Transition Metal Hydride Complexes: From Computation to Catalysis and Everything in Between
Bibliographic record
Abstract
Computational methods were used to investigate transition metal hydride complexes both in the ground state and as reactive intermediates. Chapter 2 investigates an interesting phenomenon concerning the reversal of the relative position of the antisymmetric and symmetric vibrational modes of trans-dihydrides of transition metals and main group elements. These findings were used to show the different modes of hydride attack on CO2 by a transition metal and main group porphyrinate trans-dihydride complex. Chapter 3 details both an experimental and computational study on the catalytic transfer hydrogenation of aryl ketones with basic iso-propanol by a ruthenium hydride complex with a protic N-heterocyclic carbene ligand. The bifunctional character of the protic N-heterocyclic carbene moiety was determined to play a crucial role in the mechanism of the inner-sphere hydride transfer to acetophenone in a computational study. Chapter 4 describes the synthesis of an iridium(III) azolato complex through rearrangement of a tethered C8-iodinated theophylline fragment. The rearrangement proceeds through an unusual seven-coordinate cationic iridium(V) hydride intermediate which is supported by computational analysis. Finally, chapter 5 concerns the development of an undergraduate laboratory experiment in which an iron carbonyl complex is synthesized to investigate its catalytic activity in the transfer hydrogenation of acetophenone.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.000 |
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".