Multivariate Evaluation Method for Screening Optimum Gas-Sensitive Materials for Detecting SF₆ Decomposition Products

Area of Science:

• Materials Science
• Chemical Sensing
• Analytical Chemistry

Background:

• Traditional gas sensor selection often overlooks recovery capability, focusing primarily on response value.
• Effective detection of sulfur hexafluoride (SF6) decomposition products requires comprehensive performance evaluation of gas-sensing materials.

Purpose of the Study:

• To develop and validate a multivariate method for evaluating gas sensor effectiveness beyond simple response values.
• To quantitatively identify optimal working temperatures and materials for SF6 decomposition product detection.

Main Methods:

• Investigated four metal-oxide semiconductor sensors (WO3, CeO2, In2O3, SnO2) for SF6 decomposition product detection.
• Employed a multivariate evaluation combining principal component analysis, information entropy, and variation coefficient.
• Utilized five variables: working temperature, response value, recovery capability, fluctuation rate, and detection limit.

Main Results:

• Quantitatively determined optimal working temperatures for the gas sensors.
• Calculated weights for various performance indices, enabling comprehensive sensor scoring.
• Identified the most effective metal-oxide semiconductor materials for detecting SF6 decomposition products based on integrated scores.

Conclusions:

• The proposed multivariate evaluation method provides a quantitative approach to select optimal gas sensors.
• This methodology enhances sensor selection by incorporating critical indices like recovery capability.
• The approach is adaptable for selecting optimal sensors for detecting various other gases.