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The purpose of this paper is to design and validate an electronic nose (E-nose) prototype using commercially available metal oxide gas sensors (MOX). This prototype has a sensor array board that integrates eight different MOX gas sensors to handle multi-purpose applications. The number of sensors can be adapted to match different requirements and classification cases. The paper presents the validation of this E-nose prototype when used to identify three gas samples, namely, alcohol, butane and cigarette smoke. At the same time, it discusses the discriminative abilities of the prototype for the identification of alcohol, acetone and a mixture of them. In this respect, the selection of the appropriate type and number of gas sensors, as well as obtaining excellent discriminative abilities with a miniaturized design and minimal computation time, are all drivers for such implementation.

The suggested prototype contains two main parts: hardware (low-cost components) and software (Machine Learning). An interconnection printed circuit board, a Raspberry Pi and a sensor chamber with the sensor array board make up the first part. Eight sensors were put to the test to see how effective and feasible they were for the classification task at hand, and then the bare minimum of sensors was chosen. The second part consists of machine learning algorithms designed to ensure data acquisition and processing. These algorithms include feature extraction, dimensionality reduction and classification. To perform the classification task, two features taken from the sensors’ transient response were used.

Results reveal that the system presents high discriminative ability. The K-nearest neighbor (KNN) and support vector machine radial basis function based (SVM-RBF) classifiers both achieved 97.81% and 98.44% mean accuracy, respectively. These results were obtained after data dimensionality reduction using linear discriminant analysis, which is more effective in terms of discrimination power than principal component analysis. A repeated stratified K-cross validation was used to train and test five different machine learning classifiers. The classifiers were each tested on sets of data to determine their accuracy. The SVM-RBF model had high, stable and consistent accuracy over many repeats and different data splits. The total execution time for detection and identification is about 10 s.

Using information extracted from transient response of the sensors, the system proved to be able to accurately classify the gas types only in three out of the eight MQ-X gas sensors. The training and validation results of the SVM-RBF classifier show a good bias-variance trade-off. This proves that the two transient features are sufficiently efficient for this classification purpose. Moreover, all data processing tasks are performed by the Raspberry Pi, which shows real-time data processing with miniaturized architecture and low prices.

This study aims to investigate the ammonia-sensing performance of polyaniline/polyethylene oxide (PANI/PEO) and polyaniline/polyethylene oxide/zinc oxide (PANI/PEO-ZnO) composite nanofibers at room temperature.

Gas sensor structures were fabricated using microfabrication techniques. First, onto the SiO₂ wafer, gold electrodes were fabricated via thermal evaporation. PANI/PEO nanofibers were produced by the electrospinning method, and the ZnO layer was deposited by using radio frequency (RF) magnetron sputtering on the electrospun nanofibers as a sensing layer. Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and X-ray diffraction were performed to characterize the analysis of nanofibers. After all, gas sensing analysis of PANI/PEO and PANI/PEO/ZnO nanofibers was conducted using an experimental setup at room temperature conditions. Furthermore, the impact of humidity (17%–90% RH) on the sensor resistance was actively investigated.

FTIR analysis confirms the presence of functional groups of PANI, PEO and ZnO in nanofiber structure. SEM micrographs demonstrate beads-free, thinner and smooth nanofibers with ZnO contribution to electrospun PANI/PEO nanofibers. Moreover, according to the gas sensing results, the PANI/PEO nanofibers exhibit 115 s and 457 s response time and recovery time, respectively. However, the PANI/PEO/ZnO nanofibers exhibit 245 s and 153 s response time and recovery time, respectively. PANI/PEO/MOx composite nanofibers ensure stability to the NH₃ gas owing to the high surface/volume ratio and decrease in the humidity dependence of gas sensors, making gas sensors more stable to the environment.

In this study, ZnO was deposited via RF magnetron sputtering techniques on PANI/PEO nanofibers as a different approach instead of in situ polymerization to investigate and enhance the sensor response and recovery time of the PANI/PEO/ZnO and PANI/PEO composite nanofibers to ammonia. These results indicated that ZnO can enhance the sensing properties of conductive polymer-based resistive sensors.

The purpose of this paper is to deal with the classification improvement of pollutant using WO3 gases sensors. To evaluate the discrimination capacity, some experiments were achieved using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol via four WO3 sensors.

To improve the classification accuracy and enhance selectivity, some combined features that were configured through the principal component analysis were used. First, evaluate the discrimination capacity; some experiments were performed using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol, via four WO3 sensors. To this end, three features that are derivate, integral and the time corresponding to the peak derivate have been extracted from each transient sensor response according to four WO3 gas sensors used. Then these extracted parameters were used in a combined array.

The results show that the proposed feature extraction method could extract robust information. The Extreme Learning Machine (ELM) was used to identify the studied gases. In addition, ELM was compared with the Support Vector Machine (SVM). The experimental results prove the superiority of the combined features method in our E-nose application, as this method achieves the highest classification rate of 90% using the ELM and 93.03% using the SVM based on Radial Basis Kernel Function SVM-RBF.

Combined features have been configured from transient response to improve the classification accuracy. The achieved results show that the proposed feature extraction method could extract robust information. The ELM and SVM were used to identify the studied gases.

Due to the environmental conditions, the detection and identification of hazardous situations in coal mines is a challenge. The purpose of this research is to focus on the underground fire detection, especially of smoldering fires, which are characterized by the outgassing of CO and C2H4.

