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               <dc:title>Performance of Metal-Catalyzed Hydrodebromination of Dibromomethane Analyzed by Descriptors Derived from Statistical Learning</dc:title>
               <dc:creator>Saadun, A. J.</dc:creator>
               <dc:creator>Pablo-García, S.</dc:creator>
               <dc:creator>Paunović, V.</dc:creator>
               <dc:creator>Li, Q.</dc:creator>
               <dc:creator>Sabadell-Rendón, A.</dc:creator>
               <dc:creator>Kleemann, K.</dc:creator>
               <dc:creator>Krumeich, F.</dc:creator>
               <dc:creator>López, N.</dc:creator>
               <dc:creator>Pérez-Ramírez, J.</dc:creator>
               <dc:description>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 (&lt;8%) Fe/SiO2, Co/SiO2, Cu/SiO2, and Ag/SiO2; (ii) Rh/SiO2 and Ni/SiO2, which exhibit intermediate CH3Br selectivity (&lt;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:description>
               <dc:date>2021-08-23T11:31:59Z</dc:date>
               <dc:date>2024-04-23T10:30:47Z</dc:date>
               <dc:date>2021-08-23T11:31:59Z</dc:date>
               <dc:date>2024-04-23T10:30:47Z</dc:date>
               <dc:date>2020-04-10</dc:date>
               <dc:type>info:eu-repo/semantics/article</dc:type>
               <dc:type>info:eu-repo/semantics/acceptedVersion</dc:type>
               <dc:identifier>http://hdl.handle.net/2072/450539</dc:identifier>
               <dc:identifier>https://doi.org/10.1021/acscatal.0c00679</dc:identifier>
               <dc:language>eng</dc:language>
               <dc:relation>ETH-43 181</dc:relation>
               <dc:relation>RTI2018-101394-B100</dc:relation>
               <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
               <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:rights>
               <dc:source>RECERCAT (Dipòsit de la Recerca de Catalunya)</dc:source>
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