeightingIntercept -1.97621 ± 0.07294Slope 0.39852 ± 0.04403Residual Sum of Squares 0.01419Pearson's r 0.97644R-Square (COD) 0.95344Adj. R-Square 0.941873 To achieve consistency in sensing film surface area, Kapton tape was applied on the glass slide to create a surface of 5 mm x 5 mm. Then, GO-PANI suspensions were drop casted in different volumes, using a pipette, on a glass slide, as shown in Figure 5.4. Seven GO-PANI samples with different thicknesses of 1, 5, 11, 14, 18, 33, and 42 μm were prepared by this method. It should be mentioned that the thickness of the films was measured by a profilometer. Figure 5.4 Drop casting GO-PANI on the surface of the glass slide. The area of GO-PANI film is a surface of 5 mm x 5 mm The preparation steps depicted in Figure 5.5 indicate the deposition of the GO-PANI on a glass slide followed by its placement over the microwave split-ring resonator. 50 mm 50 mm 74 Figure 5.5 The schematic of the microwave resonator preparation steps The performance of the fabricated sensor was tested by comparing the S21 response of bare and GO-PANI-coated glass slides laid down on the resonator as shown in Figure 5.6. In this stage of the experiments, there is no ammonia gas in the system. The resonance behavior of the sensor was affected by placing a clean glass slide on the top of the resonator due to the change in the dielectric properties in the proximity of the sensor. 75 Figure 5.6 Experimental results comparing the response of the resonator when GO-PANI on a glass slide lies on the ring gap of SRR Moreover, the glass slide with GO-PANI of 1, 14, 42 μm thickness – aligned with the gap of the outer ring as the sensitive area – resulted in the reduction of the resonant amplitude by 1.96, 5.65, and 12.66 dB, respectively. This trend in the sensor response is expected as the conductance and loss of generated charges increase by the conductive film thickness, resulting in a lower transmission of power between two ports (i.e S21). The resonant profile also upshifted the resonant frequency by 350, 320, and 300 MHz, respectively, as a result of the induced variation in the GO-PANI's permittivity. In general, changes in the resonant amplitude and resonant frequency of the circuit are most closely linked to the polymer's dielectric loss tangent and permittivity, respectively. The characteristic of the spectral response is outlined in Table 5.2, where the presence of the glass slide changed the resonant profile. 6.0 6.2 6.4 6.6 6.8 7.0 7.2-40-35-30-25-20-15-10-5 Bare resonator GO-PANI- Thickness 1μm Bare resonator with glass GO-PANI- Thickness 14 μm GO-PANI- Thickness 42 μm S21(dB)Frequency (GHz)76 Table 5.1 Variation in response in the presence of the clean and with the GO-PANI coated glass slide Simulated bare Experimental bare Clean glass slide GO-PANI 1μm 14μm 42 μm fr (GHz) 6.48 6.56 6.33 6.21 6.24 6.26 Ar (dB) -9.73 -10.54 -12.70 -12.50 -16.19 -23.20 The effect of GO-PANI thickness on gas sensor performance was evaluated by placing different thicknesses of GO-PANI on a glass slide ranging from 1 to 42 μm. Measurements were conducted at four ammonia gas concentrations ranging from 5 to 100 ppm in seven different thicknesses of GO-PANI (1, 5, 11, 14, 18, 33, 42 μm). The humidity and temperature were kept constant in the laboratory during experiments, which was monitored by a commercial humidity sensor. The resonant amplitude of the sensor was extracted to generate the calibration curve for each thickness as shown in Figure 5.7 (a), where the slope of the calibration curve determines the sensitivity of the gas sensors. Based on the results, the sensitivity of the gas sensor increased by the thickness of the GO-PANI film (up to the thickness of 14 μm). As all film samples had approximately the same surface area, this increased sensitivity was an indication that ammonia had penetrated the sensing layer, where this absorption increased by increasing the thickness. This is a confirmation that the diffusion mechanism may contribute to our gas sensor response. 