US team makes advance for direct air capture

One approach to DAC is to utilise liquid solvents and solid sorbents to chemically absorb CO2 from air streams that can later be processed to desorb and recover CO2.

Recently published research from a team at the US Department of Energy’s Oak Ridge National Laboratory (ORNL) focused on the foundational steps of carbon dioxide sequestration using aqueous glycine, an amino acid with absorbent qualities. By combining a series of advanced computational methods, the scientists investigated less-explored dynamic phenomena in liquid solutions related to the rate at which carbon dioxide can be captured. 

“Chemical reactions in water are complicated, especially when the motion of water molecules plays a big role,” said Santanu Roy, who designed the computational investigation with colleague Vyacheslav Bryantsev. “Understanding these dynamic interactions, known as nonequilibrium solvent effects, is essential to getting the full picture of how reactions work and how fast they happen.”

The researchers found that when examining the rate at which carbon dioxide is absorbed, focusing on the free energy barrier - the energy threshold that must be overcome for a system to transition from one state to another - is an oversimplification and incomplete approach that can lead to an inaccurate understanding of reaction kinetics.

“We employed a more complete approach that considers the influence of water on the motion along the reaction path, and the outcome was intriguing,” Bryantsev said in a statement. “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. Strikingly, the free energy barrier remains constant for both steps, and so this different perspective truly sets the speed of these two critical stages apart and offers a pathway to boost the efficiency of carbon dioxide absorption and separation.”

According to ORNL, the ab initio molecular dynamics simulations used in the study were limited by their short time and length scales and high computational costs in representing the chemical reactions.

“For future projects, we intend to combine the emerging machine-learning approach with highly accurate simulations and develop interatomic interaction potentials based on deep neural networks. This will allow us to perform molecular simulations with high accuracy at large scales with significantly reduced computational costs,” said Xinyou Ma, who carried out the simulations.

“While we have portrayed a molecular-level kinetics picture of carbon dioxide capture by aqueous amino acids, accessing large length and time scales through the use of the machine-learning approach will help us understand the effects of macroscopic factors such as temperature, pressure and viscosity on DAC and how these effects are related to the attained molecular picture,” said Roy.