This thesis describes the development of high performing low-cost and low-power ammonia sensors on plastic substrates using solution processing techniques. As a part of the Marie Curie Initial Training Networks, FlexSmell project aimed at the realisation of such sensors as elements of a sensing system on flexible tags for wireless compatible applications. Ammonia was selected as the target analyte due to its importance in many application fields including food industry, air and water quality monitoring. Polyaniline, a conjugated polymer, was used as the sensing layer for chemiresistive detection of ammonia because of its well-known gas sensing properties. Two distinctive strategies were adapted to tackle doped polyaniline’s lack of solution processablity. Firstly, dopant engineering was utilised to prepare doped polyaniline formulations in aprotic solvents such as n-methyl-2-pyrrolidone. Hybrid composites were then prepared by simply mixing the polyaniline solutions and carbon nanoparticles. Sensors made by spin coating the polyaniline hybrid composites on plastic substrates operating at ~80 °C showed sensitivities more than 6 times higher than that of a commercial metal oxide sensor when exposed to sub-ppm concentrations of ammonia in air. The incompatibility of the multifunctional dopants used in this method with printed electronics, as well as the high boiling point and toxicity of the solvent led to the second approach. A two-step vapour-phase deposition polymerisation method was exploited to in-situ polymerise different polymeric acid-doped polyaniline thin films on plastic substrates. Polyaniline sensors doped with poly(4-styrenesulphonic acid), demonstrated sensitive response to sub-ppm concentrations of ammonia vapour under both dry and humid conditions. These sensors showed enhanced recovery and repeatability when operated at elevated temperatures. Moreover, room temperature ammonia sensors were realised using Nafion as the dopant. Finally, ammonia sensors were made on small (~1 mm^2) printed polymeric micro-hotplates using a vapour-phase deposited polyaniline sensing layer in order to allow reliable operation at ~95 °C with power consumptions as low as 35 mW. Such low-cost, low-power, sensitive and selective ammonia chemiresistors may be incorporated in smart RFID tags for food, air and water quality monitoring.

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The master's thesis deals with the question of breath analysis using chemiresistors as detection elements for exhaled air analysis. Emphasis is placed on the application of fluctuation-enhanced sensing for chemiresistors for breath analysis, construction design of usable measurement system, and its calibration. Compared to the usual concept, which includes various methods ranging from DC processing in time to controlled impedance measurement, this method of signal analysis focuses on the evaluation of fluctuations and determination of indicators of its change in dependence of change in detected substance concentration.

A determinação de cada espécie que compõe uma mistura gasosa tem sido alvo de muitas pesquisas. Existem equipamentos para tal finalidade tais como, cromatografia gasosa, espectroscopia de infravermelho e sensores. A fim de viabilizar uma aplicação de baixo custo para a determinação da concentração de espécies em uma mistura gasosa, neste trabalho, é proposto um aparato para sensoriamento de H₂, CH₄ e CO encontrados em gases combustíveis. O sensoriamento é efetuado por quimioresistores de SnO₂ comercialmente disponíveis. O aparato consiste de um sistema de coleta da mistura gasosa e de sua diluição antes de seguir com a análise feita pelos sensores, obedecendo aos requisitos de segurança contra explosões. O aparato foi submetido a 125 diferentes misturas oriundas da combinação das concentrações de 0, 200, 800, 1500 e 2000 ppm de cada espécie gasosa utilizando o nitrogênio (99,999%) como gás de arraste. As amostragens foram avaliadas sob dois diferentes métodos de recuperação dos sensores: forçado e natural. Através dos resultados experimentais obtidos, foi observado que: a sensibilidade cruzada dos sensores de CO e de CH₄ é bastante elevada enquanto que o sensor de H₂ apresentou maior seletividade e, o método de recuperação natural apresentou melhores resultados em função da estabilidade térmica do sistema. Uma rede neural artificial foi desenvolvida e treinada com o objetivo de superar o problema das sensibilidades cruzadas. Os resultados obtidos pela rede neural são promissores e apresentaram erro máximo de 0,1 % para o hidrogênio, 23% para o metano e 29% para o monóxido de carbono para a obtenção da concentração absoluta de H₂, CH₄ e CO encontrados em misturas com composições conhecidas de antemão. / The achievement of the content of each component of a gas mixture from gasifiers has been a matter of several studies. There are specific techniques for this purpose, such as: gas chromatography, infrared spectroscopy and sensors. In order to allow a low cost application for obtaining the concentrations in a gas mixture, this study proposes a set up for sensing H₂, CH₄ and CO found in fuel gases produced by gasifiers. The sensing is performed by commercially available chemiresistors of SnO₂. The proposed set up collects the gas mixture and dilutes it before proceeding the sensing step, based on the safety requirements to avoid explosion. 125 different gas mixtures were prepared from the combination of 0, 200, 800, 1500 and 2000 ppm of H₂, CH₄ and CO using nitrogen (99.999%) as the carrier gas. The samples were evaluated under two different methods for sensor recovery: forced and natural. Based on the results, it was established that: the cross sensitivity of the CO and CH₄ sensors is too high while the H₂ sensor presents higher selectivity (almost 100%) and the natural recovery method showed improved results because of the better thermal stability of the system. An artificial neural network was developed and trained with the purpose of overcoming the problem of cross sensitivities. The results achieved by means of the neural network are promising and indicated a maximum error of 0.1% for hydrogen, 23% for methane and 29% for carbon monoxide when the absolute concentration of H₂, CH₄ and CO found in the gas mixtures are obtained from well known compositions.

