Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering

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dc.contributor.authorZhao, Yifangen
dc.contributor.authorKoike, Suguruen
dc.contributor.authorNakama, Rikutoen
dc.contributor.authorIhara, Shiroen
dc.contributor.authorMitsuhara, Masatoshien
dc.contributor.authorMurayama, Mitsuhiroen
dc.contributor.authorHata, Satoshien
dc.contributor.authorSaito, Hikaruen
dc.date.accessioned2022-02-10T12:06:04Zen
dc.date.available2022-02-10T12:06:04Zen
dc.date.issued2021-10-26en
dc.date.updated2022-02-10T12:06:00Zen
dc.description.abstractScanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively thick specimen than the conventional transmission electron microscopy, whose resolution is limited by the chromatic aberration of image forming lenses, and thus, the STEM mode has been employed frequently for computed electron tomography based three-dimensional (3D) structural characterization and combined with analytical methods such as annular dark field imaging or spectroscopies. However, the image quality of STEM is severely suffered by noise or artifacts especially when rapid imaging, in the order of millisecond per frame or faster, is pursued. Here we demonstrate a deep-learning-assisted rapid STEM tomography, which visualizes 3D dislocation arrangement only within five-second acquisition of all the tilt-series images even in a 300 nm thick steel specimen. The developed method offers a new platform for various in situ or operando 3D microanalyses in which dealing with relatively thick specimens or covering media like liquid cells are required.en
dc.description.versionPublished versionen
dc.format.extent12 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN 20720 (Article number)en
dc.identifier.doihttps://doi.org/10.1038/s41598-021-99914-5en
dc.identifier.eissn2045-2322en
dc.identifier.issn2045-2322en
dc.identifier.issue1en
dc.identifier.orcidMurayama, Mitsuhiro [0000-0003-1965-4891]en
dc.identifier.other10.1038/s41598-021-99914-5 (PII)en
dc.identifier.pmid34702955en
dc.identifier.urihttp://hdl.handle.net/10919/108246en
dc.identifier.volume11en
dc.language.isoenen
dc.publisherNature Portfolioen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000711622600006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectELECTRON-MICROSCOPYen
dc.subjectATOMIC-STRUCTUREen
dc.subjectCONTRASTen
dc.subjectRECONSTRUCTIONen
dc.subjectHOLDERen
dc.titleFive-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filteringen
dc.title.serialScientific Reportsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
dcterms.dateAccepted2021-09-30en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Materials Science and Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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