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Machine learning demonstrates the impact of proton transfer and solvent dynamics on CO 2 capture in liquid ammonia.

Marcos F Calegari AndradeSichi LiTuan Anh PhamSneha A AkhadeSimon H Pang
Published in: Chemical science (2024)
Direct air capture of CO 2 using supported amines provides a promising means to achieve the net-zero greenhouse gas emissions goal; however, many mechanistic details regarding the CO 2 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 CO 2 chemisorption in liquid ammonia as a model system. Our simulations suggest that CO 2 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 CO 2 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 CO 2 capture.
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