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<p>This review summarizes recent advances in molybdenum trioxide (MoO<sub>3</sub>) nanostructured gas sensors, focusing on three key strategies for performance enhancement: (i) morphology engineering for sensitivity optimization, e.g., nanobelts achieving a response of 49 to 5 ppm H₂S at 250&#xa0;°C; (ii) heterojunction engineering for selectivity, e.g., MoO<sub>3</sub>/ZnO showing ten-fold higher selectivity for NO₂ over CO at 100 ppm; and (iii) heterojunction engineering for environmental robustness, e.g., MoO3/rGO (reduced graphene oxide, a highly conductive carbon-based material) exhibiting a response of 843 to 100 ppm ethylenediamine at room temperature (23&#xa0;°C). The review also discusses synthesis methods, gas adsorption mechanisms, and critical challenges such as humidity interference (10–20% sensitivity reduction at 50% relative humidity) and cross-sensitivity. By integrating recent findings into a structured framework, we highlight research gaps and suggest future directions toward low-power, highly selective sensors, including artificial intelligence-assisted designs for real-world applications.</p>

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Emerging strategies in MoO3 nanostructured gas sensors: a review of morphology engineering and hybrid interface design

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月24日

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  • A. H. Farahani,
  • R. Zarei Moghadam

摘要

This review summarizes recent advances in molybdenum trioxide (MoO3) nanostructured gas sensors, focusing on three key strategies for performance enhancement: (i) morphology engineering for sensitivity optimization, e.g., nanobelts achieving a response of 49 to 5 ppm H₂S at 250 °C; (ii) heterojunction engineering for selectivity, e.g., MoO3/ZnO showing ten-fold higher selectivity for NO₂ over CO at 100 ppm; and (iii) heterojunction engineering for environmental robustness, e.g., MoO3/rGO (reduced graphene oxide, a highly conductive carbon-based material) exhibiting a response of 843 to 100 ppm ethylenediamine at room temperature (23 °C). The review also discusses synthesis methods, gas adsorption mechanisms, and critical challenges such as humidity interference (10–20% sensitivity reduction at 50% relative humidity) and cross-sensitivity. By integrating recent findings into a structured framework, we highlight research gaps and suggest future directions toward low-power, highly selective sensors, including artificial intelligence-assisted designs for real-world applications.