Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/38822
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDe Backer , Annick-
dc.contributor.authorVan Aert, Sandra-
dc.contributor.authorFAES, Christel-
dc.contributor.authorIrmak, Ece Arslan-
dc.contributor.authorNellist, Peter D.-
dc.contributor.authorJones, Lewys-
dc.date.accessioned2022-10-27T12:37:02Z-
dc.date.available2022-10-27T12:37:02Z-
dc.date.issued2022-
dc.date.submitted2022-10-20T11:36:32Z-
dc.identifier.citationnpj Computational Materials, 8 (1) (Art N° 216)-
dc.identifier.urihttp://hdl.handle.net/1942/38822-
dc.description.abstractWe introduce a Bayesian genetic algorithm for reconstructing atomic models of monotype crystalline nanoparticles from a single projection using Z-contrast imaging. The number of atoms in a projected atomic column obtained from annular dark field scanning transmission electron microscopy images serves as an input for the initial three-dimensional model. The algorithm minimizes the energy of the structure while utilizing a priori information about the finite precision of the atom-counting results and neighbor-mass relations. The results show promising prospects for obtaining reliable reconstructions of beam-sensitive nanoparticles during dynamical processes from images acquired with sufficiently low incident electron doses.-
dc.description.sponsorshipThis work was supported by the European Research Council (Grant 770887 PICOMETRICS to S.V.A. and Grant 823717 ESTEEM3). The authors acknowledge financial support from the Research Foundation Flanders (FWO, Belgium) through project fundings (G.0267.18N, G.0502.18N, G.0346.21N) and a postdoctoral grant to A.D.B. L.J. acknowledges Science Foundation Ireland (SFI – grant number URF/RI/ 191637), the Royal Society, and the AMBER Centre. The authors acknowledge Aakash Varambhia for his assistance and expertise with the experimental recording and use of characterization facilities within the David Cockayne Centre for Electron Microscopy, Department of Materials, University of Oxford, and in particular the EPSRC (EP/K040375/1 South of England Analytical Electron Microscope).-
dc.language.isoen-
dc.publisherNATURE PORTFOLIO-
dc.rightsThe Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http:// creativecommons.org/licenses/by/4.0/.-
dc.titleExperimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm-
dc.typeJournal Contribution-
dc.identifier.issue1-
dc.identifier.volume8-
local.bibliographicCitation.jcatA1-
dc.description.notesVan Aert, S (corresponding author), Univ Antwerp, EMAT, Groenenborgerlaan 171, B-2020 Antwerp, Belgium.; Van Aert, S (corresponding author), Univ Antwerp, NANOlab, Ctr Excellence, Groenenborgerlaan 171, B-2020 Antwerp, Belgium.; Jones, L (corresponding author), Ctr Res Adapt Nanostruct & Nanodevices CRANN, Adv Microscopy Lab, Dublin 2, Ireland.; Jones, L (corresponding author), Univ Dublin, Trinity Coll Dublin, Sch Phys, Dublin 2, Ireland.-
dc.description.notessandra.vanaert@uantwerpen.be; lewys.jones@tcd.ie-
local.publisher.placeHEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY-
local.type.refereedRefereed-
local.type.specifiedArticle-
local.bibliographicCitation.artnr216-
dc.identifier.doi10.1038/s41524-022-00900-w-
dc.identifier.isi000866500900001-
dc.contributor.orcidDe Backer, Annick/0000-0002-8592-4776-
local.provider.typewosris-
local.description.affiliation[De Backer, Annick; Van Aert, Sandra; Irmak, Ece Arslan] Univ Antwerp, EMAT, Groenenborgerlaan 171, B-2020 Antwerp, Belgium.-
local.description.affiliation[De Backer, Annick; Van Aert, Sandra; Irmak, Ece Arslan] Univ Antwerp, NANOlab, Ctr Excellence, Groenenborgerlaan 171, B-2020 Antwerp, Belgium.-
local.description.affiliation[Faes, Christel] Hasselt Univ, Data Sci Inst, I BioStat, Hasselt, Belgium.-
local.description.affiliation[Nellist, Peter D.] Univ Oxford, Dept Mat, Parks Rd, Oxford OX1 3PH, England.-
local.description.affiliation[Jones, Lewys] Ctr Res Adapt Nanostruct & Nanodevices CRANN, Adv Microscopy Lab, Dublin 2, Ireland.-
local.description.affiliation[Jones, Lewys] Univ Dublin, Trinity Coll Dublin, Sch Phys, Dublin 2, Ireland.-
local.uhasselt.internationalyes-
item.accessRightsOpen Access-
item.fullcitationDe Backer , Annick; Van Aert, Sandra; FAES, Christel; Irmak, Ece Arslan; Nellist, Peter D. & Jones, Lewys (2022) Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm. In: npj Computational Materials, 8 (1) (Art N° 216).-
item.fulltextWith Fulltext-
item.validationecoom 2023-
item.contributorDe Backer , Annick-
item.contributorVan Aert, Sandra-
item.contributorFAES, Christel-
item.contributorIrmak, Ece Arslan-
item.contributorNellist, Peter D.-
item.contributorJones, Lewys-
crisitem.journal.eissn2057-3960-
Appears in Collections:Research publications
Files in This Item:
File Description SizeFormat 
Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm.pdfPublished version3.96 MBAdobe PDFView/Open
Show simple item record

WEB OF SCIENCETM
Citations

3
checked on May 12, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.