ArticleNature communications2021
Distilling nanoscale heterogeneity of amorphous silicon using tip-enhanced Raman spectroscopy (TERS) via multiresolution manifold learning.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Development of scanning ion-conductance microscopy based tip-enhanced Raman spectroscopy in liquid environments.Nanotechnology · 2026Review
- Redefining the Conversion Mechanism of Chalcopyrite CuFeSSmall (Weinheim an der Bergstrasse, Germany) · 2026Article
- SPECTRA-based Detection of Drug-Induced Hepatotoxicity through Extracellular Vesicle Analysis.Sensors and actuators. B, Chemical · 2026Article
- Ultrahigh energy-storage dielectric ceramics via synergistic polymorphic nanodomain and defect design.Nature communications · 2026Article
- Plasmonic Nanocubes with a Controllable "Crescent Arc" Facet: Tunable Hotspot Engineering for Highly Reliable and Sensitive SERS Detection.Analytical chemistry · 2024Article
- Insights into the LiMnChemistry of materials : a publication of the American Chemical Society · 2024Article
- Unravelling the Influence of Surface Modification on the Ultimate Performance of Carbon Fiber/Epoxy Composites.ACS applied materials & interfaces · 2022Article
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9 authors.
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Abstract
Accurately identifying the local structural heterogeneity of complex, disordered amorphous materials such as amorphous silicon is crucial for accelerating technology development. However, short-range atomic ordering quantification and nanoscale spatial resolution over a large area on a-Si have remained major challenges and practically unexplored. We resolve phonon vibrational modes of a-Si at a lateral resolution of <60 nm by tip-enhanced Raman spectroscopy. To project the high dimensional TERS imaging to a two-dimensional manifold space and categorize amorphous silicon structure, we developed a multiresolution manifold learning algorithm. It allows for quantifying average Si-Si distortion angle and the strain free energy at nanoscale without a human-specified physical threshold. The multiresolution feature of the multiresolution manifold learning allows for distilling local defects of ultra-low abundance (< 0.3%), presenting a new Raman mode at finer resolution grids. This work promises a general paradigm of resolving nanoscale structural heterogeneity and updating domain knowledge for highly disordered materials.
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