Molecular Insights into Co-Solvent and Water Duality in Amine-Based CO₂ Absorption via Neural Network-Based Atomistic Simulations

Amine-based absorbents dominate industrial CO₂ capture due to their high chemical affinity for CO₂. However, optimizing the composition of amine-based absorbents presents a significant challenge because the role of constituent molecules and their molecular interactions has not yet been fully understood. In this work, we employ universal neural network interatomic potentials to probe how organic solvent characteristics affect molecular solvation shell distribution and eventually the CO₂ absorption kinetics in amine–co-solvent mixtures. Specifically, we analyze nine sets of mixtures containing one of three representative amines monoethanolamine, 3-methylamino propylamine and triethylenetetramine with one of three organic solvents ethylene glycol, 2-ethoxyethanol and 2-butoxyethanol at a fixed concentration of water. The computed results show that compact and polar solvent molecules such as ethylene glycol strengthen and prolong N–CO₂ coordination in the mixture, promoting CO2 absorption. Water exhibits two contrasting effects: it increases the desolvation penalty when clustering around the amine nitrogen by disrupting the CO2 to bond, while stabilizing the transition state and accelerating the absorption by forming a solvation shell when localizing near CO₂. These findings underscore the need to balance solvation strength, molecular mobility, and transition-state stabilization to optimize the CO2 capture performance, while also providing a molecular-level perspective to guide the design of amine–co-solvent systems.