Performance of Metal-Catalyzed Hydrodebromination of Dibromomethane Analyzed by Descriptors Derived from Statistical Learning

dc.contributor.author
Saadun, A. J.
dc.contributor.author
Pablo-García, S.
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Paunović, V.
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Li, Q.
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Sabadell-Rendón, A.
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Kleemann, K.
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Krumeich, F.
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López, N.
dc.contributor.author
Pérez-Ramírez, J.
dc.date.accessioned
2021-08-23T11:31:59Z
dc.date.accessioned
2024-04-23T10:30:47Z
dc.date.available
2021-08-23T11:31:59Z
dc.date.available
2024-04-23T10:30:47Z
dc.date.issued
2020-04-10
dc.identifier.uri
https://hdl.handle.net/2072/450539
dc.description.abstract
The catalyzed semihydrogenation of dibromomethane (CH2Br2) to methyl bromide (CH3Br) is a key step in the bromine-mediated upgradation of methane. This study presents a cutting-edge strategy combining density functional theory (DFT), catalytic tests complemented with the extensive characterization of a wide range of metal catalysts (Fe, Co, Ni, Cu, Ru, Rh, Ag, Ir, and Pt), and statistical tools for a computer-assisted investigation of this reaction. The steady-state catalytic tests identified four classes of materials comprising (i) poorly active (<8%) Fe/SiO2, Co/SiO2, Cu/SiO2, and Ag/SiO2; (ii) Rh/SiO2 and Ni/SiO2, which exhibit intermediate CH3Br selectivity (<60%); (iii) Ir/SiO2 and Pt/SiO2, which display great propensity to CH4 (>50%); and (iv) Ru/SiO2, which exhibits the highest selectivity to CH3Br (up to 96%). In-depth characterization of representative catalysts in fresh and used forms was done by X-ray diffraction, inductively coupled plasma optical emission spectroscopy, N2 sorption, temperature-programmed reduction, Raman spectroscopy, electron microscopy, and X-ray photoelectron spectroscopy. The dimensionality reduction performed on the 272 DFT intermediate adsorption energies using principal component analysis identified two descriptors that, when employed together with the experimental data in a random forest regressor, enabled the understanding of activity and selectivity trends by connecting them to the energy intervals of the descriptors. In addition, a representative analytic model was found using the Bayesian inference. These findings illustrate the exciting opportunities presented by integrated experimental/computational screening and set the fundamental basis for the accelerated discovery of superior hydrodebromination catalysts and beyond.
dc.format.extent
6129 p.
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dc.language.iso
eng
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dc.rights
L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons:http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.source
RECERCAT (Dipòsit de la Recerca de Catalunya)
dc.subject.other
54
cat
dc.title
Performance of Metal-Catalyzed Hydrodebromination of Dibromomethane Analyzed by Descriptors Derived from Statistical Learning
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dc.type
info:eu-repo/semantics/article
cat
dc.type
info:eu-repo/semantics/acceptedVersion
cat
dc.embargo.terms
12 mesos
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dc.relation.projectID
ETH-43 181
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dc.relation.projectID
RTI2018-101394-B100
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dc.identifier.doi
https://doi.org/10.1021/acscatal.0c00679
dc.rights.accessLevel
info:eu-repo/semantics/openAccess


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