Using computational methods to explore improvements to Knölker's iron catalyst
Bibliographic record
Abstract
Knölker's iron catalyst is characterized by low toxicity and relatively low price in comparison with precious metal catalysts. Density functional theory was used to explore improvements to this catalyst. It was found that electron-withdrawing substituents on the CpOH ring are favorable for improving the efficiency of iron catalysts. Increasing the acidity of CpOH is also an available means of improving the catalytic efficiency. However, replacing the hydroxyl of CpOH with the amino group is not a valid choice for improvement. In contrast, substituting phosphine ligands for carbonyls is the most effective method for improving the catalytic activity of the iron catalyst. But the PR3 ligand must have electron-donating groups and its steric effect should be controlled in a suitable range. Replacing carbonyl groups by PH3 and PPhH2 ligands can effectually improve the catalytic activity for hydrogenation of ketones.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".