4.7 Article

Detection of four alcohol homologue gases by ZnO gas sensor in dynamic interval temperature modulation mode

Journal

SENSORS AND ACTUATORS B-CHEMICAL
Volume 350, Issue -, Pages -

Publisher

ELSEVIER SCIENCE SA
DOI: 10.1016/j.snb.2021.130867

Keywords

Four alcohol homologue gases; Dynamic interval temperature modulation mode; Triangular wave; Gas sensor

Funding

  1. National Key R&D Program of China [2020YFB2008702]
  2. National Natural Science Foundation of China [62033002, 61833006, 62071112, 61973058]
  3. Fundamental Research Funds for the Central Universities in China [N2004019, N2004028]
  4. 111 Project [B16009]
  5. Liao Ning Revitalization Talents Program [XLYC1807198]
  6. Liaoning Province Natural Science Foundation [2020-KF-11-04]
  7. Hebei Natural Science Foundation [F2020501040]

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This paper successfully detected four alcohol homologue gases using a ZnO gas sensor with dynamic temperature modulation method, achieving a recognition accuracy of 97.62% after optimization with decision tree classification algorithm.
Metal oxide semiconductor (MOS) gas sensors have poor selectivity, especially in volatile organic compounds (VOCs). Dynamic measurement method of gas sensors can bring a potential to resolve this problem. However, it is also a challenge to distinguish homologue gases with the same functional group. In this paper, four alcohol homologue gases were detected by a ZnO gas sensor. The period of triangular wave is changed to explore the optimal temperature range in dynamic interval temperature modulation mode for detecting ethyl alcohol, n-propyl alcohol, isopropyl alcohol and n-butyl alcohol. The experimental results show that only when the low temperature is lower than 180 degrees C and the high temperature is higher than 460 degrees C, the unsaturation phenomenon can be solved, so as to realize the qualitative and quantitative analysis of the four alcohol homologue gases. Under this experimental condition, the concentration gradient measurement of 100-400 ppm is carried out. After the optimization with the decision tree classification algorithm, the recognition accuracy of the four alcohol homologue gases is 97.62%.

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