Structure of the catalytic sites in Fe/N/C-catalysts for O2-reduction in PEM fuel cells
Bibliographic record
Abstract
Fe-based catalytic sites for the reduction of oxygen in acidic medium have been identified by (57)Fe Mössbauer spectroscopy of Fe/N/C catalysts containing 0.03 to 1.55 wt% Fe, which were prepared by impregnation of iron acetate on carbon black followed by heat-treatment in NH(3) at 950 °C. Four different Fe-species were detected at all iron concentrations: three doublets assigned to molecular FeN(4)-like sites with their ferrous ions in a low (D1), intermediate (D2) or high (D3) spin state, and two other doublets assigned to a single Fe-species (D4 and D5) consisting of surface oxidized nitride nanoparticles (Fe(x)N, with x≤ 2.1). A fifth Fe-species appears only in those catalysts with Fe-contents ≥0.27 wt%. It is characterized by a very broad singlet, which has been assigned to incomplete FeN(4)-like sites that quickly dissolve in contact with an acid. Among the five Fe-species identified in these catalysts, only D1 and D3 display catalytic activity for the oxygen reduction reaction (ORR) in the acid medium, with D3 featuring a composite structure with a protonated neighbour basic nitrogen and being by far the most active species, with an estimated turn over frequency for the ORR of 11.4 e(-) per site per s at 0.8 V vs. RHE. Moreover, all D1 sites and between 1/2 and 2/3 of the D3 sites are acid-resistant. A scheme for the mechanism of site formation upon heat-treatment is also proposed. This identification of the ORR-active sites in these catalysts is of crucial importance to design strategies to improve the catalytic activity and stability of these materials.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".