The interactive wireless sensor detects many hazardous gases such as Hexane, Propane, Carbon monoxide and Hydrogen. These gases are highly toxic and used in different kinds of manufacturing industries, domestic purpose and so on. So, building a sensor that can detect this kind of gases can save the environment; prevent the potential for explosion, and endangering human life. In long term, interactive wireless sensor can also prevent the financial losses that might occur due to the hazardous incident that might occur due to these toxic gases. Hexane is a colorless, strong gas which inhaled in significant amounts by a person then he may suffer with hexane poisoning and suffocation. It also causes skin burns when exposed in high concentrations. Propane, carbon monoxide and hydrogen can easily freeze in room temperature, if in contact with eye, it could permanently damage eye or cause blindness. The advantage of this wireless sensor is the use of artificial olfactory system (electronic nose) that can be taught to detect these hazardous gases. This sensor has a unique molecular combination of analysts, impurities and background that corresponds to a gas leak. It consists of a chemiresistor, such as an array of conductometric sensors, and a mechanism analyzing the data in real time. A smell-print is composed of many molecules which reaches receptor in the human nose. When a specific receptor receives a molecule, it sends a signal to the brain where the smell is identified and associated with that particular molecule. Similar manner, albeit substituting sensors for the receptors, and transmitting the signal to a machine learning algorithm for processing, rather than to the brain. This wireless gas leak sensing consists of microchip Pic 32, integrated electronic nose, automated data analysis unit, power supply, and communications. The communication channel will use the ZigBee link, or the cellular links, or other specific frequency wireless link. The time-stamped and position-stamped sensor measurement data are transmitted to the central computer in predetermined periods of time. The data will be stored in the computer database for possible future analysis of the gas leak development process.

A determinação de cada espécie que compõe uma mistura gasosa tem sido alvo de muitas pesquisas. Existem equipamentos para tal finalidade tais como, cromatografia gasosa, espectroscopia de infravermelho e sensores. A fim de viabilizar uma aplicação de baixo custo para a determinação da concentração de espécies em uma mistura gasosa, neste trabalho, é proposto um aparato para sensoriamento de H₂, CH₄ e CO encontrados em gases combustíveis. O sensoriamento é efetuado por quimioresistores de SnO₂ comercialmente disponíveis. O aparato consiste de um sistema de coleta da mistura gasosa e de sua diluição antes de seguir com a análise feita pelos sensores, obedecendo aos requisitos de segurança contra explosões. O aparato foi submetido a 125 diferentes misturas oriundas da combinação das concentrações de 0, 200, 800, 1500 e 2000 ppm de cada espécie gasosa utilizando o nitrogênio (99,999%) como gás de arraste. As amostragens foram avaliadas sob dois diferentes métodos de recuperação dos sensores: forçado e natural. Através dos resultados experimentais obtidos, foi observado que: a sensibilidade cruzada dos sensores de CO e de CH₄ é bastante elevada enquanto que o sensor de H₂ apresentou maior seletividade e, o método de recuperação natural apresentou melhores resultados em função da estabilidade térmica do sistema. Uma rede neural artificial foi desenvolvida e treinada com o objetivo de superar o problema das sensibilidades cruzadas. Os resultados obtidos pela rede neural são promissores e apresentaram erro máximo de 0,1 % para o hidrogênio, 23% para o metano e 29% para o monóxido de carbono para a obtenção da concentração absoluta de H₂, CH₄ e CO encontrados em misturas com composições conhecidas de antemão. / The achievement of the content of each component of a gas mixture from gasifiers has been a matter of several studies. There are specific techniques for this purpose, such as: gas chromatography, infrared spectroscopy and sensors. In order to allow a low cost application for obtaining the concentrations in a gas mixture, this study proposes a set up for sensing H₂, CH₄ and CO found in fuel gases produced by gasifiers. The sensing is performed by commercially available chemiresistors of SnO₂. The proposed set up collects the gas mixture and dilutes it before proceeding the sensing step, based on the safety requirements to avoid explosion. 125 different gas mixtures were prepared from the combination of 0, 200, 800, 1500 and 2000 ppm of H₂, CH₄ and CO using nitrogen (99.999%) as the carrier gas. The samples were evaluated under two different methods for sensor recovery: forced and natural. Based on the results, it was established that: the cross sensitivity of the CO and CH₄ sensors is too high while the H₂ sensor presents higher selectivity (almost 100%) and the natural recovery method showed improved results because of the better thermal stability of the system. An artificial neural network was developed and trained with the purpose of overcoming the problem of cross sensitivities. The results achieved by means of the neural network are promising and indicated a maximum error of 0.1% for hydrogen, 23% for methane and 29% for carbon monoxide when the absolute concentration of H₂, CH₄ and CO found in the gas mixtures are obtained from well known compositions.