Abstract
Nowadays, there is increasing interest in fast, accurate, and highly sensitive smart gas sensors with excellent selectivity boosted by the high demand for environmental safety and healthcare applications. Significant research has been conducted to develop sensors based on novel highly sensitive and selective materials. Computational and experimental studies have been explored in order to identify the key factors in providing the maximum active location for gas molecule adsorption including bandgap tuning through nanostructures, metal/metal oxide catalytic reactions, and nano junction formations. However, there are still great challenges, specifically in terms of selectivity, which raises the need for combining interdisciplinary fields to build smarter and high-performance gas/chemical sensing devices. This review discusses current major gas sensing performance-enhancing methods, their advantages, and limitations, especially in terms of selectivity and long-term stability. The discussion then establishes a case for the use of smart machine learning techniques, which offer effective data processing approaches, for the development of highly selective smart gas sensors. We highlight the effectiveness of static, dynamic, and frequency domain feature extraction techniques. Additionally, cross-validation methods are also covered; in particular, the manipulation of the k-fold cross-validation is discussed to accurately train a model according to the available datasets. We summarize different chemresistive and FET gas sensors and highlight their shortcomings, and then propose the potential of machine learning as a possible and feasible option. The review concludes that machine learning can be very promising in terms of building the future generation of smart, sensitive, and selective sensors.
Highlights
The article is divided into two parts: The first part emphasizes the recent progress in the field of gas sensing, their different performance improvement methods, and current challenges
The second part highlights the significance of machine learning as a potential approach to tackle these limitations
Recent development in smart sensors and breath analyzers using machine learning has been discussed in details
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. For environmental safety and better monitoring of human health, there is an urgent demand for the development of a sensor with trace-level molecule detection, minimum drift, high sensitivity, fast response/recovery, and excellent selectivity under different environments (dry and humid). Optimized (with Pt60 and Ag40 atomic ratio) sensors demonstrated maximum response on an OT of 280 ◦ C with DR varying from 120 ppb to 2 ppm They described how Ag@Pt core−shell NPs play a vital role as a catalyst during the HCHO detection process by dramatically enhancing the oxidation of HCHO molecules on the ZnO (NWs) surface. We analyze the significance of different features (static, dynamic, and frequency domain) extracted from response curves of chemiresistive and field-effect transistor (FET) devices for the unique single point signature marker (dimensionality reduction) generation through principal component analysis (PCA) followed by accurate model training. The review discusses recent progress in building highly selective chemiresistive and FET gas sensors and breath analyzers using machine learning
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