DFT Investigation on the Adsorption and Sensing Mechanisms of Transformer Fault Gases over MOF-Based Cu-BTC and ZIF-8

Real-time detection of the six characteristic gases dissolved in transformer oil (H2, CO, CO2, CH4, C2H4, C2H2), present from sub-ppm levels for C2H2 to several thousand ppm for CO2, is essential for early fault diagnosis in oil-immersed power equipment, yet conventional metal-oxide sensors require elevated operating temperatures and suffer from poor selectivity. Density functional theory is used here to evaluate two metal–organic frameworks, Cu-BTC and ZIF-8, as room-temperature sensing candidates for these gases. Adsorption energy, charge transfer, DOS, work-function variation and temperature-dependent recovery time are analyzed for all twelve systems. Cu-BTC exhibits the strongest response to CO (adsorption energy: −1.745 eV, charge transfer: 0.426 e) with pronounced C-2p/Cu-3d hybridization, whereas ZIF-8 shows the strongest affinity toward C2H2 (adsorption energy: −1.986 eV); the two frameworks thus display complementary selectivity. Weakly bound systems desorb within microseconds to seconds at room temperature, enabling rapid regeneration, while CO on Cu-BTC and C2H2 on ZIF-8 are retained almost irreversibly and suit gas capture better. These results provide a theoretical basis for MOF-based on-line monitoring of dissolved gases in transformer oil and power-equipment fault diagnosis.