Understanding the adsorption process in ZIF-8 using high pressure crystallography and computational modelling

Claire Hobday, Christopher H. Woodall, Matthew J. Lennox, Mungo Frost, Konstantin Kamenev, Tina Düren, Carole A Morrison, Stephen A. Moggach

Research output: Contribution to journalArticlepeer-review

114 Citations (SciVal)
127 Downloads (Pure)

Abstract

Some porous crystalline solids change their structure upon guest inclusion. Unlocking the potential of these solids for a wide variety of applications requires full characterisation of the response to adsorption and the underlying framework–guest interactions. Here, we introduce an approach to understanding gas uptake in porous metal-organic frameworks (MOFs) by loading liquefied gases at GPa pressures inside the Zn-based framework ZIF-8. An integrated experimental and computational study using high-pressure crystallography, grand canonical Monte Carlo (GCMC) and periodic DFT simulations has revealed six symmetry-independent adsorption sites within the framework and a transition to a high-pressure phase. The cryogenic high-pressure loading method offers a different approach to obtaining atomistic detail on guest molecules. The GCMC simulations provide information on interaction energies of the adsorption sites allowing to classify the sites by energy. DFT calculations reveal the energy barrier of the transition to the high-pressure phase. This combination of techniques provides a holistic approach to understanding both structural and energetic changes upon adsorption in MOFs.
Original languageEnglish
Article number1429 (2018)
Pages (from-to)1-9
Number of pages9
JournalNature Communications
Volume9
Issue number1
Early online date12 Apr 2018
DOIs
Publication statusPublished - 1 Dec 2018

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

Fingerprint

Dive into the research topics of 'Understanding the adsorption process in ZIF-8 using high pressure crystallography and computational modelling'. Together they form a unique fingerprint.

Cite this