Insights into CO2/N2 Selectivity in Porous Carbons from Deep Learning

Porous carbons are an important class of porous material for carbon capture. The textural properties of porous carbons greatly influence their CO2 adsorption capacities. But it is still unclear what features are most conductive to achieving high CO2/N-2 selectivity. Here, we trained deep neural networks from the experimental data of CO2 and N-2 uptakes in porous carbons based on textural features of micropore volume, mesopore volume, and BET surface area. We then used the model to screen porous carbons and to predict CO2 and N-2 uptakes, as well as CO2/N-2 selectivity. We found that the highest CO2/N-2 selectivity can be achieved not at the regions of highest CO2 uptake but at the regions of lowest N-2 uptake where mesopores disrupt N-2 adsorption. This insight will help guide experiments to synthesize better porous carbons for post-combustion CO2 capture.