Machine Learning-Assisted Development of Sensitive Electrode Materials for Mixed Potential-Type NO2 Gas Sensors.

Abstract

Yttrium-stabilized zirconia (YSZ)-based mixed potential-type NOx sensors have broad application prospects in automotive exhaust gas detection. Great efforts continue to be made in developing high-performance sensitive electrode materials for mixed potential-type NO2 gas sensors. However, only five kinds of new sensing electrode materials have been developed for this type of gas sensor in the last 3 years. In this work, four different tree-based machine learning models were trained to find potentially sensitive electrode materials for NO2 detection. More than 400 materials were selected from 8000 materials by the above machine learning models. To further verify the reliability of the model, 13 of these materials containing unexploited elements were selected as sensitive electrode materials for making sensors and testing their gas-sensing performances. The experimental results showed that all 13 materials exhibited good gas-sensing performance for NO2. More interestingly, an electrode material BPO4, which does not contain any metal elements, was also screened out and showed good sensing properties to NO2. In a short period of time, 13 new sensitive electrode materials for NO2 detection were targeted and screened, which was difficult to achieve by a trial-and-error procedure.