dc.contributor.author | Karl, Teresa M. | |
dc.contributor.author | Bouayad-Gervais, Samir | |
dc.contributor.author | Hueffel, Julian A. | |
dc.contributor.author | Sperger, Theresa | |
dc.contributor.author | Wellig, Sebastian | |
dc.contributor.author | Kaldas, Sherif J. | |
dc.contributor.author | Dabranskaya, Uladzislava | |
dc.contributor.author | Ward, Jas S. | |
dc.contributor.author | Rissanen, Kari | |
dc.contributor.author | Tizzard, Graham J. | |
dc.contributor.author | Schoenebeck, Franziska | |
dc.date.accessioned | 2024-02-21T07:22:35Z | |
dc.date.available | 2024-02-21T07:22:35Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Karl, T. M., Bouayad-Gervais, S., Hueffel, J. A., Sperger, T., Wellig, S., Kaldas, S. J., Dabranskaya, U., Ward, J. S., Rissanen, K., Tizzard, G. J., & Schoenebeck, F. (2023). Machine Learning-Guided Development of Trialkylphosphine Ni(I) Dimers and Applications in Site-Selective Catalysis. <i>Journal of the American Chemical Society</i>, <i>145</i>(28), 15414-15424. <a href="https://doi.org/10.1021/jacs.3c03403" target="_blank">https://doi.org/10.1021/jacs.3c03403</a> | |
dc.identifier.other | CONVID_183977559 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/93514 | |
dc.description.abstract | Owing to the unknown correlation of a metal’s ligand and its resulting preferred speciation in terms of oxidation state, geometry, and nuclearity, a rational design of multinuclear catalysts remains challenging. With the goal to accelerate the identification of suitable ligands that form trialkylphosphine-derived dihalogen-bridged Ni(I) dimers, we herein employed an assumption-based machine learning approach. The workflow offers guidance in ligand space for a desired speciation without (or only minimal) prior experimental data points. We experimentally verified the predictions and synthesized numerous novel Ni(I) dimers as well as explored their potential in catalysis. We demonstrate C–I selective arylations of polyhalogenated arenes bearing competing C–Br and C–Cl sites in under 5 min at room temperature using 0.2 mol % of the newly developed dimer, [Ni(I)(μ-Br)PAd2(n-Bu)]2, which is so far unmet with alternative dinuclear or mononuclear Ni or Pd catalysts. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | American Chemical Society (ACS) | |
dc.relation.ispartofseries | Journal of the American Chemical Society | |
dc.rights | In Copyright | |
dc.subject.other | catalysis | |
dc.subject.other | hydrocarbons | |
dc.subject.other | ligands | |
dc.subject.other | oligomers | |
dc.subject.other | palladium | |
dc.title | Machine Learning-Guided Development of Trialkylphosphine Ni(I) Dimers and Applications in Site-Selective Catalysis | |
dc.type | article | |
dc.identifier.urn | URN:NBN:fi:jyu-202402211981 | |
dc.contributor.laitos | Kemian laitos | fi |
dc.contributor.laitos | Department of Chemistry | en |
dc.contributor.oppiaine | Orgaaninen kemia | fi |
dc.contributor.oppiaine | Organic Chemistry | en |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 15414-15424 | |
dc.relation.issn | 0002-7863 | |
dc.relation.numberinseries | 28 | |
dc.relation.volume | 145 | |
dc.type.version | acceptedVersion | |
dc.rights.copyright | © 2023 American Chemical Society | |
dc.rights.accesslevel | openAccess | fi |
dc.subject.yso | katalyysi | |
dc.subject.yso | hiilivedyt | |
dc.subject.yso | ligandit | |
dc.subject.yso | oligomeeri | |
dc.subject.yso | palladium | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p8704 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p1169 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p24741 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p961 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p26929 | |
dc.rights.url | http://rightsstatements.org/page/InC/1.0/?language=en | |
dc.relation.doi | 10.1021/jacs.3c03403 | |
jyx.fundinginformation | We thank RWTH Aachen University, the European Research Council (ERC-637993), the Volkswagen Foundation (Momentum Program), the DFG (German Research Foundation) Cluster of Excellence 2186 (“The Fuel Science Center”ID: 390919832), and the Fonds der Chemischen Industrie (Kekulé scholarship to T.M.K.) for funding. Calculations were performed with computing resources granted by JARA-HPC from RWTH Aachen University under the project “jara0091”. | |
dc.type.okm | A1 | |