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Ti Group Metallocene-Catalyzed Synthesis of 1-Hexene Dimers and Tetramers.

ABSTRACT: 1-Hexene transformations in the catalytic systems L₂MCl₂-XAlBuⁱ₂ (L = Cp, M = Ti, Zr, Hf; L = Ind, rac-H₄C₂[THInd]₂, M = Zr; X = H, Bu ⁱ) and [Cp₂ZrH₂]₂-ClAlR₂ activated by MMAO-12, B(C₆F₅)₃, or (Ph₃C)[B(C₆F₅)₄] in chlorinated solvents (CH₂Cl₂, CHCl₃, o-Cl₂C₆H₄, ClCH₂CH₂Cl) were studied. The systems [Cp₂ZrH₂]₂-MMAO-12, [Cp₂ZrH₂]₂-ClAlBuⁱ₂-MMAO-12, or Cp₂ZrCl₂-HAlBuⁱ₂-MMAO-12 (B(C₆F₅)₃) in CH₂Cl₂ showed the highest activity and selectivity towards the formation of vinylidene head-to-tail alkene dimers. The use of chloroform as a solvent provides further in situ dimer dimerization to give a tetramer yield of up to 89%. A study of the reaction of [Cp₂ZrH₂]₂ or Cp₂ZrCl₂ with organoaluminum compounds and MMAO-12 by NMR spectroscopy confirmed the formation of Zr,Zr-hydride clusters as key intermediates of the alkene dimerization. The probable structure of the Zr,Zr-hydride clusters and ways of their generation in the catalytic systems were analyzed using a quantum chemical approach (DFT).

SUBMITTER: Kovyazin PV

PROVIDER: S-EPMC8125888 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The rational design of catalysts remains a challenging endeavor within the broader chemical community owing to the myriad variables that can affect key bond-forming events. Designing selective catalysts for any reaction requires an efficient strategy for discovering predictive structure-activity relationships. Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H-pyrrole (C) via a Ti-catalyzed formal [2 + 2 +1] cycloaddition of phenylpropyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space and k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (>90% C) by incorporating 2,6-dimethyl-4-(pyrrolidin-1-yl)pyridine as a ligand. The origin of catalyst selectivity was probed by examining ISPCA variable loadings in combination with DFT modeling, revealing that ligand lability plays an important role in selectivity. A parallel catalyst search using multivariate linear regression (MLR), a popular approach in catalysis informatics, was also conducted in order to compare these strategies in a hypothetical catalyst scouting campaign. ISPCA appears to be more robust and predictive than MLR when sparse training sets are used that are representative of the data available during the early search for an optimal catalyst. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development.

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Ti Group Metallocene-Catalyzed Synthesis of 1-Hexene Dimers and Tetramers.

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