Multi-Scale Computer-Aided Design of Covalent Organic Frameworks for CO₂ Capture in Wet Flue Gas

Discovery of remarkable porous materials for CO₂ capture from wet flue gas is of great significance to reduce the CO₂ emissions, but elucidating the most critical structure features for boosting CO₂ capture capabilities remains a great challenge. Here, machine-learning-assisted Monte Carlo computational screening on 516 experimental covalent organic frameworks (COFs) identifies the superior secondary building units (SBUs) for wet flue gas separation using COFs, which are tetraphenylporphyrin units for boosting CO₂ adsorption uptake and functional groups for boosting CO₂/N₂ selectivity. Accordingly, 1233 COFs are assembled using the identified superior SBUs. Density functional theory calculation analysis on frontier orbitals, electrostatic potential, and binding energy reveals the influencing mechanism of the SBUs on the wet flue gas separation performance. The "electron-donating-induced vdW interaction" effect is discovered to construct the better-performing COFs, which can achieve high CO₂ uptake of 4.4 mmol·g^-1 with CO₂/N₂ selectivity of 104.8. Meanwhile, the "electron-withdrawing-induced vdW + electrostatic coupling interaction" effect is unearthed to construct the better-performing COFs with superior CO₂/N₂ selectivity, which can reach 277.6 with CO₂ uptake of 2.2 mmol·g^-1; in this case, H₂O plays a positive contribution in improving CO₂/N₂ selectivity. This work provides useful guidelines for designing optimized two-dimensional-COF adsorbents for wet flue gas separation.