New Directions in Catalyst Design and Interrogation: Applications in Dinitrogen Activation and Olefin Metathesis
Bibliographic record
Abstract
A major driving force for development of new catalyst systems is the need for more efficient synthesis of chemical compounds essential to modern life. Catalysts having superior performance offer significant environmental and economic advantages, but their discovery is not trivial. Well-defined, homogeneous catalysts can offer unparalleled understanding of ligand effects, which proves invaluable in directing redesign strategies. This thesis work focuses on the design of ruthenium complexes for applications in dinitrogen activation and olefin metathesis. The complexes developed create new directions in small-molecule activation and asymmetric catalysis by late-metal complexes. Also examined are the dual challenges, ubiquitous in catalysis, of adequate interrogation of catalyst structure and performance. Insight into both is essential to enable correlation of ligand properties with catalyst activity and/or selectivity. Improved methods for accelerated assessment of catalyst performance are described, which expand high-throughput catalyst screening to encompass parallel acquisition of kinetic data. A final aspect focuses on direct examination of metal complexes, both as isolated species, and under catalytic conditions. Applications of charge-transfer MALDI mass spectrometry to structural elucidation in organometallic chemistry is described, and the technique is employed to gain insight into catalyst decomposition pathways under operating conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".