Finding Furfural Hydrogenation Catalysts via Predictive Modelling

Z. Strassberger; M. Mooijman; E. Ruijter; A.H. Alberts; A.G. Maldonado; R.V.A. Orru; G. Rothenberg

doi:10.1002/adsc.201000308

Finding Furfural Hydrogenation Catalysts via Predictive Modelling

Z. Strassberger, M. Mooijman, E. Ruijter, A.H. Alberts, A.G. Maldonado, R.V.A. Orru, G. Rothenberg

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (k

Original language	English
Pages (from-to)	2201-2210
Number of pages	10
Journal	Advanced Synthesis and Catalysis
Volume	352
Issue number	13
DOIs	https://doi.org/10.1002/adsc.201000308
Publication status	Published - 2010

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10.1002/adsc.201000308

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AU - Strassberger, Z.

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AU - Maldonado, A.G.

AU - Orru, R.V.A.

AU - Rothenberg, G.

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AB - We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (k

U2 - 10.1002/adsc.201000308

DO - 10.1002/adsc.201000308

M3 - Article

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ER -

Finding Furfural Hydrogenation Catalysts via Predictive Modelling

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