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Related Experiment Video

Updated: Jun 4, 2026

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing
10:42

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing

Published on: March 22, 2019

From Empirical Ratio Tuning to Mechanistic Insight: Decoding NiO-ZnO Heterojunction Effects in Gas Sensing via

Haixia Mei1, Jingyi Peng1, Jiaqi Zhu2

  • 1Key Lab Intelligent Rehabil & Barrier free Disable (Ministry of Education), Changchun University, Changchun 130022, China.

ACS Sensors
|June 2, 2026
PubMed
Summary
This summary is machine-generated.

The NiO-ZnO composite ratio non-monotonically affects gas sensor performance. Optimal sensing and recognition were achieved with a specific ratio (0.75), revealing a mechanism-driven design approach for metal oxide semiconductor gas sensors.

Keywords:
P−n heterojunctionSHAPdeep learninggas sensormultitask learning

Related Experiment Videos

Last Updated: Jun 4, 2026

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing
10:42

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing

Published on: March 22, 2019

Area of Science:

  • Materials Science
  • Chemical Sensing
  • Artificial Intelligence

Background:

  • P-n heterostructures enhance metal oxide semiconductor gas sensor performance.
  • The precise role of NiO-ZnO composite ratios in gas sensing remains unclear.

Purpose of the Study:

  • Investigate the influence of NiO-ZnO molar ratios on gas sensing and recognition.
  • Optimize composite gas sensor design using a deep learning framework.

Main Methods:

  • Fabricated seven NiO-ZnO sensors with varied molar ratios.
  • Conducted sensing experiments for six volatile organic compounds.
  • Employed a deep learning multitask framework and explainable AI (SHapley Additive exPlanations) for analysis.

Main Results:

  • Sensing performance showed a non-monotonic dependence on the NiO-ZnO ratio.
  • A molar ratio of 0.75 demonstrated superior gas classification and concentration regression.
  • Optimal ratio range (0.6-0.8) improved feature separability and recognition accuracy.

Conclusions:

  • Established a link between NiO-ZnO composition, response features, and gas recognition.
  • Demonstrated a mechanism-driven approach for optimizing composite gas sensors beyond empirical tuning.