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

  • Nickolas Gantzler
  • , Min Bum Kim
  • , Alexander Robinson
  • , Maxwell W. Terban
  • , Sanjit Ghose
  • , Robert E. Dinnebier
  • , Arthur Henry York
  • , Davide Tiana
  • , Cory M. Simon
  • , Praveen K. Thallapally

Research output: Contribution to journalArticlepeer-review

Abstract

Our objective is to tune a “lead” metal-organic framework, Ni(PyC)2 (pyridine-4-carboxylate [PyC]), by functionalizing its PyC ligands to maximize its adsorptive selectivity for xenon over krypton at room temperature. To guide experiments, we (1) construct a library of Ni(PyC-X)2 (X = functional group) crystal structure models then (2) use molecular simulations to predict their noble gas adsorption and selectivity at room temperature. Motivated by our virtual screening, we synthesize Ni(PyC-m-NH2)2, determine its crystal structure by X-ray powder diffraction, measure its Xe, Kr, and Ar adsorption isotherms (298 K), and indeed find that its dilute Xe/Kr selectivity at 298 K (20) exceeds that of its parent Ni(PyC)2 (17). Corroborated by molecular models, in situ X-ray diffraction shows that Ni(PyC-m-NH2)2 organizes well-defined, Xe-tailored binding pockets along its one-dimensional channels. Our study illustrates the computation-informed optimization of a “lead” metal-organic framework.

Original languageEnglish
Article number101025
JournalCell Reports Physical Science
Volume3
Issue number9
DOIs
Publication statusPublished - 21 Sep 2022

Keywords

  • computational screening
  • gas adsorption
  • gas separations
  • metal-organic frameworks
  • MOFs
  • molecular simulations
  • noble gas adsorption
  • Xe/Kr separations

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