A Wireless Electronic Nose System Using a Fe2O3 Gas Sensing Array and Least Squares Support Vector Regression

This paper describes the design and implementation of a wireless electronic nose (WEN) system which can online detect the combustible gases methane and hydrogen (CH4/H2) and estimate their concentrations, either singly or in mixtures. The system is composed of two wireless sensor nodes—a slave node and a master node. The former comprises a Fe2O3 gas sensing array for the combustible gas detection, a digital signal processor (DSP) system for real-time sampling and processing the sensor array data and a wireless transceiver unit (WTU) by which the detection results can be transmitted to the master node connected with a computer. A type of Fe2O3 gas sensor insensitive to humidity is developed for resistance to environmental influences. A threshold-based least square support vector regression (LS-SVR)estimator is implemented on a DSP for classification and concentration measurements. Experimental results confirm that LS-SVR produces higher accuracy compared with artificial neural networks (ANNs) and a faster convergence rate than the standard support vector regression (SVR). The designed WEN system effectively achieves gas mixture analysis in a real-time process

Classification of Two Volatiles Using an eNose Composed by an Array of 16 Single-Type Miniature Micro-Machined Metal-Oxide Gas Sensors

The artificial replication of an olfactory system is currently an open problem. The development of a portable and low-cost artificial olfactory system, also called electronic nose or eNose, is usually based on the use of an array of different gas sensors types, sensitive to different target gases. Low-cost Metal-Oxide semiconductor (MOX) gas sensors are widely used in such arrays. MOX sensors are based on a thin layer of silicon oxide with embedded heaters that can operate at different temperature set points, which usually have the disadvantages of different volatile sensitivity in each individual sensor unit and also different crossed sensitivity to different volatiles (unspecificity). This paper presents and eNose composed by an array of 16 low-cost BME680 digital miniature sensors embedding a miniature MOX gas sensor proposed to unspecifically evaluate air quality. In this paper, the inherent variability and unspecificity that must be expected from the 16 embedded MOX gas sensors, combined with signal processing, are exploited to classify two target volatiles: ethanol and acetone. The proposed eNose reads the resistance of the sensing layer of the 16 embedded MOX gas sensors, applies PCA for dimensional reduction and k-NN for classification. The validation results have shown an instantaneous classification success higher than 94% two days after the calibration and higher than 70% two weeks after, so the majority classification of a sequence of measures has been always successful in laboratory conditions. These first validation results and the low-power consumption of the eNose (0.9 W) enables its future improvement and its use in portable and battery-operated applications

Colorimetric Plasmonic Gas Sensor

학위논문(석사)--서울대학교 대학원 :공과대학 재료공학부,2019. 8. 장호원.Plasmonics 분야는 지난 수십 년 동안 많은 관심을 받았으며 다양한 응용 분야에 적용 가능성을 보여주었습니다. 다양한 응용 분야 중 특히 가스 감지를 목적으로 하는 플라즈몬 (plasmonics)에 대한 연구가 활발히 진행되고 있다. 가스 센서의 감도, 선택도 및 내구성을 향상시키기 위해 가스 센서의 광 센서로서 플라즈몬을 사용하는 것에 대한 많은 연구가 이루어졌습니다. 광학 센서는 전압을 가할 필요가 없으며 전자기적으로 노이즈에 영향을 받지 않으며 가열 메커니즘을 필요로 하지 않으므로 반도체식 센서에 비해 더 높은 신뢰도를 보인다. 본 연구에서, 플라즈몬 공명의 전자기 강화와 결합 된 광학 간섭의 개념을 기반으로 가스 검출을 위한 센서를 설계하였다. 센서의 플라즈몬 층과 빛의 상호 작용에 의해 야기 된 국부적 인 표면 플라즈몬 공명 (LSPR)과 표면 플라즈몬 분극 (SPP)은 다양한 색의 센서를 제조하는데 이용되었다. 색상은 Lumerical software Finite Difference Time Domain (FDTD) 솔루션을 통해 시뮬레이션 하였다. Reflectance 를 위해 Si 기판 위에 Al layer를 thermal evaporator로 증착 하였다. 이후 e-beam evaporator를 이용해 WO3 박막 또는 WO3 nanorods 구조체를 제작하였다. 최종적으로 Au 필름을 증착하여 plasmonic 효과를 분석하였다. 센서의 플라즈몬 층에서 발생하는 공진은 환경 변화에 매우 민감하다. 따라서, 촉매로서 귀금속으로 장식 된 나노 구조 금속 산화물은 기체의 흡착 및 탈착을 위한 유전체 매체로 사용되었다. 가스의 흡착으로 인해 센서의 광학적 특성에 변화가 생길 것으로 예상하였으며, 그 결과 산란, 흡수 및 투과 스펙트럼에서 피크 시프트가 발생할 것으로 기대한다. 이러한 피크 시프트와 색 변화가 플라즈몬 센서의 가스 감지 능력을 판별하는 파라미터로 사용된다.The field of plasmonics has been of much interest over the past few decades, showing potential for use in various applications. Of these applications, the use of plasmonics in gas sensing is currently being investigated. In order to enhance the sensitivity, selectivity, and durability of gas sensors, many studies have focused on the use of plasmonics as optical sensors for gas sensing. Because optical sensors require no contact measurements, are electromagnetically noise independent, and do not require a heating mechanism they can be more reliable compared to electrical sensors. In this study, the concept of optical interference coupled with the electromagnetic enhancement of plasmon resonances is used to design a sensor for the colorimetric detection of gases. The localized surface plasmon resonance (LSPR) and surface plasmon polariton (SPP) caused by the interaction of light with the plasmonic layer of the sensor is utilized in fabricating sensors of various structural colors. The structural colors were simulated through Lumerical software Finite Difference Time Domain (FDTD) Solutions then fabricated for comparison. The resonances that occur at the plasmonic layers of the sensors are extremely sensitive to changes in its environment. Therefore, nanostructured metal oxides decorated with noble metals as catalysts were used as the dielectric medium for the adsorption and desorption of gases. The adsorption of gas is expected to bring about a change in the sensors optical properties, which in turn causes a peak shift in the scattering, absorption, and transmission spectra. These peak shifts and the possible color change associated with these shifts are used as the response for our plasmonic sensor.Table of Contents Abstract i Contents iii List of tables vi List of figures vii Chapter 1. Introduction 1.1 Background 2 1.2 Objectives of this study 5 Chapter 2. Literature review 2.1 Classification of gas sensing methods 9 2.2 Fundamentals of optical gas sensors 12 2.2.1 Types of optical gas sensors 12 2.2.2 Plasmonic gas sensors 14 2.3 Optical Interference 16 2.3.1 Thin film optical interference theory 16 2.3.2 Structural colors 17 Chapter 3. WO3 thin film with Au plasmonic layer on Al mirror layer for the detection of NO2 3.1 Introduction 19 3.2 Sensor fabrication 21 3.2.1 Thin film plasmonic sensor 21 3.3 Characterization 22 3.4 Finite Difference Time Domain (FDTD) simulation 24 3.5 Gas sensing measurement 25 3.5.1 Optical response 25 3.6 Conclusion 28 Chapter 4. Au/Pd decorated WO3 Nanorods on Al mirror layer for the detection of H2 and NO2 4.1 Introduction 30 4.2 Sensor fabrication 34 4.2.1 Resistive sensor with nanorods 34 4.2.2 Plasmonic sensor with nanorods 35 4.3 Characterization 36 4.4 Gas sensing measurement 38 4.4.1 Resistive response 38 4.4.2 Optical response 42 4.5 Conclusion 45 Chapter 5. Summary 5.1 Summary 47 References 48 Abstract (in Korean) 58Maste

