Stability of heterogeneous single-atom catalysts: a scaling law mapping thermodynamics to kinetics

Su, Ya-Qiong; Zhang, Long; Wang, Yifan; Liu, Jin-Xun; Muravev, Valery; Alexopoulos, Konstantinos; Filot, Ivo A. W.; Vlachos, Dionisios G.; Hensen, Emiel J. M.

doi:10.1038/s41524-020-00411-6

Title: Stability of heterogeneous single-atom catalysts: a scaling law mapping thermodynamics to kinetics

Journal Article · Thu Sep 24 00:00:00 UTC 2020 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-020-00411-6 · OSTI ID:1768834

^[1]; Zhang, Long ^[2]; Wang, Yifan ^[3]; Liu, Jin-Xun ^[4];

^[2]; Alexopoulos, Konstantinos ^[3];

^[2];

^[3];

^[2]

Eindhoven Univ. of Technology (The Netherlands); RAPID Manufacturing Institute
Eindhoven Univ. of Technology (The Netherlands)
Univ. of Delaware, Newark, DE (United States)
Eindhoven Univ. of Technology (The Netherlands); Univ. of Michigan, Ann Arbor, MI (United States)

Heterogeneous single-atom catalysts (SACs) hold the promise of combining high catalytic performance with maximum utilization of often precious metals. We extend the current thermodynamic view of SAC stability in terms of the binding energy (E_bind) of single-metal atoms on a support to a kinetic (transport) one by considering the activation barrier for metal atom diffusion. A rapid computational screening approach allows predicting diffusion barriers for metal–support pairs based on E_bind of a metal atom to the support and the cohesive energy of the bulk metal (E_c). Metal–support combinations relevant to contemporary catalysis are explored by density functional theory. Assisted by machine-learning methods, we find that the diffusion activation barrier correlates with (E_bind)²/E_c in the physical descriptor space. This diffusion scaling-law provides a simple model for screening thermodynamics to kinetics of metal adatom on a support.

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Research Organization:: RAPID Manufacturing Inst., Newark, DE (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: EE0007888

OSTI ID:: 1768834

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 6; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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