77 Figure 5.7 (a) and (b) Response of GO-PANI toward ammonia gas in different thicknesses Figure 5.7 (a) also shows that the slope of the calibration curves remained almost constant after a certain thickness. To explain this trend, Figure 5.7 (b) illustrates the effect of thickness in each concentration individually. Clearly, there is an optimal thickness for each concentration in 5 ppm 10 ppm 50 ppm 100 ppm0.000.020.040.060.080.100.120.140.160.18Normalized Resonant amplitude 1 μm 14 μm 33 μm 5 μm 18 μm 42 μm 11 μm0 20 40 60 80 1000.000.050.100.150.200.25Normalized RA (dB)Ammonia Concentration (ppm) 1 μm 14 μm 33 μm 5 μm 18 μm 42 μm 11 μm (a) (b) 78 which the sensor has the best performance. Microwaves can sense the variations in permittivity and conductivity of materials when penetrating through materials. As already noted, the majority of the sorption phenomenon took place on the surface of the sensing film, due to active sites for binding. As the thickness of the sensing film increases, the performance of the microwave sensor may decrease since the surface becomes farther away from the transmission line and the intensity of the fringing waves decreases (see Figure 5.8). Accordingly, we suggest that the designed microwave sensor became less capable of detecting GO-PANI permittivity variation at thicknesses over 14 μm, which is the reason for the almost constant response after this thickness. Consequently, it is important to realize that thickness has significant effects on both gas diffusion and microwave propagation. Further studies will be needed in order to optimize each microwave propagative structure. Figure 5.8 A schematic illustrating how extending a surface of sensing film away from the transmission line affects the fringing E-field waves` intensity 5.2 Summary and conclusion Throughout this chapter, the microwave sensor's response to ammonia gas was monitored and analyzed to understand the interactions and mechanisms of gas sensing in the proposed microwave gas sensor. 79 An analysis of the surface adsorption revealed that the Langmuir isotherm model matched well with adsorption and desorption data, suggesting that the surface of the synthesized GO-PANI was homogeneous, which agreed with the SEM images (See Section 4.2.1). Additionally, the purpose of thickness experiments was to understand if the thickness of sensitive elements affects the sensitivity of microwave gas sensors at a constant surface area, temperature, and humidity. Results suggest that there is an optimal thickness for the sensing material, denoting the contribution of both sorption mechanisms. 80 Chapter 6: Conclusion and future works 6.1 Results summary This study was performed to fabricate a microwave split-ring resonator (SRR) as an ammonia gas sensor that can surpass the previously reported sensors in selectivity, sensitivity, and response time. The developed GO-PANI nanocomposite-based microwave SRR sensor successfully demonstrated the detection of minimal concentrations of ammonia gas as low as 1 ppm. The identification of ammonia gas was based on analyzing the transient response of the resonant amplitude in different environments quantified by exponential fitting analysis. The study showed that the addition of 10 wt.% of GO to PANI could provide PANI with both an outstanding template and dopant that could alter the conformation of PANI towards higher conductivity structures. The fabricated sensor resulted in a sensitivity of 0.038 dB ppm–1 for a concentration range of 1–25 ppm and 0.0045 dB ppm–1 for concentrations above 25 ppm of ammonia gas at room temperature. The ammonia gas detection was measured to be as quick as 150 s. Moreover, the sensor could selectively sense ammonia gas in the presence of other hazardous gases and a wide range of humidity levels (15–90%). Furthermore, the sensor's stability and reproducibility were confirmed after repeating more experiments over a one-month interval. The sensitive, selective, and fast response offered by the developed SRR is a combination of multiple improvements, including the unique implementation of the GO-PANI nanocomposite, and the compact and high-resolution design of the resonator. The proposed ammonia gas sensor has the potential to be utilized in various applications such as healthcare and provide insights towards future improvements of the microwave-based SRR sensors, enabling more effective ammonia gas detection. Further, the surface adsorption and diffusion mechanisms of ammonia gas sensing in a GO-PANI-based microwave gas sensor were also investigated. 81 In surface adsorption, the Langmuir model was well fitted. It is suggested that the monolayer adsorption mechanism regulates the interaction between ammonia and GO-PANI nanocomposites, which is due to the homogenous surface. In the presence of oxygen functional groups of GO, a homogeneous surface of GO-PANI can be obtained since heterogeneous polymerization of PANI increases linearity within the polymer chain and increases surface homogeneity. An investigation was designed and conducted to explain the effect of the thickness of GO-PANI on the performance of microwave gas sensors. As the thickness increases, the response amplitude increases, demonstrating that absorption is also involved in the gas sensing mechanism along with surface adsorption. However, the effect of larger thicknesses on microwave propagations obligates us to find the ideal thickness for sensitive materials embedded in the microwave propagative structure. 