loading page

Machine learning guided design of covalent organic frameworks for CO2 capture in wet flue gas
  • +7
  • Shuna Yang,
  • Weichen Zhu,
  • Linbin Zhu,
  • Xue Ma,
  • Tongan Yan,
  • Nengcui Gu,
  • Youshi Lan,
  • Yi Huang,
  • Mingyuan Yuan,
  • Minman Tong
Shuna Yang
Jiangsu Normal University

Corresponding Author:2459583596@qq.com

Author Profile
Weichen Zhu
Jiangsu Normal University
Author Profile
Linbin Zhu
Jiangsu Normal University
Author Profile
Xue Ma
Jiangsu Normal University
Author Profile
Tongan Yan
Beijing University of Chemical Technology
Author Profile
Nengcui Gu
Jiangsu Normal University
Author Profile
Youshi Lan
China Institute of Atomic Energy
Author Profile
Yi Huang
Jiangsu Normal University
Author Profile
Mingyuan Yuan
Jiangsu Normal University
Author Profile
Minman Tong
Jiangsu Normal University
Author Profile

Abstract

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 computational screening on 516 experimental covalent organic frameworks (COFs) identify the superior secondary building units (SBUs) for wet flue gas separation, which are tetraphenylporphyrin unit in sql-type COFs and functional groups. Accordingly, 1233 COFs are assembled using the superior SBUs. Combined with DFT calculations, the “electron-donating induced vdW interaction” effect is discovered to design better-performing COFs with superior CO2 uptake, which can achieve 4.4 mmol·g-1 with CO2/N2 selectivity of 104.8; while the “electron-withdrawing induced vdW+electrostatic coupling interaction” effect is proposed to design better-performing COFs with superior CO2/N2 selectivity, which can arrive 277.6 with CO2 uptake of 2.2 mmol·g-1, in this case, H2O contributes to improving the CO2/N2 selectivity.