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dc.contributor.authorMuñoz-Ocaña, Juan M.
dc.contributor.authorBouziane, Ainouna
dc.contributor.authorSakina, Farzeen
dc.contributor.authorBaker, Richard T.
dc.contributor.authorHungría, Ana B.
dc.contributor.authorCalvino, Jose J.
dc.contributor.authorRodríguez-Chía, Antonio M.
dc.contributor.authorLópez-Haro, Miguel
dc.date.accessioned2021-05-16T23:48:44Z
dc.date.available2021-05-16T23:48:44Z
dc.date.issued2020-06-09
dc.identifier.citationMuñoz-Ocaña , J M , Bouziane , A , Sakina , F , Baker , R T , Hungría , A B , Calvino , J J , Rodríguez-Chía , A M & López-Haro , M 2020 , ' Optimization of STEM-HAADF electron tomography reconstructions by parameter selection in compressed sensing total variation minimization-based algorithms ' , Particle & Particle Systems Characterization , vol. 37 , no. 6 , 2000070 . https://doi.org/10.1002/ppsc.202000070en
dc.identifier.issn0934-0866
dc.identifier.otherPURE: 268060292
dc.identifier.otherPURE UUID: bfd94fcb-7aea-4aa4-bd5c-ccf02ade306f
dc.identifier.otherRIS: urn:6E8544EC554EFED535BDE037D126A905
dc.identifier.otherORCID: /0000-0002-3304-3280/work/74510229
dc.identifier.otherWOS: 000533228500001
dc.identifier.otherScopus: 85084796113
dc.identifier.urihttp://hdl.handle.net/10023/23196
dc.descriptionThis work has received financial support from FEDER/MINECO (MAT2017‐87579‐R, MAT2016‐81118‐P, and MTM2016‐74983‐C2‐2‐R). NetmeetData Project from Fundación BBVA convocatoria 2020. Junta de Andalucía is also acknowledged (MULTITOM Project, P18‐FR‐1422 Project FQM334, FQM355). J.M.M. acknowledges the Youth Employment Program (Plan de Empleo Juvenil) of Junta de Andalucía. Electron microscopy data were acquired using the equipment at the DME‐UCA node of the ELECMI Spanish Unique Infrastructure for Electron Microscopy of Materials. The authors thank the University of St. Andrews for a Ph.D. scholarship for F.S.en
dc.description.abstractA novel procedure to optimize the 3D morphological characterization of nanomaterials by means of high angle annular dark field scanning‐transmission electron tomography is reported and is successfully applied to the analysis of a metal‐ and halogen‐free ordered mesoporous carbon material. The new method is based on a selection of the two parameters (μ and β) which are key in the reconstruction of tomographic series by means of total variation minimization (TVM). The parameter‐selected TVM reconstructions obtained using this approach clearly reveal the porous structure of the carbon‐based material as consisting of a network of parallel, straight channels of ≈6 nm diameter ordered in a honeycomb‐type arrangement. Such an unusual structure cannot be retrieved from a TVM 3D reconstruction using default reconstruction values. Moreover, segmentation and further quantification of the optimized 3D tomographic reconstruction provide values for different textural parameters, such as pore size distribution and specific pore volume that match very closely with those determined by macroscopic physisorption techniques. The approach developed can be extended to other reconstruction models in which the final result is influenced by parameter choice.
dc.language.isoeng
dc.relation.ispartofParticle & Particle Systems Characterizationen
dc.rightsCopyright © 2020 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1002/ppsc.202000070en
dc.subject3D characterizationen
dc.subjectCompressed-sensingen
dc.subjectMesoporous materialsen
dc.subjectParameters selectionen
dc.subjectSTEM-HAADF electron tomographyen
dc.subjectQD Chemistryen
dc.subjectDASen
dc.subject.lccQDen
dc.titleOptimization of STEM-HAADF electron tomography reconstructions by parameter selection in compressed sensing total variation minimization-based algorithmsen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Chemistryen
dc.contributor.institutionUniversity of St Andrews.St Andrews Sustainability Instituteen
dc.contributor.institutionUniversity of St Andrews.EaSTCHEMen
dc.identifier.doihttps://doi.org/10.1002/ppsc.202000070
dc.description.statusPeer revieweden
dc.date.embargoedUntil2021-05-17


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