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<p>Methane(CH<sub>4</sub>), as a widely used clean energy source and industrial feedstock, poses both flammable and explosive safety risks while exhibiting a strong greenhouse effect. Its precise detection is crucial for industrial safety and environmental monitoring. Semiconductor metal oxides, favored for their low cost, fast response, and strong process compatibility, serve as the core sensing material for CH<sub>4</sub> sensors. However, traditional devices rely on high-temperature operation, leading to high energy consumption and poor low-temperature adaptability. Current research employs strategies such as noble metal doping, morphology control, heterostructure construction, and visible light activation to effectively reduce operating temperatures, with some achieving near-room-temperature operation. Through multi-method synergistic optimization, these approaches enhance response performance using light energy and doping while improving selectivity to reduce interference, thereby establishing high-performance composite sensing systems. Future efforts should focus on further “temperature reduction and efficiency enhancement” while overcoming high-humidity challenges. Additionally, integrating artificial intelligence with sensing systems—utilizing AI algorithms to model sensor data—addresses issues such as poor selectivity and signal drift. Combining machine learning models has improved sensor performance, expanded application scenarios in complex environments, and driven innovation in sensing technology.</p>

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Semiconductor metal oxide-based methane sensors

  • 期刊论文

  • 发表日期：

    2026年3月6日

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  • Qiao Wang,
  • Xinyi Ren,
  • Shuqing Li,
  • Haiyang Cai,
  • Xiangjun Chen,
  • JunJie Wang

摘要

Methane(CH4), as a widely used clean energy source and industrial feedstock, poses both flammable and explosive safety risks while exhibiting a strong greenhouse effect. Its precise detection is crucial for industrial safety and environmental monitoring. Semiconductor metal oxides, favored for their low cost, fast response, and strong process compatibility, serve as the core sensing material for CH4 sensors. However, traditional devices rely on high-temperature operation, leading to high energy consumption and poor low-temperature adaptability. Current research employs strategies such as noble metal doping, morphology control, heterostructure construction, and visible light activation to effectively reduce operating temperatures, with some achieving near-room-temperature operation. Through multi-method synergistic optimization, these approaches enhance response performance using light energy and doping while improving selectivity to reduce interference, thereby establishing high-performance composite sensing systems. Future efforts should focus on further “temperature reduction and efficiency enhancement” while overcoming high-humidity challenges. Additionally, integrating artificial intelligence with sensing systems—utilizing AI algorithms to model sensor data—addresses issues such as poor selectivity and signal drift. Combining machine learning models has improved sensor performance, expanded application scenarios in complex environments, and driven innovation in sensing technology.