What is he best known for?
The fields of study he is best known for:
- Catalysis
- Organic chemistry
- Alkene
His scientific interests lie mostly in Catalysis, Organic chemistry, Enantioselective synthesis, Photochemistry and Medicinal chemistry.
Catalysis and Polymer chemistry are commonly linked in his work.
His studies in Enantioselective synthesis integrate themes in fields like Silanes and Silylation.
His Silanes study incorporates themes from Combinatorial chemistry and Stereochemistry.
His study in Photochemistry is interdisciplinary in nature, drawing from both Imine, Pincer movement, Catalytic cycle, Iridium and Deprotonation.
His work carried out in the field of Medicinal chemistry brings together such families of science as Palladium, Heterolysis, Organic synthesis, Aryl and Coupling.
His most cited work include:
- The Mizoroki–Heck Reaction (344 citations)
- A unified survey of Si–H and H–H bond activation catalysed by electron-deficient boranes (262 citations)
- Conclusive Evidence for an SN2‐Si Mechanism in the B(C6F5)3‐Catalyzed Hydrosilylation of Carbonyl Compounds: Implications for the Related Hydrogenation (257 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Catalysis, Organic chemistry, Medicinal chemistry, Enantioselective synthesis and Lewis acids and bases.
He interconnects Photochemistry, Silicon and Polymer chemistry in the investigation of issues within Catalysis.
His Silicon study combines topics from a wide range of disciplines, such as Chirality and Silanes.
His research in Medicinal chemistry focuses on subjects like Electrophile, which are connected to Electrophilic aromatic substitution.
As part of the same scientific family, Martin Oestreich usually focuses on Enantioselective synthesis, concentrating on Stereochemistry and intersecting with Stereoselectivity.
His studies deal with areas such as Adduct, Steric effects and Borane as well as Lewis acids and bases.
He most often published in these fields:
- Catalysis (47.03%)
- Organic chemistry (27.69%)
- Medicinal chemistry (25.93%)
What were the highlights of his more recent work (between 2019-2021)?
- Catalysis (47.03%)
- Medicinal chemistry (25.93%)
- Lewis acids and bases (17.58%)
In recent papers he was focusing on the following fields of study:
His main research concerns Catalysis, Medicinal chemistry, Lewis acids and bases, Silylation and Enantioselective synthesis.
His biological study spans a wide range of topics, including Combinatorial chemistry, Aryl and Polymer chemistry.
His Medicinal chemistry research incorporates themes from Coupling, Ring, Silanes and Nickel.
His Lewis acids and bases research includes elements of Ether, Iminium, Aldehyde and Borane.
The study incorporates disciplines such as Intramolecular force and Stereochemistry in addition to Silylation.
Enantioselective synthesis is the subject of his research, which falls under Organic chemistry.
Between 2019 and 2021, his most popular works were:
- Cationic silicon Lewis acids in catalysis (26 citations)
- Emerging Strategies for C–H Silylation (21 citations)
- Beyond Carbon: Enantioselective and Enantiospecific Reactions with Catalytically Generated Boryl- and Silylcopper Intermediates. (14 citations)
In his most recent research, the most cited papers focused on:
- Catalysis
- Organic chemistry
- Alkene
His primary areas of study are Catalysis, Medicinal chemistry, Reaction mechanism, Combinatorial chemistry and Ring.
He has researched Catalysis in several fields, including Nanotechnology and Silicon.
In his study, which falls under the umbrella issue of Medicinal chemistry, Copper catalyzed, Enantiomer and Enyne is strongly linked to Silanes.
His Reaction mechanism research is multidisciplinary, relying on both Silylation and Regioselectivity.
Martin Oestreich has included themes like Lewis acids and bases and Organic synthesis in his Combinatorial chemistry study.
His research in Ring intersects with topics in Silylium ion, Friedel–Crafts reaction and Annulation.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
