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

03 December 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

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.

Keywords

MOF
metal-organic framework
virtual screening
Xe/Kr separations
gas separations

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