A Smart Gas Sensor Using Machine Learning Algorithms: Sensor Types Based on IED Configurations, Fabrication Techniques, Algorithmic Approaches, Challenges, Progress, and Limitations: A Review

Abstract

Over the past decade, Machine Learning and Artificial Intelligence have attracted great interest in research and various practical applications. Currently, smart, fast, highly sensitive with excellent selectivity are becoming increasingly interesting due to the high need for environmental safety and medical applications. The main challenge is to improve sensor selectivity, which requires the combination of interdisciplinary research areas to successfully develop smart gas/chemical sensing devices with better performance. In this review, we present a few principles of gas sensing based on low-cost interdigitated electrodes (IDE), such as electrochemical, resistive, capacitive and acoustic sensors. In addition, the most important current methods for improving gas sensing performance, the different materials, the different techniques used to fabricate IDE gas sensors, their advantages and limitations are presented. In addition, a comparison between different machine learning and artificial intelligence algorithms for pattern recognition and classification algorithms is also discussed. The discussion then establishes application cases of smart machine learning algorithms, which provide efficient data processing methods, for the design of smart gas sensors which are highly selective. In addition, the challenges and limitations of machine learning in gas sensor applications are critically discussed. The study shows the importance of machine learning with the need for structural optimization to develop and improve smart, sensitive and selective sensors.