001     1043286
005     20250917202326.0
024 7 _ |a 10.1016/j.matdes.2025.114224
|2 doi
024 7 _ |a 0264-1275
|2 ISSN
024 7 _ |a 0141-5530
|2 ISSN
024 7 _ |a 0261-3069
|2 ISSN
024 7 _ |a 1873-4197
|2 ISSN
024 7 _ |a 1878-2876
|2 ISSN
024 7 _ |a 10.34734/FZJ-2025-02812
|2 datacite_doi
024 7 _ |a WOS:001553529800003
|2 WOS
037 _ _ |a FZJ-2025-02812
082 _ _ |a 690
100 1 _ |a Ganesan, Hariprasath
|0 P:(DE-Juel1)200155
|b 0
|e Corresponding author
245 _ _ |a Capturing thin-film microstructure contributions during ultrafast laser-metal interactions using atomistic simulations
260 _ _ |a Amsterdam [u.a.]
|c 2025
|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 1750773989_12429
|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 Progress in the emerging fields of atomic and close-to-atomic scale manufacturing is underpinned by enhanced precision and optimization of laser-controlled nanostructuring. Understanding thin films' crystallographic orientations and microstructure effects becomes crucial for optimizing the laser-metallic thin film interactions; however, these effects remain largely unexplored at the atomic scale. Using a hybrid two-temperature model and molecular dynamics, we simulated ultrafast laser-metal interactions for gold thin films with varying crystallographic orientations and microstructure configurations. Microstructure features, namely grain size, grain topology, and local crystallographic orientation, controlled the rate and extent of lattice disorder evolution and phase transformation, particularly at lower applied fluences. Our simulations provided comprehensive insights encompassing both the nanomechanical and thermodynamic aspects of ultrafast laser-metal interactions at atomic resolution. Microstructure-aware/informed thin film fabrication and targeted defect engineering could improve the precision of nanoscale laser processing and potentially emerge as an energy-efficient optimization strategy.
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
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Sandfeld, Stefan
|0 P:(DE-Juel1)186075
|b 1
773 _ _ |a 10.1016/j.matdes.2025.114224
|g Vol. 256, p. 114224 -
|0 PERI:(DE-600)2015480-X
|p 114224 -
|t Materials and design
|v 256
|y 2025
|x 0264-1275
856 4 _ |u https://juser.fz-juelich.de/record/1043286/files/1-s2.0-S0264127525006446-main-1.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:1043286
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB
|p openCost
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)200155
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|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 2025
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2025-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2025-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1160
|2 StatID
|b Current Contents - Engineering, Computing and Technology
|d 2025-01-02
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2025-01-02
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b MATER DESIGN : 2022
|d 2025-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2024-01-26T11:03:51Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2024-01-26T11:03:51Z
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2025-01-02
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2025-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2025-01-02
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2025-01-02
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2025-01-02
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b MATER DESIGN : 2022
|d 2025-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2025-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2025-01-02
915 p c |a APC keys set
|2 APC
|0 PC:(DE-HGF)0000
915 p c |a Local Funding
|2 APC
|0 PC:(DE-HGF)0001
915 p c |a DFG OA Publikationskosten
|2 APC
|0 PC:(DE-HGF)0002
915 p c |a DEAL: Elsevier 09/01/2023
|2 APC
|0 PC:(DE-HGF)0125
915 p c |a DOAJ Journal
|2 APC
|0 PC:(DE-HGF)0003
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IAS-9-20201008
|k IAS-9
|l Materials Data Science and Informatics
|x 0
980 1 _ |a FullTexts
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)IAS-9-20201008
980 _ _ |a APC


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21