High-Throughput Screening of Gas Sensor Materials for Decomposition Products of Eco-Friendly Insulation Medium by Machine Learning

Nowadays, trifluoromethyl sulfonyl fluoride (CF₃SO₂F) has shown great potential to replace SF₆ as an eco-friendly insulation medium in the power industry. In this work, an effective and low-cost design strategy toward ideal gas sensors for the decomposed gas products of CF₃SO₂F was proposed. The strategy achieved high-throughput screening from a large candidate space based on first-principle calculation and machine learning (ML). The candidate space is made up of different transition metal-embedded graphic carbon nitrides (TM/g-C₃N₄) owing to their high surface area and subtle electronic structure. Four main noteworthy decomposition gases of CF₃SO₂F, namely, CF₄, SO₂, SO₂F₂, and HF, as well as their initial stable structure on TM/g-C₃N₄ were determined. The best-performing ML model was established and implemented to predict the interaction strength between gas products and TM/g-C₃N₄, thus determining the promising gas-sensing materials for target gases with the requirements of interaction strength, recovery time, sensitivity, and selectivity. Further analysis guarantees their stability and reveals the origin of excellent properties as a gas sensor. The high-throughput strategy opens a new avenue of rational and low-cost design principles of desirable gas-sensing materials in an interdisciplinary view.