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Markus Bursch

Researcher at University of Bonn

Publications -  43
Citations -  1504

Markus Bursch is an academic researcher from University of Bonn. The author has contributed to research in topics: Medicine & Density functional theory. The author has an hindex of 17, co-authored 34 publications receiving 871 citations.

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Full Selectivity Control in Cobalt(III)‐Catalyzed C−H Alkylations by Switching of the C−H Activation Mechanism

TL;DR: Detailed mechanistic studies provide compelling evidence for a programmable switch in the C-H activation mechanism from a linear-selective ligand-to-ligand hydrogen transfer to a branched- selective base-assisted internal electrophilic-type substitution.
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C–F/C–H Functionalization by Manganese(I) Catalysis: Expedient (Per)Fluoro-Allylations and Alkenylations

TL;DR: The robust nature of the manganese(I) catalysis regime was among others reflected by the first C–F/C–H activation with perfluoroalkenes as well as racemization-free C–H functionalizations on imines, amino acids, and peptides.
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Understanding and Quantifying London Dispersion Effects in Organometallic Complexes.

TL;DR: The quality of calculated structural and thermodynamic properties in gas-phase obtained with DFT-D4 corrected methods, specifically for organometallic complexes is described, highlighting the major role of dispersion interactions for the accurate description of thermochemistry in gas.
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Mild Cobalt(III)-Catalyzed Allylative C-F/C-H Functionalizations at Room Temperature.

TL;DR: Sustainable, cobalt-catalyst enabled, synthetically significant C-F/C-H functionalizations were achieved with an ample substrate scope at an ambient temperature of 25 °C, thereby delivering perfluoroallylated heteroarenes.
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Best‐Practice DFT Protocols for Basic Molecular Computational Chemistry

TL;DR: In this paper , the authors provide best-practice guidance on the numerous methodological and technical aspects of density functional theory (DFT) calculations in three parts: Firstly, they set the stage and introduce a step-by-step decision tree to choose a computational protocol that models the experiment as closely as possible.