A Survey of Reaction Energetics for Diverse Small Molecule Activation: Where Do Molecular Electrocatalysts Go From Here?
Bibliographic record
Abstract
Abstract The invention of technologies that can activate, transform, and upgrade small molecules is a significant challenge. The starting point for many such technologies is molecular catalysts. Their well‐defined active sites, multitude of tools to characterize their reactions, and their synthetic flexibility makes such molecules logical starting points. However, it is increasingly clear that challenges exist in the applications of molecular catalysts at the scales needed to address modern chemical and energy demands. In this review, we discuss selected classes of molecular electrocatalysts and highlight their development and key features. Of special interest are proton‐coupled transformations of H 2 , O 2 , N 2 , CO 2 , and related small molecules. We also frame important thermodynamic features for different catalysts using new approaches and ask forward looking questions about their applications in practical systems.
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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) | 0.000 | 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".