Probing the thermal decomposition mechanism of CF₃SO₂F by deep learning molecular dynamics

Area of Science:

• Materials Science
• Environmental Science
• Chemical Engineering

Background:

• Sulfur hexafluoride (SF6) is a potent greenhouse gas used in electrical grids.
• There is an urgent need for eco-friendly alternatives to SF6.
• Trifluoromethanesulfonyl fluoride (CF3SO2F) shows promise as a sustainable substitute.

Purpose of the Study:

• Investigate the thermal decomposition mechanisms of CF3SO2F.
• Analyze decomposition products and pathways under various conditions.
• Evaluate the environmental feasibility and safety of CF3SO2F.

Main Methods:

• Utilized a deep learning potential for ab initio accuracy and molecular dynamics (MD) efficiency.
• Employed machine learning-driven MD to study decomposition versus temperature, gas ratios, and buffer gas.
• Performed experimental validation using a thermal decomposition platform.

Main Results:

• Decomposition pathways are temperature-dependent.
• Elevated temperatures and higher buffer gas ratios promote decomposition.
• Nitrogen (N2) inhibits decomposition more than carbon dioxide (CO2) at high pressures.

Conclusions:

• CF3SO2F decomposition is influenced by temperature, pressure, and buffer gas composition.
• Findings are critical for the safe deployment of CF3SO2F in power grids.
• This research supports greenhouse gas mitigation in the energy sector.