English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cu-exchanged zeolites rely on mobile solvated Cu+ cations for their catalyticactivity, but the role of framework composition on transport is not fullyunderstood. Ab initio molecular dynamics simulations can provide quantitativeatomistic insight but are too computationally expensive to explore largelength- and time-scales or diverse compositions. We report a machine-learninginteratomic potential that accurately reproduces ab initio results andeffectively generalizes to allow multi-nanosecond simulations of largesupercells and diverse chemical compositions. Biased and unbiased simulationsof [Cu(NH3)2]+ mobility show that aluminum pairing in eight-membered ringsaccelerates local hopping, and demonstrate that increased NH3 concentrationenhances long-range diffusion. The probability of finding two [Cu(NH3)2]+complexes in the same cage - key for SCR-NOx reaction - increases with Cucontent and Al content, but does not correlate with the long-range mobility ofCu+. Supporting experimental evidence was obtained from reactivity tests ofCu-CHA catalysts with controlled chemical composition.

Effect of framework composition and NH3 on the diffusion of Cu+ in Cu-CHA catalysts predicted by machine-learning accelerated molecular dynamics