Journal article
Visualizing nanoparticle surface dynamics and instabilities enabled by deep denoising
Science (American Association for the Advancement of Science), Vol.387(6737), pp.949-954
02/28/2025
DOI: 10.1126/science.ads2688
PMID: 40014729
Abstract
Materials functionalities may be associated with atomic-level structural dynamics occurring on the millisecond timescale. However, the capability of electron microscopy to image structures with high spatial resolution and millisecond temporal resolution is often limited by poor signal-to-noise ratios. With an unsupervised deep denoising framework, we observed metal nanoparticle surfaces (platinum nanoparticles on cerium oxide) in a gas environment with time resolutions down to 10 milliseconds at a moderate electron dose. On this timescale, many nanoparticle surfaces continuously transition between ordered and disordered configurations. Stress fields can penetrate below the surface, leading to defect formation and destabilization, thus making the nanoparticle fluxional. Combining this unsupervised denoiser with in situ electron microscopy greatly improves spatiotemporal characterization, opening a new window for the exploration of atomic-level structural dynamics in materials.
Details
- Title: Subtitle
- Visualizing nanoparticle surface dynamics and instabilities enabled by deep denoising
- Creators
- Peter A Crozier - Arizona State UniversityMatan Leibovich - New York UniversityPiyush Haluai - Arizona State UniversityMai Tan - Arizona State UniversityAndrew M Thomas - University of IowaJoshua Vincent - Arizona State UniversitySreyas Mohan - New York UniversityAdria Marcos Morales - Center for Data Science, New York University, New York, NY, USAShreyas A Kulkarni - Center for Data Science, New York University, New York, NY, USADavid S Matteson - Cornell UniversityYifan Wang - Arizona State UniversityCarlos Fernandez-Granda - New York University
- Resource Type
- Journal article
- Publication Details
- Science (American Association for the Advancement of Science), Vol.387(6737), pp.949-954
- Publisher
- AMER ASSOC ADVANCEMENT SCIENCE
- DOI
- 10.1126/science.ads2688
- PMID
- 40014729
- ISSN
- 0036-8075
- eISSN
- 1095-9203
- Grant note
- National Science Foundation: OAC-1940263, 2104105, DMR 1840841, CBET 1604971, OAC-1940097, CHE 2109202, OAC-2103936, OAC-1940124, DMS-2114143
This work was supported by the National Science Foundation (grants OAC-1940263 and 2104105 to P.A.C. and P.H., grant CBET 1604971 to J.V., grant DMR 1840841 to M.T., grant CHE 2109202 to Y.W., grant OAC-1940097 to M.L., grant OAC-2103936 to C.F.-G., and grants OAC-1940124 and DMS-2114143 to D.S.M. and A.M.T.).
- Language
- English
- Date published
- 02/28/2025
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984795474002771
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