Computation-informed optimization of Ni(PyC)₂ functionalization for noble gas separations

Abstract

Metal-organic frameworks (MOFs) are promising nanoporous materials for the adsorptive capture and separation of noble gases at room temperature. Among the numerous MOFs synthesized and tested for noble gas separations, Ni(PyC)₂ (PyC = pyridine-4-carboxylate) exhibits one of the highest xenon/krypton selectivities at room temperature. Like lead-optimization in drug discovery, here we aim to tune the chemistry of Ni(PyC)₂, by appending a functional group to its PyC ligands, to maximize its Xe/Kr selectivity. To guide experiments in the laboratory, we virtually screen Ni(PyC-X)₂ (X=functional group) structures for noble gas separations by (i) constructing a library of Ni(PyC-X)₂ crystal structure models then (ii) using molecular simulations to predict noble gas (Xe, Kr, Ar) adsorption and selectivity at room temperature in each structure. The virtual screening predicts several Ni(PyC-X)₂ structures to exhibit a higher Xe/Kr, Xe/Ar, and Kr/Ar selectivity than the parent Ni(PyC)₂ MOF, with Ni(PyC-m-NH₂)₂ among them. In the laboratory, we synthesize Ni(PyC-m-NH₂)₂, determine its crystal structure by X-ray powder diffraction, and measure its Xe, Kr, and Ar adsorption isotherms (298 K). In agreement with our molecular simulations, the Xe/Kr, Xe/Ar, and Kr/Ar selectivities of Ni(PyC-m-NH₂)₂ exceed those of the parent Ni(PyC)₂. Particularly, Ni(PyC-m-NH₂)₂ exhibits a [derived from experimental, equilibrium adsorption isotherms] Xe/Kr selectivity of 20 at dilute conditions and 298 K, compared to 17 for Ni(PyC)₂. According to in situ X-ray diffraction, corroborated by molecular models, Ni(PyC-m-NH₂)₂ presents well-defined binding pockets tailored for Xe and organized along its one-dimensional channels. In addition to discovering the new, performant Ni(PyC-m-NH₂)₂ MOF for noble gas separations, our study illustrates the computation-informed optimization of the chemistry of a "lead" MOF to target adsorption of a specific gas.

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

“…A joint experimental computational work involving about 2 orders of magnitude less number of materials has recently been published. 63 Therefore, our work focuses on the elucidation of the potential improvement/deterioration in the separation performance of materials due to the grafting of the functional groups on high-performing bare MOFs but not determining separation performances of the entire >300,000 hypothetical MOFs which would be too costly.…”

Computational investigation of multifunctional MOFs for adsorption and membrane-based separation of CF4/CH4, CH4/H2, CH4/N2, and N2/H2 mixtures

“…It has been shown earlier that structure optimizations (and optimization settings) may lead to considerably different gas uptakes. 60 For instance, it has been shown earlier that while Xe at 298 K, 1 bar in experimentally determined structure of Ni(PyC) 2 matches that in one of its optimized counterparts (optimized with unit cell parameter constraints), it is overestimated by other optimized counterpart structures (optimized with unit cell angle constraints and no constraints). 60 Secondly, while the studied gas mixtures are assumed to be dry, in practice, they may include varying levels of humidity which can affect the separation performances (adsorption selectivity, membrane selectivity, gas permeability) of adsorbents and/or membranes.…”

“…60 For instance, it has been shown earlier that while Xe at 298 K, 1 bar in experimentally determined structure of Ni(PyC) 2 matches that in one of its optimized counterparts (optimized with unit cell parameter constraints), it is overestimated by other optimized counterpart structures (optimized with unit cell angle constraints and no constraints). 60 Secondly, while the studied gas mixtures are assumed to be dry, in practice, they may include varying levels of humidity which can affect the separation performances (adsorption selectivity, membrane selectivity, gas permeability) of adsorbents and/or membranes. 61 As an example, Daglar et al 61 demonstrated that the inclusion of H 2 O in CO 2 /N 2 mixture can hamper the adsorption and membrane selectivity together with gas permeabilities.…”