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dc.rights.licenseopenen_US
hal.structure.identifierLaboratoire Matière sous Conditions Extrêmes [LMCE]
hal.structure.identifierInstitut des Sciences Moléculaires [ISM]
hal.structure.identifierDAM Île-de-France [DAM/DIF]
dc.contributor.authorPOLEWCZYK, Franck
hal.structure.identifierLaboratoire Matière sous Conditions Extrêmes [LMCE]
hal.structure.identifierInstitut des Sciences Moléculaires [ISM]
hal.structure.identifierDAM Île-de-France [DAM/DIF]
dc.contributor.authorLAFOURCADE, Paul
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
hal.structure.identifierBordeaux Sciences Agro [Gradignan]
dc.contributor.authorCOSTA, Jean-Pierre Da
hal.structure.identifierLaboratoire des Composites Thermostructuraux [LCTS]
dc.contributor.authorVIGNOLES, Gerard
IDREF: 070191875
hal.structure.identifierInstitut des Sciences Moléculaires [ISM]
dc.contributor.authorLEYSSALE, Jean-Marc
dc.date.accessioned2023-11-22T11:47:16Z
dc.date.available2023-11-22T11:47:16Z
dc.date.issued2023-08-01
dc.identifier.issn0008-6223en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/186055
dc.description.abstractEnAtomistic modeling of disordered yet textured carbons is notoriously difficult even though it can prove extremely helpful in rationalizing structure–property relationships for this class of materials. In this work we introduce a polygranular image-guided atomistic reconstruction method, which allows building models with fine-tuned values of the in-plane (L$_a$) and out-of-plane (L$_c$) coherence lengths, and of the orientation angle (OA). Applying a parametric study of grain size and orientation distribution, a database of 210 models is presented with parameters spanning domains characteristic of high and medium textured pyrolytic carbons: 1.5–8 nm, 2–5.5 nm and 25–110°, for L$_c$, L$_a$ and OA, respectively. A machine learning model based on a random forest regression shows that these three measurable properties can be accurately predicted from a limited set of microscopic information characterizing the distribution of local atomic environments in the models. Finally, the computed diffraction properties and high-resolution transmission electron microscopy images of a series of six models, that best match the properties of a set of well-characterized pyrocarbons, are extensively compared to experimental data, showing excellent agreement and drastically improving over former modeling studies on high textured pyrocarbons, in addition to providing the first atomistic model of a medium textured pyrocarbon.
dc.language.isoENen_US
dc.subject.enPyrolytic carbon Structure Texture Modeling Machine learning
dc.title.enPolygranular image guided atomistic reconstruction: A parametric model of pyrocarbon nanostructure
dc.title.alternativeCarbonen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1016/j.carbon.2023.118109en_US
dc.subject.halPhysique [physics]en_US
bordeaux.journalCarbonen_US
bordeaux.page118109en_US
bordeaux.volume212en_US
bordeaux.hal.laboratoriesLaboratoire des Composites Thermo Structuraux (LCTS) - UMR 5801en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionCNRSen_US
bordeaux.institutionCEAen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.identifier.funderIDMinistère de la Défense Nationaleen_US
bordeaux.import.sourcehal
hal.identifierhal-04249516
hal.version1
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Carbon&rft.date=2023-08-01&rft.volume=212&rft.spage=118109&rft.epage=118109&rft.eissn=0008-6223&rft.issn=0008-6223&rft.au=POLEWCZYK,%20Franck&LAFOURCADE,%20Paul&COSTA,%20Jean-Pierre%20Da&VIGNOLES,%20Gerard&LEYSSALE,%20Jean-Marc&rft.genre=article


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