Multi-Level Computational Screening of in Silico Designed MOFs for Efficient SO₂ Capture

Abstract

SO 2 presence in the atmosphere can cause significant harm to the human and environment through acid rain and/or smog formation. Combining the operational advantages of adsorption-based separation and diverse nature of metal–organic frameworks (MOFs), cost-effective separation processes for SO 2 emissions can be developed. Herein, a large database of hypothetical MOFs composed of >300,000 materials is screened for SO 2 /CH 4 , SO 2 /CO 2 , and SO 2 /N 2 separations using a multi-level computational approach. Based on a combination of separation performance metrics (adsorption selectivity, working capacity, and regenerability), the best materials and the most common functional groups in those most promising materials are identified for each separation. The top bare MOFs and their functionalized variants are determined to attain SO 2 /CH 4 selectivities of 62.4–16899.7, SO 2 working capacities of 0.3–20.1 mol/kg, and SO 2 regenerabilities of 5.8–98.5%. Regarding SO 2 /CO 2 separation, they possess SO 2 /CO 2 selectivities of 13.3–367.2, SO 2 working capacities of 0.1–17.7 mol/kg, and SO 2 regenerabilities of 1.9–98.2%. For the SO 2 /N 2 separation, their SO 2 /N 2 selectivities, SO 2 working capacities, and SO 2 regenerabilities span the ranges of 137.9–67,338.9, 0.4–20.6 mol/kg, and 7.0–98.6%, respectively. Besides, using breakdowns of gas separation performances of MOFs into functional groups, separation performance limits of MOFs based on functional groups are identified where bare MOFs (MOFs with multiple functional groups) tend to show the smallest (largest) spreads.

Effects of MOF linker rotation and functionalization on methane uptake and diffusion

“…These computational approaches to incorporate flexibility have shown that framework motion can have strong effects on guest molecule properties, 19,34–38 such as adsorption 39–41 and diffusivity, 25,27,42–48,57 in accordance with experiment. 20,47,58–65 Yang et al 47 studied the diffusion of methane in UiO-66 using flexible force fields and observed a significant improvement in prediction over rigid force fields, although predictions were still lacking in quantitative and qualitative agreement with experiment.…”

“…This approach allows for a less computationally demanding means of accessing the variation in channel environment, but it relies on the assumption that the presence of guest molecules has a negligible impact on the dynamic configurations of the MOF, which does not hold true for all MOFs. 26,27 These computational approaches to incorporate flexibility have shown that framework motion can have strong effects on guest molecule properties, 19,[34][35][36][37][38] such as adsorption [39][40][41] and diffusivity, 25,27,[42][43][44][45][46][47][48]57 in accordance with experiment. 20,47,[58][59][60][61][62][63][64][65] Yang et al 47 studied the diffusion of methane in UiO-66 using flexible force fields and observed a significant improvement in prediction over rigid force fields, although predictions were still lacking in quantitative and qualitative agreement with experiment.…”

“…These flexible force field parameters allow framework atoms to structurally adjust on-the-fly in response to changing gas pressure. 19,25,27,[34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] Flexible force field parameters are available in the general universal force field (UFF) 49 and Dreiding 50 force fields, but they were developed without specificity to MOF environments. 44,51,52 Consequently, properties such as the diffusion coefficient obtained from these different flexible force fields vary by an order of magnitude.…”

Discovery of anion‐pillared metal–organic frameworks for efficient SO2/CO2 separation via computational screening

“…Due to the time and cost limitations, it is impossible to carry out a large‐scale exploration through trial‐and‐error experiments 35,36 . In this context, high‐throughput computational screening can be handily performed to guide the experimentalists toward the right direction by predicting the performance of a large library of structures thanks to the development of computational resources and algorithms 37–39 …”

“…35,36 In this context, high-throughput computational screening can be handily performed to guide the experimentalists toward the right direction by predicting the performance of a large library of structures thanks to the development of computational resources and algorithms. [37][38][39] Herein, we performed high-throughput grand canonical Monte Carlo (GCMC) simulations to predict the SO 2 /CO 2 separation performance in an anion-pillared MOF database through using our in-house code, namely high-throughput-based complex adsorption and diffusion simulation suite (HT-CADSS). 40 The relationship between SO 2 / CO 2 separation performances and structural characteristics was established based on the simulation results.…”