Application of Semiconductor Metal Oxide in Chemiresistive Methane Gas Sensor: Recent Developments and Future Perspectives

Abstract

The application of semiconductor metal oxides in chemiresistive methane gas sensors has seen significant progress in recent years, driven by their promising sensitivity, miniaturization potential, and cost-effectiveness. This paper presents a comprehensive review of recent developments and future perspectives in this field. The main findings highlight the advancements in material science, sensor fabrication techniques, and integration methods that have led to enhanced methane-sensing capabilities. Notably, the incorporation of noble metal dopants, nanostructuring, and hybrid materials has significantly improved sensitivity and selectivity. Furthermore, innovative sensor fabrication techniques, such as thin-film deposition and screen printing, have enabled cost-effective and scalable production. The challenges and limitations facing metal oxide-based methane sensors were identified, including issues with sensitivity, selectivity, operating temperature, long-term stability, and response times. To address these challenges, advanced material science techniques were explored, leading to novel metal oxide materials with unique properties. Design improvements, such as integrated heating elements for precise temperature control, were investigated to enhance sensor stability. Additionally, data processing algorithms and machine learning methods were employed to improve selectivity and mitigate baseline drift. The recent developments in semiconductor metal oxide-based chemiresistive methane gas sensors show promising potential for practical applications. The improvements in sensitivity, selectivity, and stability achieved through material innovations and design modifications pave the way for real-world deployment. The integration of machine learning and data processing techniques further enhances the reliability and accuracy of methane detection. However, challenges remain, and future research should focus on overcoming the limitations to fully unlock the capabilities of these sensors. Green manufacturing practices should also be explored to align with increasing environmental consciousness. Overall, the advances in this field open up new opportunities for efficient methane monitoring, leak prevention, and environmental protection.

Introduction and Advancements in Room-Temperature Ferromagnetic Metal Oxide Semiconductors for Enhanced Photocatalytic Performance

“…Significance of N-Type and P-Type MOS Models: Recent research underscores the significance of the n-type and p-type MOS models in various applications [41]. The distinct electronic characteristics of these models offer diverse avenues for tailoring material responses.…”

Mn Doped ZnO Film for Ethanol Vapor Detection

“…This device was very sensitive even for a small amount of gas and the measurements were reproducible. The sensitivity of the gas sensor was evaluated using the following relation [1] where, R a is the resistance of the sensor in the air and R g is the resistance in the presence of the gas.…”

“…The rapid development of industrial civilization requires monitoring of harmful gases in order to protect human health and the environment. For this purpose, metal oxide semiconductors such as ZnO have gained much research interest [1]. Much effort has been devoted to develop highly sensitive, selective, fast responsive, and fully recyclable gas sensors for the detection of toxic and explosive gases including ethanol [2,3].…”

Calibration of a Low-Cost Methane Sensor Using Machine Learning

“…Many categories of methane sensors have been developed: optical, capacitance-based, calorimetric, resonant, acoustic-based, pyroelectric, metal oxide semiconductor (MOS), and electrochemical [ 1 , 10 , 11 ]. Of these types, MOS sensors show particular potential for compact, low-power and low-cost applications, but additional steps must be taken to improve their performance in outdoor settings [ 10 , 12 , 13 , 14 , 15 , 16 ].…”

“…Machine learning techniques are often employed to improve the usability of gas sensor data, by addressing either the selectivity of the sensor or the calibration accuracy [ 11 , 13 , 17 ]. Classification algorithms, such as support vector machines and neural networks, have been shown to improve the ability of both individual sensors and sensor arrays to identify specific target gases or gas mixtures [ 18 , 19 , 20 , 21 , 22 , 23 ].…”