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<p>Gas sensors with fast response are in high demand for environmental and health applications. Conventional solid-state sensing materials are inherently constrained by response delays arising from chemical bond transformations, posing significant challenges in overcoming response time lag. In this work, inspired by the alveoli of our respiratory system, we develop a triboelectric nanogenerator probe sensor driven by the formation of water droplets containing an air cavity, which incorporates NH<sub>3</sub> molecules. The sensor achieves rapid response through instantaneous electron transfer at the liquid-solid interface, bypassing the need for gas adsorption and desorption on the surface of solid-state sensing materials. This hydro-electrochemical sensing mechanism achieves a response time of 1.4 s, surpassing that of most reported ammonia sensors. Through the integration of deep learning algorithms for optimization, the sensor demonstrates an ammonia detection accuracy of 96.2%. This study indicates a promising strategy for the rational design of gas sensors based on hydro-electrochemical sensing mechanisms.</p>

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Bioinspired triboelectric droplet sensor for ammonia monitoring

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月29日

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  • Tao Liu,
  • Xuedi Li,
  • Huanjie He,
  • Kang Yu,
  • Song Zhang,
  • Ziyi Ye,
  • Xue Cui,
  • Bin Luo,
  • Yanhua Liu,
  • Mingchao Chi,
  • Jinlong Wang,
  • Chenchen Cai,
  • Shuangfei Wang,
  • Shuangxi Nie

摘要

Gas sensors with fast response are in high demand for environmental and health applications. Conventional solid-state sensing materials are inherently constrained by response delays arising from chemical bond transformations, posing significant challenges in overcoming response time lag. In this work, inspired by the alveoli of our respiratory system, we develop a triboelectric nanogenerator probe sensor driven by the formation of water droplets containing an air cavity, which incorporates NH3 molecules. The sensor achieves rapid response through instantaneous electron transfer at the liquid-solid interface, bypassing the need for gas adsorption and desorption on the surface of solid-state sensing materials. This hydro-electrochemical sensing mechanism achieves a response time of 1.4 s, surpassing that of most reported ammonia sensors. Through the integration of deep learning algorithms for optimization, the sensor demonstrates an ammonia detection accuracy of 96.2%. This study indicates a promising strategy for the rational design of gas sensors based on hydro-electrochemical sensing mechanisms.