In Silico Screening of CO2-Dipeptide Interactions for Bioinspired Carbon Capture.

Carbon capture, sequestration and utilization offers a viable solution for reducing the total amount of atmospheric CO₂ concentrations. On an industrial scale, amine-based solvents are extensively employed for CO₂ capture through chemisorption. Nevertheless, this method is marked by the high cost associated with solvent regeneration, high vapor pressure, and the corrosive and toxic attributes of by-products, such as nitrosamines. An alternative approach is the biomimicry of sustainable materials that have strong affinity and selectivity for CO₂. Bioinspired approaches, such as those based on naturally occurring amino acids, have been proposed for direct air capture methodologies. In this study, we present a database consisting of 960 dipeptide molecular structures, composed of the 20 naturally occurring amino acids. Those structures were analyzed with a novel computational workflow presented in this work that considers certain interaction sites that determine CO₂ affinity. Density functional theory (DFT) and symmetry-adapted perturbation theory (SAPT) computations were performed for the calculation of CO₂ interaction energies, which allowed to limit our search space to 400 unique dipeptide structures. Using this computational workflow, we provide statistical insights into dipeptides and their affinity for CO₂ binding, as well as design principles that can further enhance CO₂ capture through cooperative binding.