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Description:

Persistent homology is a powerful tool for quantifying various structures, but it is equally crucial to maintain its interpretability. In this study, we extracted interpretable geometric features from the persistent diagrams (PDs) of scanning transmission electron microscopy (STEM) images of self-assembled Pt-CeO2 nanostructures synthesized under different annealing conditions. We focused on PD quadrants and extracted five interpretable features from the zeroth and first PDs of nanostructures ranging from maze-like to striped patterns. A combination of hierarchical clus-tering and inverse analysis of PDs reconstructed by principal component analysis through vec-torization of the PDs highlighted the importance of the number of arc-like structures of the CeO2 phase in the first PDs, particularly those that were smaller than a characteristic size. This descriptor enabled us to quantify the degree of disorder, namely the density of bends, in nanostructures formed under different conditions. By using this descriptor along with the width of the CeO2 phase, we classified 12 Pt-CeO2 nanostructures in an interpretable way.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: persistent homology, metal-oxide nanostructures, scanning transmission electron microscopy (STEM)

Date published: 2024-08-29

Publisher: MDPI AG

Journal:

• Nanomaterials (ISSN: 20794991) vol. 14 issue. 17 1413

Funding:

• JST FOREST Program JPMJFR213U
• JST PRESTO JPMJPR17S7
• ARIM JPMXP1223NM5325
• ARIM JPMXP1224NM5122
• JSPS Grant-in-Aid for Scientific Research 22H01799
• JSPS Grant-in-Aid for Scientific Research 23K23067

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.3390/nano14171413

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Updated at: 2024-10-09 08:30:46 +0900

Published on MDR: 2024-10-09 08:30:46 +0900