Theoretical Investigations of the Oxygen Reduction Reaction on Pt(111)
Bibliographic record
Abstract
Computational modeling can provide important insights into chemical reactions in both applied and fundamental fields of research. One of the most critical processes needed in practical renewable energy sources is the oxygen reduction reaction (ORR). Besides being the key process in combustion and corrosion, the ORR has an elusive mechanism that may proceed in a number of complicated reaction steps in electrochemical fuel cells. Indeed, the mechanism of the ORR on highly studied Pt(111) electrodes has been the subject of interest and debate for decades. Herein, we first outline the theory behind these types of simulations and then show how to use these quantum mechanical approaches and approximations to create a realistic model. After reviewing the performance of these methods in studying the binding of molecular oxygen to Pt(111), we then outline our own results in elucidating the ORR and its dependence on environmental parameters, such as solvent, thermodynamic energies, and the presence of an external electrode potential. This approach can, in principle, be applied to other equally complicated investigations of other surfaces or electrochemical reactions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".