<i>MaxKinEff</i>: A Collision Theory‐Based Approach for Analyzing Turnover Frequency and Turnover Number in Catalytic Processes
Bibliographic record
Abstract
Abstract The efficiency of catalysts relies on comprehending the underlying kinetics that govern their performance. Under the steady‐state regime, the “rate” is referred to as the turnover frequency, where the reaction rate is first order with respect to catalysts. Here, we introduce the Maximum Kinetic Efficiency ( MaxKinEff ) model, grounded in collision theory, to predict efficiency based on maximum turnover frequency, and maximum turnover number, . The model was applied to molecular water oxidation using twenty‐six transition metal catalysts from the first (3d), second (4d), and third (5d) rows. A thorough investigation reveals that [Ru(pda)(Br‐py) 2 ] (pda=1,10‐phenanthroline‐2,9‐dicarboxylate; Py=pyridinophane) exhibits a notable of 1176.87×10 −5 s −1 due to its larger collision diameter ( σ RC ) and lower activation energy ( E a ). Importantly, the trend in the computed values aligns with experimental TON, validating the model's accuracy. For instance, [Cp * Ir( κ 2 ‐N,O)NO 3 ] is identified by MaxKinEff as a standout performer, with the normalized maximum computed TON, resembling the experimental TON, =2000.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".