A descriptor‐driven predictive model for bifunctional amine promoters in <scp> CO ₂ </scp> capture: Mechanism and validation

Abstract To overcome the energy penalty of monoethanolamine (MEA) regeneration, a rational strategy for designing trace bifunctional promoters is needed. Here, we introduce a simple yet predictive performance‐gain function (PGF) model, based on three molecular descriptors (proton affinity, hydration free energy, and polarizability). With N‐ethylmorpholine (NEM) as a primary example, adding only 3 wt% to 3 M MEA enhances the CO 2 desorption rate by 48.3% and reduces regeneration energy to 3.24 GJ/t CO 2 while preserving absorption. 13 C NMR and DFT calculations reveal NEM's dual‐function mechanism: facilitating proton transfer in carbamate breakdown and enriching at the gas–liquid interface to enhance mass transfer. Crucially, the PGF model successfully predicted the performance ranking of five additional amine promoters, identifying 1‐ethylpiperazine (1EPRZ) as more effective than NEM (R 2 = 0.936). This study provides a descriptor‐based framework for the rapid screening and rational design of high‐performance additives for energy‐efficient amine‐based CO 2 capture.