A Review on Topical Advancement and Challenges of Indium Oxide Based Gas Sensors: Future Outlooks

纳米技术 氧化物 材料科学 光电子学 冶金
作者
Katlego L. Morulane,H.C. Swart,David E. Motaung
出处
期刊:Journal of environmental chemical engineering [Elsevier]
卷期号:: 112144-112144
标识
DOI:10.1016/j.jece.2024.112144
摘要

The advancement of industry and the hastening of urbanization have led to serious problems associated with the emanation of various harmful and lethal gases, which are a threat to humankind. Therefore, the fabrication of highly sensitive and selective sensors for monitoring these gases is desirable. Due to its stability, sensitivity, and selectivity in the general environment, indium oxide (In2O3) has attracted considerable attention for gas sensing. Thus, the current review discusses the latest research made over the last five years on In2O3-based gas sensors. The influence of environmental pollutants, such as VOCs, NOX, CO, O3, NH3, and SO2 on the environment and human life were discussed. Among the synthesis methods, the hydrothermal approach has been the most used for the preparation of sensors based on In2O3. The drawbacks and challenges linked with the In2O3 sensors for the last five (5) years, showed that most of the sensors have been functioning at higher temperatures. This has led to a lack of commercialization because of reliability issues associated with the long-term stability drift. So far, noble metals doped-In2O3 sensors have shown promising advantages for room temperature operation, which could be useful for reaching the next generation of intelligent gas sensors from micro/nanomaterials to the era of self-powered, artificially intelligent system and their integration on smartphones. Thus, In2O3 gas sensors with Bluetooth modules can make it feasible to wirelessly monitor the concentration of different gases without the need for an external power source and transmit information via a smartphone. This would further be suitable in wireless signal detection, the Internet of Things, in data processing, particularly in machine learning, to aid in gas sensing.
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