6.2 Future works • Stability and repeatability measurements For a short time (one month), the stability and repeatability of the designed gas sensor were examined (See Section 4.1.3). 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Using equation (1) and the target frequency of 8 GHz, L was calculated to be approximately 1.25 cm, which was the initial design for the resonator circumference, as shown in Figure A.1 Figure A.1 The measured topological values of the designed microwave split-ring resonant. Rogers RT/duroid 5880 high-frequency substrates with a dielectric thickness of 0.079 mm, a relative permittivity of 2.20, and a loss tangent of 0.0009 were used to fabricate the resonator. The bare resonator response is shown in Figure A.2 a, and the S21 response with no PANI or GO is compared with the simulated response generated by Ansys High-Frequency Structure Simulator (HFSS) electromagnetic modeling software. The resonator was further simulated to 97 determine the location inside the ring that could provide maximum sensitivity and increase the reliability of the measurements. According to the electric field simulation results, presented in Figure A.2 b, the ring gap was the region of the highly concentrated electric field and the suitable position for drop-casting the material. Figure A.2 (a) the simulated and experimentally measured S21 response of the bare resonator, (b) HFSS simulations of electric field intensity of the bare resonator 7.6 7.8 8.0 8.2 8.4-45-40-35-30-25-20-15-10S21 (dB)Frequency (GHz) Simulated Measured(a) (b) 98 Appendix B Design of split-ring resonator using in Chapter 5 To perform gas sensing, a double split-ring resonator was designed and optimized using Ansys High Frequency Structure Simulator (HFSS) to operate at 6.48 GHz. In microwave resonators, the resonant frequency is related to the dimensions of the structure, resulting in a trade-off between the operating frequency and miniaturization. Thus, in this work, C band (4-8 GHz) was selected to achieve a compact sensing device while maintaining a sufficient penetration depth of the microwave signal to sense and detect the material under test on the distance of 201 μm to 242 μm (glass slide + thickness of GO-PANI). The top view of the microstrip structure implemented on a 35 µm thick copper layer with the relevant dimensions in millimeters is depicted in Figure B.1. Figure B.1 Top view of the microstrip ring resonator structure modeled in HFSS A Rogers RT/duroid 5880 substrate with a dielectric thickness of 0.79 mm, a relative dielectric constant of 2.20 ± 0.02, and a loss tangent of 0.0009 was utilized to implement the designed resonator structure and guide the microwave signal between two ports. In order to optimize the design for the target application, the ring gap of the inner and outer rings was changed to achieve a high intensity of E-field on the distance of 0.2 to 0.7 mm from the top of the sensor. To do so, the electric field analysis was performed by considering a 0.2 mm glass slide on top of the sensor gi= 0.3 go=0.3 1.35 2.35 1 Li=9.7 L0=16.9 0.3 99 where the GO-PANI would be deposited. Based on the E-field distribution shown in Figure B.2, it can be observed that as the gap of the outer ring decreases, the intensity and penetration depth of the fringing E-field increases. The minimum feature size of the conductor that can be fabricated through the conventional etching process in our laboratory is ~ 0.2 mm. Thus, the inner and outer ring gap was selected to be 0.3 mm, which results in a highly concentrated E-field near the outer gap where the material detection would be performed. Figure B.2 Examine different inner and outer ring gap sizes on electric field distribution on the resonator model simulated using HFSS. The comparison of the simulation and measurement results of the transmission coefficient (S21) response of the bare sensor and the sensor with the glass slide is shown in Figure B.3. E Field [V/m] 1.000e+02 4.000e+04 5.634e+03 1.237e+03 6.693e+03 1.464e+04 gi=0.5 mm, go=1 9.813e+03 1.982e+04 gi=0.5 mm, go=0.8 1.536e+04 2.364e+04 gi=0.5 mm, go=0.5 1.9763e+04 2.957e+04 gi=0.5 mm, go=0.3 2.397e+04 3.701e+04 gi=0.3 mm, 100 Figure B.3 Simulation and measurement results for S21 response of the bare resonator 6.0 6.2 6.4 6.6 6.8 7.0 7.2-40-35-30-25-20-15-10-5 Simulated Measured S21(dB)Frequency (GHz)
Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Arjmand, Mohammad
Zarifi, Mohammad Hossein
Microwave gas sensor technology : developing graphene oxide-polyaniline nanocomposites for improved sensing and detection of ammonia gas
Text