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Gas Sensors: Materials, Mechanism and Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Chemical Sensors".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 7083

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Dr. Anne Hémeryck

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Guest Editor

Laboratory for Analysis and Architecture of Systems, UPR 8001, Toulouse, France
Interests: micro and nanoelectronics; nanoenergetic materials; metals; oxides; semiconductors; surfaces and interfaces; defects and diffusion multiscale modeling (TCAD) from atomic scale calculations to macroscopic simulations
Special Issues, Collections and Topics in MDPI journals

Dr. Tingqiang Yang

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Guest Editor

LAAS-CNRS, Université de Toulouse, CNRS, INP, 6 AllÃée Emile Monso, 31400 Toulouse, France
Interests: gas sensors; semiconducting metal oxides; density functional theory; surface chemistry

Special Issue Information

Dear Colleagues,

Gas sensors with high sensitivity, excellent selectivity, good stability, probably low power consumption, low detection limits and low cost are a goal of most researchers in this field. As a type of chemical sensor, sensitive materials play key role in gas sensing performance in most cases, except for some optical gas sensors. Deepening our understanding of gas-sensing mechanisms is necessary to improve or optimize such sensors’ performance. Only with more reliable performance will gas sensors draw increased attention and be applied to different areas.

This Special Issue aims to collect research and review papers reporting on recent progress in materials utilized in different types of gas sensors, including novel synthesis methods, morphology control, doping and functionalization. New experimental and theoretical insights into gas sensing mechanisms are particularly welcome. Developments in applications of gas sensors also fall within the scope of this issue.

Dr. Anne Hémeryck
Dr. Tingqiang Yang
Guest Editors

Manuscript Submission Information

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Keywords

gas sensors
nanomaterials
semiconducting metal oxides
polymers
gas sensing mechanism
gas absorption
gas sensor applications

Published Papers (5 papers)

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Research

11 pages, 2818 KiB

Open AccessArticle

Minimum Detection Concentration of Hydrogen in Air Depending on Substrate Type and Design of the 3ω Sensor

by Dong-Wook Oh, Kwangu Kang and Jung-Hee Lee

Sensors 2023, 23(21), 9009; https://doi.org/10.3390/s23219009 - 06 Nov 2023

Viewed by 918

Abstract

Hydrogen has emerged as a promising carbon-neutral fuel source, spurring research and development efforts to facilitate its widespread adoption. However, the safe handling of hydrogen requires precise leak detection sensors due to its low activation energy and explosive potential. Various detection methods exist, with thermal conductivity measurement being a prominent technique for quantifying hydrogen concentrations. However, challenges remain in achieving high measurement sensitivity at low hydrogen concentrations below 1% for thermal-conductivity-based hydrogen sensors. Recent research explores the 3ω method’s application for measuring hydrogen concentrations in ambient air, offering high spatial and temporal resolutions. This study aims to enhance hydrogen leak detection sensitivity using the 3ω method by conducting thermal analyses on sensor design variables. Factors including substrate material, type, and sensor geometry significantly impact the measurement sensitivity. Comparative evaluations consider the minimum detectable hydrogen concentration while accounting for the uncertainty of the 3ω signal. The proposed suspended-type 3ω sensor is capable of detecting hydrogen leaks in ambient air and provides real-time measurements that are ideal for monitoring hydrogen diffusion. This research serves to bridge the gap between precision and real-time monitoring of hydrogen leak detection, promising significant advancements in the related safety applications. Full article

(This article belongs to the Special Issue Gas Sensors: Materials, Mechanism and Applications)

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9 pages, 2798 KiB

Open AccessArticle

An Artificial Neural Network to Eliminate the Detrimental Spectral Shift on Mid-Infrared Gas Spectroscopy

by Sanghoon Chin, Jérôme Van Zaen, Séverine Denis, Enric Muntané, Stephan Schröder, Hans Martin, Laurent Balet and Steve Lecomte

Sensors 2023, 23(19), 8232; https://doi.org/10.3390/s23198232 - 03 Oct 2023

Viewed by 763

Abstract

We demonstrate the successful implementation of an artificial neural network (ANN) to eliminate detrimental spectral shifts imposed in the measurement of laser absorption spectrometers (LASs). Since LASs rely on the analysis of the spectral characteristics of biological and chemical molecules, their accuracy and precision is especially prone to the presence of unwanted spectral shift in the measured molecular absorption spectrum over the reference spectrum. In this paper, an ANN was applied to a scanning grating-based mid-infrared trace gas sensing system, which suffers from temperature-induced spectral shifts. Using the HITRAN database, we generated synthetic gas absorbance spectra with random spectral shifts for training and validation. The ANN was trained with these synthetic spectra to identify the occurrence of spectral shifts. Our experimental verification unambiguously proves that such an ANN can be an excellent tool to accurately retrieve the gas concentration from imprecise or distorted spectra of gas absorption. Due to the global shift of the measured gas absorption spectrum, the accuracy of the retrieved gas concentration using a typical least-mean-squares fitting algorithm was considerably degraded by 40.3%. However, when the gas concentration of the same measurement dataset was predicted by the proposed multilayer perceptron network, the sensing accuracy significantly improved by reducing the error to less than ±1% while preserving the sensing sensitivity. Full article

