A significant breakthrough in the pursuit of direct air capture (DAC) of carbon dioxide has been achieved by scientists at the Department of Energy's Oak Ridge National Laboratory. Published in Cell Reports Physical Science, the research delves into the foundational aspects of carbon dioxide sequestration using aqueous glycine, an absorbent amino acid. The goal of DAC is to achieve negative emissions, surpassing the amount of carbon dioxide emitted into Earth's atmosphere. The study utilized advanced computational methods to explore dynamic phenomena in liquid solutions, emphasizing the importance of understanding nonequilibrium solvent effects in complex chemical reactions.
Santanu Roy, involved in designing the computational investigation alongside colleague Vyacheslav Bryantsev, highlighted the complexity of chemical reactions in water, particularly when water molecules' motion significantly influences the process. By adopting a more comprehensive approach that considers the impact of water on reaction paths, the researchers discovered that focusing solely on the free energy barrier oversimplifies the understanding of carbon dioxide absorption rates.
Bryantsev noted, “The initial step, where glycine interacts with carbon dioxide, is nearly 800 times slower compared with the next step, where a proton is released to ultimately form a mixture of product state for holding the absorbed carbon dioxide.” The constant free energy barrier for both steps, despite the significant difference in speed, offers insights into enhancing the efficiency of carbon dioxide absorption and separation.
While the study's molecular dynamics simulations had limitations in time and length scales, the researchers plan to integrate machine-learning approaches with accurate simulations to address computational challenges. Xinyou Ma, responsible for the simulations, emphasized the intention to utilize deep neural networks for molecular simulations with high accuracy at larger scales and reduced computational costs.
Roy highlighted the importance of understanding macroscopic factors such as temperature, pressure, and viscosity on DAC, envisioning a comprehensive understanding facilitated by the integration of machine-learning approaches. The study's revelations regarding the kinetics, thermodynamics, and molecular interactions in DAC using aqueous amino acids contribute valuable insights, bringing large-scale DAC technology closer to reality. Globally, various DAC projects are progressing through different stages of research, testing, and development.
Source: Oak Ridge National Laboratory