Metodologia de seleccion de componentes principales comunes para representacion y extraccion de las derivas presentes en sensores de gas

Los sistemas de detección y clasificación de olores a menudo se ven afectados por la presencia de derivas, esto ocasiona que los modelos utilizados en los algoritmos para el reconocimiento de patrones tengan cortos periodos de utilidad y por lo tanto tienen la necesidad de una recalibración constante (Arthurson, Eklöv, Lundström, Marterson, Sjöstrom, & Holmberg, 2000). Este fenómeno, además de hacer obsoletos los modelos construidos, degradan la estabilidad del dispositivo en el proceso de reconocer y cuantificar los compuestos volátiles (Ziyatdinov, y otros, 2009). Este trabajo presenta una metodología novedosa para enfrentar el problema de las derivas existentes en sensores químicos empleados en sistemas de olfato artificial, por medio de la cual se logra mitigar el efecto causado por las mismas al reducir los errores en la clasificación de diferentes compuestos volátiles. Se aplicó la técnica de análisis estadístico multivariado, denominada Análisis de Componentes Principales Comunes (CPCA) combinada con la técnica de corrección de componentes (CC) planteada por (Arthurson, y otros, 2000) y se determinó un criterio de selección del número de componentes principales comunes a ser substraídas de las medidas para mejorar la exactitud en el proceso de detección de olores. La metodología propuesta en este trabajo tuvo como punto de partida la investigación realizada en (Ziyatdinov, y otros, 2009), donde los autores emplean sólo la primera componente principal común para hacer la corrección de las derivas con el propósito de asumir ésta corrección como lineal. Las componentes de deriva presentes en sistemas de olfato artificial son realmente no lineales, por tanto en este trabajo se incorporó como novedad el remover un mayor número de componentes para mitigar su efecto y a su vez considerando el no capturar información relevante para el sistema de clasificación. Los resultados de substraer mayor cantidad de componentes principales comunes en la corrección de las derivas demostraron que el remover más de una componente principal común mediante la corrección de componentes, ocasiona incrementos en los porcentajes de acierto en el proceso de clasificación sobre los datos de validación. Se determina el número adecuado de componentes que se deben remover a partir de un indicador de la separabilidad de los conjuntos de datos, calculado a partir del conjunto de datos usado para el entrenamiento.Magister en Automatización y Contro

Understanding Our Local Environment: Developing Novel Approaches To Quantify and Apportion Ambient VOCs With Low-Cost Sensors

In this dissertation, we demonstrate the application of low-cost air quality sensors to better understanding our local environment. Specifically, my work has focused on the application of arrays of low-cost sensors and methods of analysis that improve our ability to attribute local sources of volatile organic compounds (VOCs). Low-cost sensors have been widely applied to the study of air quality at smaller spatial and temporal scales than was previously feasible. The research that is detailed in Chapter 2 built upon existing low-cost sensor research in order to develop an approach to both quantifying the concentrations of several compounds and also classifying the mixture based on the source that is likely to have emitted the detected gases. This research involved a chamber study where a large sensor array was exposed to complex gas mixtures that simulated realistic pollution sources. These data were used to validate the proposed methodology that involved a two-step process to accomplish the quantification and classification goals. The results of this approach show the feasibility of using low-cost sensors to directly estimate the effect of local sources of VOCs based on their chemical fingerprints.</p