Effect of Transition Metals on the Oxygen Reduction Reaction Activity at Metal‐N<sub>3</sub>/C Active Sites
Bibliographic record
Abstract
Abstract The roles of various transition and post‐transition metals in model non‐precious‐metal catalysts for the oxygen reduction reaction (ORR) are reported after being prepared via a molecularly defined terpyridine unit covalently attached to a carbon black support. We previously reported the use of a terpyridine‐modified iron‐based catalyst that allowed for the controlled deposition of highly active nitrogen functionalities on the carbon support, which adopts a Fe−N3/C active site formation. In this work, we expand on this idea by altering the metal center in the predefined active sites M−N3/C and compare the ORR reactivity of the isostructural set of catalysts, where M=Fe, Co, Ni, Mn, and Sn. The results show that the iron‐based material was the most active catalyst in acid, whereas the cobalt‐based catalyst was most active in base. In addition, nickel‐ and manganese‐based materials showed promising activity for the ORR in both acidic and basic media. We demonstrate that, with a suitable templating bis‐chelating nitrogenous ligand, the M−N3/C active site geometry is adopted with a wide range of non‐precious‐metal centers on a Vulcan carbon surface, and the resulting catalysts are ORR active in a range of conditions, further confirming the tunability and versatility of the N3 site. As expected, post‐transition metals, such as tin, do not coordinate to the N3 nitrogenous ligand under synthetic conditions and deposits tin oxides(s). This study confirms the generality of the phenomenon of M−N3/C as an ORR catalytic site.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".