Scientists at Lawrence Livermore National Laboratory have developed a machine-learning model to explore carbon dioxide (CO₂) capture at an atomic level in amine-based sorbents. This model improves the efficiency of direct air capture technologies, essential for reducing atmospheric CO₂. The research highlights how CO₂ binds with amines, involving complex solvent-mediated proton transfer reactions, significantly influenced by quantum proton fluctuations. This advancement bridges theoretical predictions with experimental validations, aiding the design of next-generation materials for achieving net-zero greenhouse gas emissions.
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