Machine learning demonstrates the impact of proton transfer and solvent dynamics on CO2 capture in liquid ammonia

• Published in: Chemical science (Cambridge) Vol. 15; no. 33; pp. 13173 - 13180
• Main Authors: Calegari Andrade, Marcos F, Li, Sichi, Pham, Tuan Anh, Akhade, Sneha A, Pang, Simon H
• Format: Journal Article
• Language: English
• Published: Cambridge Royal Society of Chemistry 22.08.2024; The Royal Society of Chemistry
• Subjects: Amines; Ammonia; Carbon dioxide; Carbon sequestration; Chemisorption; Chemistry; Emissions; Greenhouse gases; Liquid ammonia; Machine learning; Monte Carlo simulation; Protons; Solvents
• ISSN: 2041-6520; 2041-6539
• DOI: 10.1039/d4sc00105b

Direct air capture of CO2 using supported amines provides a promising means to achieve the net-zero greenhouse gas emissions goal; however, many mechanistic details regarding the CO2 adsorption process in condensed phase amines remain poorly understood. This work combines machine learning potentials, enhanced sampling and grand-canonical Monte Carlo simulations to directly compute experimentally relevant quantities to elucidate the mechanism of CO2 chemisorption in liquid ammonia as a model system. Our simulations suggest that CO2 capture in the liquid occurs in a sequential fashion, with the formation of a metastable zwitterion intermediate. Furthermore, we identified the importance of solvent-mediated proton transfer and solvent dynamics, not only in the reaction pathway but also in the efficiency of CO2 chemisorption. Beyond liquid ammonia, the methodology presented here can be readily extended to simulate amines with more complex chemical structures under experimental conditions, paving the way to elucidate the structure–performance of amines for CO2 capture.