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Atomic force microscopy analysis of nanoparticles in non-ideal conditions

dc.contributor.authorKlapetek, Petrcs
dc.contributor.authorValtr, Miroslavcs
dc.contributor.authorNečas, Davidcs
dc.contributor.authorSalyk, Otacs
dc.contributor.authorDzik, Petrcs
dc.date.accessioned2019-02-13T11:57:39Z
dc.date.available2019-02-13T11:57:39Z
dc.date.issued2011-08-30cs
dc.identifier.citationNanoscale Research Letters. 2011, vol. 6, issue 1, p. 1-9.en
dc.identifier.issn1931-7573cs
dc.identifier.other75129cs
dc.identifier.urihttp://hdl.handle.net/11012/137965
dc.description.abstractNanoparticles are often measured using atomic force microscopy or other scanning probe microscopy methods. For isolated nanoparticles on flat substrates, this is a relatively easy task. However, in real situations, we often need to analyze nanoparticles on rough substrates or nanoparticles that are not isolated. In this article, we present a simple model for realistic simulations of nanoparticle deposition and we employ this model for modeling nanoparticles on rough substrates. Different modeling conditions (coverage, relaxation after deposition) and convolution with different tip shapes are used to obtain a wide spectrum of virtual AFM nanoparticle images similar to those known from practice. Statistical parameters of nanoparticles are then analyzed using different data processing algorithms in order to show their systematic errors and to estimate uncertainties for atomic force microscopy analysis of nanoparticles under non-ideal conditions. It is shown that the elimination of user influence on the data processing algorithm is a key step for obtaining accurate results while analyzing nanoparticles measured in non-ideal conditions.en
dc.description.abstractNanoparticles are often measured using atomic force microscopy or other scanning probe microscopy methods. For isolated nanoparticles on flat substrates, this is a relatively easy task. However, in real situations, we often need to analyze nanoparticles on rough substrates or nanoparticles that are not isolated. In this article, we present a simple model for realistic simulations of nanoparticle deposition and we employ this model for modeling nanoparticles on rough substrates. Different modeling conditions (coverage, relaxation after deposition) and convolution with different tip shapes are used to obtain a wide spectrum of virtual AFM nanoparticle images similar to those known from practice. Statistical parameters of nanoparticles are then analyzed using different data processing algorithms in order to show their systematic errors and to estimate uncertainties for atomic force microscopy analysis of nanoparticles under non-ideal conditions. It is shown that the elimination of user influence on the data processing algorithm is a key step for obtaining accurate results while analyzing nanoparticles measured in non-ideal conditions.cs
dc.formattextcs
dc.format.extent1-9cs
dc.format.mimetypeapplication/pdfcs
dc.language.isoencs
dc.publisherSpringer Opencs
dc.relation.ispartofNanoscale Research Letterscs
dc.relation.urihttps://nanoscalereslett.springeropen.com/articles/10.1186/1556-276X-6-514cs
dc.rightsCreative Commons Attribution 2.0 Genericcs
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/cs
dc.subjectatomic force microscopyen
dc.subjectpalladiumen
dc.subjectnanoparticleen
dc.subjectmikroskopie atomárních sil
dc.subjectpaladium
dc.subjectnanočástice
dc.titleAtomic force microscopy analysis of nanoparticles in non-ideal conditionsen
dc.title.alternativeAtomic force microscopy analysis of nanoparticles in non-ideal conditionscs
thesis.grantorVysoké učení technické v Brně. Fakulta chemická. Ústav fyzikální a spotřební chemiecs
sync.item.dbidVAV-75129en
sync.item.dbtypeVAVen
sync.item.insts2020.04.01 10:59:33en
sync.item.modts2020.04.01 05:50:41en
dc.coverage.issue1cs
dc.coverage.volume6cs
dc.identifier.doi10.1186/1556-276X-6-514cs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1931-7573/cs
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Except where otherwise noted, this item's license is described as Creative Commons Attribution 2.0 Generic