Subnanometer Substructures in Nanoassemblies Formed from Clusters under a Reactive Atmosphere Revealed Using Machine Learning

Timoshenko, Janis; Halder, Avik; Yang, Bing; Seifert, Soenke; Pellin, Michael J.; Vajda, Stefan; Frenkel, Anatoly I.

doi:10.1021/acs.jpcc.8b07952

Title: Subnanometer Substructures in Nanoassemblies Formed from Clusters under a Reactive Atmosphere Revealed Using Machine Learning

Journal Article · Sun Aug 26 00:00:00 EDT 2018 · Journal of Physical Chemistry. C

DOI:https://doi.org/10.1021/acs.jpcc.8b07952· OSTI ID:1480971

Timoshenko, Janis ^[1]; Halder, Avik ^[2]; Yang, Bing ^[2]; Seifert, Soenke ^[3]; Pellin, Michael J. ^[2];

^[4];

^[5]

Stony Brook Univ., NY (United States). Dept. of Materials Science and Chemical Engineering
Argonne National Lab. (ANL), Argonne, IL (United States). Materials Science Division
Argonne National Lab. (ANL), Argonne, IL (United States). X-ray Science Division
Argonne National Lab. (ANL), Argonne, IL (United States). Materials Science Division; Univ. of Chicago, IL (United States). Inst. for Molecular Engineering
Stony Brook Univ., NY (United States). Dept. of Materials Science and Chemical Engineering, and Dept. of Chemistry

Size-selected clusters, soft-landed on an oxide substrate, is a promising and highly tunable material for heterogeneous catalysis. Agglomeration of the deposited clusters, however, leads to changes in the particle properties and structure. The latter for such cluster assemblies can also be different from that in self standing nanoparticles of similar sizes. To monitor the formation of such complex materials, in situ studies at different length scales are required. Toward that goal, we combined small-angle X-ray scattering (SAXS), X-ray absorption near-edge structure (XANES) spectroscopy, ab initio simulations, and machine learning (artificial neural network) techniques. We detected significant differences between the sizes of particle agglomerates, as probed by SAXS, and the sizes of locally ordered regions, as seen by XANES. We interpret these differences as an evidence for the fractal, grape-cluster-like structure of the agglomerates; thus, XANES and SAXS provide highly complementary structural information. This finding can have a profound effect on our understanding of particle sintering and assembly processes and of structure properties relationship in ultradispersed metal catalysts in reaction conditions.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0012704; AC02-06CH11357

OSTI ID:: 1480971

Alternate ID(s):: OSTI ID: 1477464

Report Number(s):: BNL-209339-2018-JAAM

Journal Information:: Journal of Physical Chemistry. C, Vol. 122, Issue 37; ISSN 1932-7447

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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