High-Throughput Computation Evaluation of Metal–Organic Frameworks for Efficient Perfluorocarbons Recovery

The
recovery of perfluorocarbons (PFCs), such as CF4 and C2F6, from exhaust gas can not
only reduce
the emissions of greenhouse gas but also improve the utilization of
PFCs in the semiconductor industry. In this work, a high-throughput
computational evaluation for nearly 10 000 MOFs in the CoRE
MOF database was performed to evaluate the potential of metal–organic
frameworks (MOFs) for the recovery of trace CF4 and C2F6 from N2-containing gas. Various adsorbent
performance metrics, including adsorption selectivity, working capacity,
recovery rate, and adsorbent performance score, were calculated to
evaluate the top-performing MOFs, and 10 top-performing MOFs for efficient
capture of CF4 and C2F6 over N2 were identified from a computation-ready experimental (CoRE)
MOF database. The machine learning model analysis reveals that the
LCD as well as the adsorption heat difference between PFCs with N2 play dominant roles in PFCs recovery. Furthermore, five design
and optimization strategies, including adjustment or functionalization
of the organic linker, substitution of metal node, regulation of topology
net, and optimization of synthesis condition, were provided to guide
the development of high-performing MOFs for PFCs recovery.