Solvent Effects on CO 2 Capture by Simple Amino Acids: An Integrated Density Functional Theory -Machine Learning Approach

Abstract

CO 2 capture by amino acids offers promising approach for carbon capture technologies, yet the influence of molecular structure and solvent environment on reaction mechanisms remains least understood. The present study investigates CO 2 capture by glycine, alanine, and serine anions across five environments (gas phase, water, DMSO, glycerol and lactic acid) using density functional theory with implicit solvation. The reaction proceeds via barrierless nucleophilic attack forming a zwitterionic intermediate followed by rate-determining intramolecular proton transfer. Glycerol emerges as the optimal medium exhibiting highly exothermic reaction enthalpies (-50.8 to -53.7 kcal/mol) and stabilized transition states below reactant energy levels, due to extensive hydrogen bonding networks. Structural variations reveal a kineticthermodynamic trade-off in which glycine shows most favorable gas-phase thermodynamics (-21.4 kcal/mol) and lowest barriers (+19.4 kcal/mol), while alanine methyl group introduces steric hindrance and serine hydroxymethyl substituent creates complex solvent-dependent behavior including endothermic reaction in DMSO (+0.4 kcal/mol) from over-stabilization of the serine-DMSO complex. A correlation analysis among the key parameters reveals that CO 2 loading capacity negatively correlates with amino acid hydrogen bond donors (r = -0.59), explaining serine suppressed aqueous activity. Machine learning analysis (Gradient Boosting Regression, R² = 0.85) identifies a molecular weight threshold (~105 g/mol) where side-chain complexity dominates reactivity and demonstrates that solvent hydrogen bond donating capability rather than dielectric constant critically governing capture efficiency. These findings establish glycerol-based formulations with glycine or alanine as superior candidates for industrial CO 2 capture (ΔG 298 = -39 to -43 kcal/mol), highlighting strategic solvent selection for designing tunable amino acid-based carbon capture.