(This article belongs to the Special Issue Gas Sensors: Materials, Mechanism and Applications)

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14 pages, 5712 KiB

Open AccessArticle

Resistive, Temperature-Independent Metal Oxide Gas Sensor for Detecting the Oxygen Stoichiometry (Air-Fuel Ratio) of Lean Engine Exhaust Gases

by Carsten Steiner, Simon Püls, Murat Bektas, Andreas Müller, Gunter Hagen and Ralf Moos

Sensors 2023, 23(8), 3914; https://doi.org/10.3390/s23083914 - 12 Apr 2023

Cited by 1 | Viewed by 1580

Abstract

This study presents a resistive sensor concept based on Barium Iron Tantalate (BFT) to measure the oxygen stoichiometry in exhaust gases of combustion processes. The BFT sensor film was deposited on the substrate by the Powder Aerosol Deposition (PAD) method. In initial laboratory experiments, the sensitivity to p_O2 in the gas phase was analyzed. The results agree with the defect chemical model of BFT materials that suggests the formation of holes

h^{•}

by filling oxygen vacancies

V_{O}^{• •}

in the lattice at higher oxygen partial pressures p_O2. The sensor signal was found to be sufficiently accurate and to have low time constants with changing oxygen stoichiometry. Further investigations on reproducibility and cross-sensitivities to typical exhaust gas species (CO₂, H₂O, CO, NO, …) confirmed a robust sensor signal that was hardly affected by other gas components. The sensor concept was also tested in real engine exhausts for the first time. The experimental data showed that the air-fuel ratio can be monitored by measuring the resistance of the sensor element, including partial and full-load operation modes. Furthermore, no signs of inactivation or aging during the test cycles were observed for the sensor film. Overall, a promising first data set was obtained in engine exhausts and therefore the BFT system is a possible cost-effective alternative concept to existing commercial sensors in the future. Moreover, the integration of other sensitive films for multi-gas sensor purposes might be an attractive field for future studies. Full article

(This article belongs to the Special Issue Gas Sensors: Materials, Mechanism and Applications)

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13 pages, 1275 KiB

Open AccessArticle

Kinetics of Chemisorption on the Surface of Nanodispersed SnO₂–PdO_x and Selective Determination of CO and H₂ in Air

by Alexey Vasiliev, Alexey Shaposhnik, Pavel Moskalev and Oleg Kul

Sensors 2023, 23(7), 3730; https://doi.org/10.3390/s23073730 - 04 Apr 2023

Cited by 1 | Viewed by 1071

Abstract

In this work, the kinetics and mechanisms of the interaction of carbon monoxide and hydrogen with the surface of a nanosized SnO₂–PdO_x metal oxide material in air is studied. Non-stationary temperature regimes make it possible to better identify the individual characteristics of target gases and increase the selectivity of the analysis. Recently, chemometric methods (PCA, PLS, ANN, etc.) are often used to interpret multidimensional data obtained in non-stationary temperature regimes, but the analytical solution of kinetic equations can be no less effective. In this regard, we studied the kinetics of the interaction of carbon monoxide and hydrogen with atmospheric oxygen on the surface of SnO₂–PdO_x using semiconductor metal oxide sensors under conditions as close as possible to classical gas analysis. An analysis of the influence of catalytic surface temperature on the mechanisms of chemisorption processes allowed us to correctly interpret and mathematically describe the electrophysical characteristics of the sensor in the selective determination of carbon monoxide and hydrogen under nonstationary temperature conditions. The reaction mechanism is applied as well to the analysis of the operation scheme of the CO sensor TGS 2442 of Figaro Inc. Full article

(This article belongs to the Special Issue Gas Sensors: Materials, Mechanism and Applications)

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14 pages, 43908 KiB

Open AccessArticle

A Bio-Inspired Spiking Neural Network with Few-Shot Class-Incremental Learning for Gas Recognition

by Dexuan Huo, Jilin Zhang, Xinyu Dai, Pingping Zhang, Shumin Zhang, Xiao Yang, Jiachuang Wang, Mengwei Liu, Xuhui Sun and Hong Chen

Sensors 2023, 23(5), 2433; https://doi.org/10.3390/s23052433 - 22 Feb 2023

Cited by 4 | Viewed by 1782

Abstract

The sensitivity and selectivity profiles of gas sensors are always changed by sensor drifting, sensor aging, and the surroundings (e.g., temperature and humidity changes), which lead to a serious decline in gas recognition accuracy or even invalidation. To address this issue, the practical solution is to retrain the network to maintain performance, leveraging its rapid, incremental online learning capacity. In this paper, we develop a bio-inspired spiking neural network (SNN) to recognize nine types of flammable and toxic gases, which supports few-shot class-incremental learning, and can be retrained quickly with a new gas at a low accuracy cost. Compared with gas recognition approaches such as support vector machine (SVM), k-nearest neighbor (KNN), principal component analysis (PCA) +SVM, PCA+KNN, and artificial neural network (ANN), our network achieves the highest accuracy of 98.75% in five-fold cross-validation for identifying nine types of gases, each with five different concentrations. In particular, the proposed network has a 5.09% higher accuracy than that of other gas recognition algorithms, which validates its robustness and effectiveness for real-life fire scenarios. Full article

(This article belongs to the Special Issue Gas Sensors: Materials, Mechanism and Applications)

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