Computational Screening Guiding the Development of a Covalent-Organic Framework-Based Gas Sensor for Early Detection of Lithium-Ion Battery Electrolyte Leakage.

This study presents a computationally guided approach for selecting covalent organic frameworks (COFs) for the selective detection of the trace ethylene carbonate (EC) vapor, a key indicator of electrolyte leakage from lithium-ion batteries (LIBs). High-throughput screening, employing grand canonical Monte Carlo (GCMC) simulation complemented by density functional theory (DFT) calculations, was used to identify potential COF candidates from the CURATED COF database. Among the screened materials, an imine COF functionalized with quaternary ammonium (QA) groups, named COF-QA-4, exhibited a high adsorption capacity (5.88 mmol/g) and selectivity of EC vapor. DFT analysis revealed strong molecular interactions driven by a partial charge transfer mechanism between EC and the COF-QA-4 framework, underpinning its superior adsorption properties. Experimental validation through chemiresistive gas sensors fabricated with COF-QA-4 demonstrated excellent sensitivity and reversibility to 1.15 ppmv of EC vapor, maintaining consistent performance over three response-recovery cycles. This work highlights the potential of computationally guided material discovery for advancing sensor technologies in LIB safety monitoring.