Innovations in WO3 gas sensors: Nanostructure engineering, functionalization, and future perspectives

表面改性 纳米技术 材料科学 石墨烯 表面工程 纳米结构 软件可移植性 氧化物 计算机科学 机械工程 工程类 冶金 程序设计语言
作者
Xingxing Li,Li Fu,Hassan Karimi‐Maleh,Fei Chen,Shichao Zhao
出处
期刊:Heliyon [Elsevier BV]
卷期号:10 (6): e27740-e27740 被引量:79
标识
DOI:10.1016/j.heliyon.2024.e27740
摘要

This review critically examines the progress and challenges in the field of nanostructured tungsten oxide (WO3) gas sensors. It delves into the significant advancements achieved through nanostructuring and composite formation of WO3, which have markedly improved sensor sensitivity for gases like NO2, NH3, and VOCs, achieving detection limits in the ppb range. The review systematically explores various innovative approaches, such as doping WO3 with transition metals, creating heterojunctions with materials like CuO and graphene, and employing machine learning models to optimize sensor configurations. The challenges facing WO3 sensors are also thoroughly examined. Key issues include cross-sensitivity to different gases, particularly at higher temperatures, and long-term stability affected by factors like grain growth and volatility of dopants. The review assesses potential solutions to these challenges, including statistical analysis of sensor arrays, surface functionalization, and the use of novel nanostructures for enhanced performance and selectivity. In addition, the review discusses the impact of ambient humidity on sensor performance and the current strategies to mitigate it, such as composite materials with humidity shielding effects and surface functionalization with hydrophobic groups. The need for high operating temperatures, leading to higher power consumption, is also addressed, along with possible solutions like the use of advanced materials and new transduction principles to lower temperature requirements. The review concludes by highlighting the necessity for a multidisciplinary approach in future research. This approach should combine materials synthesis, device engineering, and data science to develop the next generation of WO3 sensors with enhanced sensitivity, ultrafast response rates, and improved portability. The integration of machine learning and IoT connectivity is posited as a key driver for new applications in areas like personal exposure monitoring, wearable diagnostics, and smart city networks, underlining WO3's potential as a robust gas sensing material in future technological advancements.
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