Probing carbon capture, atom-by-atom

A team of scientists at LLNL has developed a machine-learning model to gain an atomic-level understanding of CO₂ capture in amine-based sorbents. This innovative approach promises to enhance the efficiency of direct air capture (DAC) technologies, which are crucial for reducing the excessive amounts of CO₂ already present in the atmosphere. The low cost of these sorbents has enabled several companies to scale up this technology, demonstrating DAC as a feasible way to combat global warming. However, significant knowledge gaps remain in the chemistry of CO₂ capture under experimentally relevant conditions. The team’s machine learning model has revealed that CO₂ capture by amines involves the formation of a carbon-nitrogen chemical bond between the amino group and CO₂, alongside a complex set of solvent-mediated proton transfer reactions. These proton transfer reactions are critical for the formation of the most stable CO₂-bound species and are significantly influenced by quantum fluctuations of protons. “By integrating machine learning with advanced simulation techniques, we’ve created a powerful approach that bridges theoretical predictions and experimental validations of CO₂-capture mechanisms in a way not accessible by traditional simulation techniques,” said LLNL scientist Sichi Li, co-corresponding author and project theory lead. Read more at LLNL News.