The study developed a system based on a single semiconductor gas sensor and sensors for relative humidity and temperature. With a high‐dynamic‐range hardware control and data acquisition platform a commercial semiconductor gas sensor is operated with an application‐optimized temperature cycle to improve stability and selectivity.

A hierarchical evaluation strategy not only allows identification of smoldering fires signified by CO and C2H4 with a ratio of 100:1, but is also suitable for separating or quantifying typical interfering compounds such as CH4, CO, NOX or H2 thus helping to avoid costly false alarms. After promising laboratory pre‐tests, a system was built for field tests including test gases and re‐calibration algorithms. Currently the authors are working on a self‐monitoring strategy based on redundant data from impedance spectroscopy to improve the stability of the system.

This approach allows detection of C2H4 at sub‐ppm concentrations even in the presence of CH4 at levels up to 1 percent with only a single gas sensor. The system achieves classification reproducibility as well as robustness allowing the development of a cost‐efficient under‐ground fire detection system. The novel self‐monitoring strategy will further improve the reliability of the system.

The purpose of this paper is to explore a new possibility of providing high-temperature stable lead-free interconnections for low-temperature co-fired ceramics (LTCC) hotplate. For gas-sensing application, a temperature range of 200°C-400°C is usually required by the sensing film to detect different gases which imply the requirement of thermally stable interconnects. To observe the effect of parameters influencing power of the device, electro-thermal simulation of LTCC hotplate is also presented. Simulated LTCC hotplate is fabricated using the LTCC technology.

The proposed task is to fabricate LTCC hotplate with interconnects through vertical access. Dedicated via-holes generated on the LTCC hotplate are used to provide the interconnections. These interconnections are based on adherence and bonding mechanism between LTCC and thick film. COMSOL software is used for finite element method (FEM) simulation of the LTCC hotplate structure.

Thermal reliability of these interconnections is tested by continuous operation of hotplate at 350°C for 175 h and cycling durability test performed at 500°C. Additionally, vibration test is also carried out for the hotplate with no damage observed in the interconnections. An optimized firing profile to reproduce these interconnections along with the experimental flowchart is presented.

Research activity includes design and fabrication of LTCC hotplate with metal to thick-film based interconnections through vertical access. Research work on interconnections based on adherence of LTCC and thick film is limited.

A new way of providing lead-free and reliable interconnections will be useful for gas sensor fabricated on LTCC substrate. The FEM results are useful for optimizing the design for developing low-power LTCC hotplate.

Adherence and bonding mechanism between LTCC and thick film can be used to provide interconnections for LTCC devices. Methodology for providing such interconnections is discussed.

This paper aims to study different possibilities for implementing easy-to-use and cost-effective micro-systems to detect and trace expelled gases from rotten food. The paper covers various radio-frequency identification (RFID) technologies and gas sensors as the two promoting feasibilities for the tracing of packaged food. Monitoring and maintaining quality and safety of food in transport and storage from producer to consumer are the most important concerns in food industry. Many toxin gases, even in parts per billion ranges, are produced from corrupted and rotten food and can endanger the consumers’ health. To overcome the issues, intelligent traceability of food products, specifically the packaged ones, in terms of temperature, humidity, atmospheric conditions, etc., has been paid attention to by many researchers.

Food poisoning is a serious problem that affects thousands of people every year. Poisoning food must be recognized early to prevent a serious health problem.

Contaminated food is usually detectable by odor. A small gas sensors and low-cost tailored to the type of food packaging and a communication device for transmitting alarm output to the consumer are key factors in achieving intelligent packaging.

Conducting polymer composite, intrinsically conducting polymer and metal oxide conductivity gas sensors, metal–oxide–semiconductor field-effect transistor (MOSFET) gas sensors offer excellent discrimination and lead the way for a new generation of “smart sensors” which will mould the future commercial markets for gas sensors.

Small size, low power consumption, short response time, wide operating temperature, high efficiency and small area are most important features of introduced system for using in package food.

The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view.

The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status.

The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design.

The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.

One of the key components of the micro-sensors is MEMS micro-hotplate. The purpose of this paper is to introduce a platinum micro-hotplate with the proper geometry using the analytical model based on the heat transfer analysis to improve both heating efficiency and time constant.

This analytical model exhibits that suitable design for the micro-hotplate can be obtained by the appropriate selection of square heater (LH) and tether width (WTe). Based on this model and requirements of routine sample loading, the size of LH and WTe are chosen 200 and 15 μm, respectively. In addition, a simple micro-fabrication process is adopted to form the suspended micro-heater using bulk micromachining technology.

The experimental results show that the heating efficiency and heating and cooling time constants are 21.27 K/mW and 2.5 ms and 2.1 ms, respectively, for the temperature variation from 300 to 400 K in the fabricated micro-hotplates which are in closed agreement with the results obtained from the analytical model with errors within 5 per cent.

Our design based on the analytical model achieves a combination of fast time constant and high heating efficiency that are comparable or superior to the previously published platinum micro-hotplate.

This paper describes a study into the development of an optimum fabrication process for the batch production of thick film titanium oxide‐PVC pH electrodes. Multi‐factorial experimental plans are used to explore the effect of fabrication parameters on the average sensitivity of sensors and their batch variability. Analysis of the results indicates that a number of the factors have a small effect on the sensitivity. Factors of particular importance are identified and TiO₂ in a PVC binder is examined in detail. The optimal values for these fabrication values are identified and examined. Mechanisms for the experimentally observed sub‐Nernstian responses and immersion‐time variations in sensor behaviour are discussed.