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Understanding Enhanced Aniline Sensing with Cu-Doped SnO2 via GC-MS Analysis.

Kuan Tian1, Wei Zhao1, Zhenxing Li1,2

  • 1Department of Material and Chemical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China.

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|March 17, 2026
PubMed
Summary

Copper-doped tin oxide nanoparticles catalyze aniline oxidation, enhancing gas sensor performance. This study links reaction rates to sensor response, enabling optimized design for detecting volatile organic compounds (VOCs).

Keywords:
Cu dopingGC−MSSnO2 nanoparticlesaniline sensorgas−solid reactionsensing mechanism

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Analytical Chemistry

Background:

  • Rational design of metal oxide gas sensors is hindered by poor understanding of gas-solid interactions.
  • Investigating surface reactions is crucial for developing high-performance sensors.

Purpose of the Study:

  • To dynamically investigate the surface reaction between aniline vapor and Cu-doped SnO2 nanoparticles.
  • To establish a correlation between gas-solid reaction mechanisms and gas sensor performance.
  • To rationally optimize Cu doping for enhanced aniline sensing.

Main Methods:

  • Headspace gas chromatography-mass spectrometry (GC-MS) was used for dynamic surface reaction analysis.
  • Cu-doped SnO2 nanoparticles were synthesized and characterized.
  • Gas sensing performance (response, detection limit, response time, selectivity) was evaluated.

Main Results:

  • GC-MS revealed Cu doping facilitates aniline oxidation to azobenzene, a reaction absent in pristine SnO2.
  • A positive correlation was found between aniline oxidation rate and sensor response value.
  • Optimized Cu doping in SnO2 resulted in a sensor with high response (4.5@10 ppm), low detection limit (80 ppb), and rapid response (~25s).

Conclusions:

  • Cu-doped SnO2 exhibits enhanced selectivity and response to aniline due to facilitated oxidation.
  • The study provides a quantitative method to link reaction kinetics with sensor performance for rational sensor design.
  • This approach supports the development of advanced metal-catalyzed sensors for selective VOC detection.