Transition Metal Catalysis for Selective Synthesis and Sustainable Chemistry
Bibliographic record
Abstract
This thesis discusses the preparation and use of transition-metal catalysts for selective organic chemical reactions. Specifically, two different matters have been studied; the asymmetric hydrogenation of carbon-carbon double bonds using N,P-ligated iridium catalysts and the metal-catalyzed transfer of small molecules from biomass to synthetic intermediates. In the first part of this thesis, chiral N,P-ligands were synthesized and evaluated in iridium catalysts for the asymmetric hydrogenation of non- and weakly functionalized alkenes (Papers I & II). The new catalysts were prepared via chiral-pool strategies and exhibited superior properties for the reduction of certain types of alkenes. In particular, some of the catalysts showed excellent activity and selectivity in the enantioselective reduction of terminal alkenes, and the preparation of a modular catalyst library allowed the asymmetric hydrogenation of a wide range of 1,1-disubstituted alkenes with unprecedented efficiency and enantioselectivity (Paper III). Methods for the selective preparation of chiral hetero- and carbocyclic fragments using iridium-catalyzed asymmetric hydrogenation as an enantiodetermining key step were also developed. A range of elusive chiral building blocks that have applications in pharmaceutical and natural-product chemistry could thus be conveniently prepared (Papers IV & V). The second part of this thesis deals with the catalytic decomposition of polysaccharides into sugar alcohols and the incorporation of their decomposition products into alkene substrates. Iridium-catalyzed dehydrogenative decarbonylation was found to decompose polyols into CO:H2 mixtures that could be used immediately in the ex situ low-pressure hydroformylation of styrene (Paper VI). The net process was thus the hydroformylation of alkenes with biomass-derived synthesis gas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".