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dc.contributor.authorPizzi, Giovannien
dc.contributor.authorMilana, Silviaen
dc.contributor.authorFerrari, Andreaen
dc.contributor.authorMarzari, Nicolaen
dc.contributor.authorGibertini, Marcoen
dc.date.accessioned2021-06-17T23:32:08Z
dc.date.available2021-06-17T23:32:08Z
dc.date.issued2021-08-09en
dc.identifier.issn1936-0851
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/324036
dc.description.abstractLayered materials (LMs), such as graphite, hexagonal boron nitride, and transition-metal dichalcogenides, are at the center of an ever-increasing research effort, due to their scienti c and technological relevance. Raman and infrared spectroscopies are accurate, non destructive, approaches to determine a wide range of properties, including the number of layers and the strength of the interlayer interactions. We present a general approach to predict the complete spectroscopic fan diagrams, i.e., the relations between frequencies and number of layers, N, for the optically active shear and layer-breathing modes of any multilayer comprising N>=2 identical layers. In order to achieve this, we combine a description of the normal modes in terms of a one-dimensional mechanical model, with symmetry arguments that describe the evolution of the point group as a function of N. Group theory is then used to identify which modes are Raman and/or infrared active, and to provide diagrams of the optically active modes for any stack composed of identical layers. We implement the method and algorithms in an open-source tool directly available on the Materials Cloud portal, to assist any researcher in the prediction and interpretation of such diagrams. Our work will underpin future efforts on Raman and infrared characterization of known, and yet not investigated, LMs.
dc.description.sponsorshipEU Graphene Flagship
dc.format.mediumPrint-Electronicen
dc.languageengen
dc.publisherAmerican Chemical Society
dc.rightsAll rights reserved
dc.titleShear and Breathing Modes of Layered Materials.en
dc.typeArticle
prism.publicationDate2021en
prism.publicationNameACS nanoen
dc.identifier.doi10.17863/CAM.71496
dcterms.dateAccepted2021-06-14en
rioxxterms.versionofrecord10.1021/acsnano.0c10672en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2021-08-09en
dc.contributor.orcidPizzi, Giovanni [0000-0002-3583-4377]
dc.contributor.orcidFerrari, Andrea [0000-0003-0907-9993]
dc.contributor.orcidMarzari, Nicola [0000-0002-9764-0199]
dc.contributor.orcidGibertini, Marco [0000-0003-3980-5319]
dc.identifier.eissn1936-086X
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEPSRC (EP/K017144/1)
pubs.funder-project-idEPSRC (EP/M507799/1)
pubs.funder-project-idEPSRC (EP/L016087/1)
cam.orpheus.successMon Aug 16 07:31:04 BST 2021 - Embargo updated*
cam.orpheus.counter8*
rioxxterms.freetoread.startdate2022-08-09


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