Determination of Iron Active Sites in Pyrolyzed Iron-Based Catalysts for the Oxygen Reduction Reaction
Bibliographic record
Abstract
Fe-based oxygen reduction reaction (ORR) catalyst materials are considered promising nonprecious alternatives to traditional platinum-based catalysts. These catalyst materials are generally produced by high-temperature pyrolysis treatments of readily available carbon, nitrogen, and iron sources. Adequate control of the structure and active site formation during pyrolysis methods is nearly impossible. Thus, the chemical nature, structure, and ORR mechanism of catalytically active sites in these materials is a subject of significant debate. We have proposed a method, utilizing CN – ions as ORR inhibitors on Fe-based catalysts, to provide insight into the exact nature and chemistry of the catalytically active sites. Moreover, we propose two possible catalytically active site formation mechanisms occurring during high-temperature pyrolysis treatments, dependent on the specific type of precursor and synthesis methods utilized. We have further provided direct evidence of our proposed active site formations using ToF-SIMS negative and positive ion imaging. This knowledge will be beneficial to future work directed at the development of Fe-based catalysts with improved ORR activity and operational stabilities for fuel cell and battery applications.
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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".