Home > Publications database > Data-mining of in-situ TEM experiments: Towards understanding nanoscale fracture > print |
001 | 910955 | ||
005 | 20231027114347.0 | ||
024 | 7 | _ | |a 10.1016/j.commatsci.2022.111830 |2 doi |
024 | 7 | _ | |a 0927-0256 |2 ISSN |
024 | 7 | _ | |a 1879-0801 |2 ISSN |
024 | 7 | _ | |a 2128/33773 |2 Handle |
024 | 7 | _ | |a WOS:000882200400008 |2 WOS |
037 | _ | _ | |a FZJ-2022-04283 |
082 | _ | _ | |a 530 |
100 | 1 | _ | |a Steinberger, Dominik |0 P:(DE-HGF)0 |b 0 |
245 | _ | _ | |a Data-mining of in-situ TEM experiments: Towards understanding nanoscale fracture |
260 | _ | _ | |a Amsterdam [u.a.] |c 2023 |b Elsevier Science |
336 | 7 | _ | |a article |2 DRIVER |
336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1674638334_21125 |2 PUB:(DE-HGF) |
336 | 7 | _ | |a ARTICLE |2 BibTeX |
336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
520 | _ | _ | |a The lifetime and performance of any engineering component, from nanoscale sensors to macroscopic structures, are strongly influenced by fracture processes. Fracture itself is a highly localized event; originating at the atomic scale by bond breaking between individual atoms close to the crack tip. These processes, however, interact with defects such as dislocations or grain boundaries and influence phenomena on much larger length scales, ultimately giving rise to macroscopic behavior and engineering-scale fracture properties. This complex interplay is the fundamental reason why identifying the atomistic structural and energetic processes occurring at a crack tip remains a longstanding and still unsolved challenge.We develop a new analysis approach for combining quantitative in-situ observations of nanoscale deformation processes at a crack tip with three-dimensional reconstruction of the dislocation structure and advanced computational analysis to address plasticity and fracture initiation in a ductile metal. Our combinatorial approach reveals details of dislocation nucleation, their interaction process, and the local internal stress state, all of which were previously inaccessible to experiments. This enables us to describe fracture processes based on local crack driving forces on a dislocation level with a high fidelity that paves the way towards a better understanding and control of local failure processes in materials. |
536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
536 | _ | _ | |a MuDiLingo - A Multiscale Dislocation Language for Data-Driven Materials Science (759419) |0 G:(EU-Grant)759419 |c 759419 |f ERC-2017-STG |x 1 |
588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
700 | 1 | _ | |a Issa, Inas |0 P:(DE-HGF)0 |b 1 |
700 | 1 | _ | |a Strobl, Rachel |0 P:(DE-Juel1)186856 |b 2 |u fzj |
700 | 1 | _ | |a Imrich, Peter J. |0 P:(DE-HGF)0 |b 3 |
700 | 1 | _ | |a Kiener, Daniel |0 P:(DE-HGF)0 |b 4 |e Corresponding author |
700 | 1 | _ | |a Sandfeld, Stefan |0 P:(DE-Juel1)186075 |b 5 |e Corresponding author |
773 | _ | _ | |a 10.1016/j.commatsci.2022.111830 |g Vol. 216, p. 111830 - |0 PERI:(DE-600)2014722-3 |p 111830 - |t Computational materials science |v 216 |y 2023 |x 0927-0256 |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/910955/files/1-s2.0-S0927025622005419-main.pdf |y OpenAccess |
909 | C | O | |o oai:juser.fz-juelich.de:910955 |p openaire |p open_access |p driver |p VDB |p ec_fundedresources |p dnbdelivery |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)186856 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)186075 |
913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 0 |
914 | 1 | _ | |y 2023 |
915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2021-02-04 |
915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2021-02-04 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2023-10-22 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2023-10-22 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2023-10-22 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1150 |2 StatID |b Current Contents - Physical, Chemical and Earth Sciences |d 2023-10-22 |
915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b COMP MATER SCI : 2022 |d 2023-10-22 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2023-10-22 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2023-10-22 |
915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2023-10-22 |
915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |d 2023-10-22 |
920 | _ | _ | |l yes |
920 | 1 | _ | |0 I:(DE-Juel1)IAS-9-20201008 |k IAS-9 |l Materials Data Science and Informatics |x 0 |
980 | _ | _ | |a journal |
980 | _ | _ | |a VDB |
980 | _ | _ | |a UNRESTRICTED |
980 | _ | _ | |a I:(DE-Juel1)IAS-9-20201008 |
980 | 1 | _ | |a FullTexts |
Library | Collection | CLSMajor | CLSMinor | Language | Author |
---|