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Retrieving the quantitative chemical information at nanoscale from scanning electron microscope energy dispersive x-ray measurements by machine learning

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Retrieving the quantitative chemical information at nanoscale from scanning electron microscope energy dispersive x-ray measurements by machine learning

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dc.contributor.author Jany, Benedykt [SAP14007747] pl
dc.contributor.author Janas, Arkadiusz [USOS115183] pl
dc.contributor.author Krok, Franciszek [SAP11015621] pl
dc.date.accessioned 2017-11-30T10:11:24Z
dc.date.available 2017-11-30T10:11:24Z
dc.date.issued 2017 pl
dc.identifier.issn 1530-6984 pl
dc.identifier.uri https://ruj.uj.edu.pl/xmlui/handle/item/46898
dc.language eng pl
dc.rights Dodaję tylko opis bibliograficzny *
dc.rights.uri *
dc.title Retrieving the quantitative chemical information at nanoscale from scanning electron microscope energy dispersive x-ray measurements by machine learning pl
dc.type JournalArticle pl
dc.description.physical 6520-6525 pl
dc.abstract.en The quantitative composition of metal alloy nanowires on InSb semiconductor surface and gold nanostructures on germanium surface is determined by blind source separation (BSS) machine learning method using non-negative matrix factorization from energy dispersive X-ray spectroscopy (EDX) spectrum image maps measured in a scanning electron microscope (SEM). The BSS method blindly decomposes the collected EDX spectrum image into three source components, which correspond directly to the X-ray signals coming from the supported metal nanostructures, bulk semiconductor signal, and carbon background. The recovered quantitative composition is validated by detailed Monte Carlo simulations and is confirmed by separate cross-sectional transmission electron microscopy EDX measurements of the nanostructures. This shows that simple and achievable SEM EDX measurements together with machine learning non-negative matrix factorization-based blind source separation processing could be successfully used for the nanostructures quantitative chemical composition determination. Our finding can make the chemical quantification at the nanoscale much faster and cost efficient for many systems. pl
dc.subject.en BSS pl
dc.subject.en EDX pl
dc.subject.en machine learning pl
dc.subject.en NMF pl
dc.subject.en SEM pl
dc.description.volume 17 pl
dc.description.number 11 pl
dc.identifier.doi 10.1021/acs.nanolett.7b01789 pl
dc.identifier.eissn 1530-6992 pl
dc.title.journal Nano Letters pl
dc.language.container eng pl
dc.affiliation Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Fizyki im. Mariana Smoluchowskiego pl
dc.subtype Article pl
dc.rights.original bez licencji pl
.pointsMNiSW [2017 A]: 45


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