Thermodynamic Stability in Acid Media of FeN<sub>4</sub>-Based Catalytic Sites Used for the Reaction of Oxygen Reduction in PEM Fuel Cells
Bibliographic record
Abstract
Two types of Fe-based catalytic sites have been proposed in the literature to perform the oxygen reduction reaction in PEM fuel cells running with FeN 4 -based electrocatalysts: [FeN 4 /C] or [FeN 4 C 12 ] derived from the molecular structure of iron-porphyrin, and [FeN 2+2 /C], derived from the structure of the iron complex with two phenanthroline molecules. The energetics and chemical thermodynamic stability (equilibrium constants, K c , for iron acid leaching) of these two types of sites and some of their oxygenated forms have been determined at pH ∼ 0. This does not consider the direct or indirect electrochemical oxidation of these catalytic sites or the electro-corrosion of their support. All evaluated FeN 4 -based sites are chemically stable in acid at both 298 and 353 K. It is their high values of T Δ S contribution to the Gibbs free energy for acid leaching which are responsible for the stability of these catalytic sites. The dioxygen and hydroxyl complexes of [FeN 4 C 12 ] and [FeN 2+2 /C] electrocatalysts demonstrate an improved stability with increasing temperature, explained by their electron withdrawing properties and their effect on the number of electrons in the antibonding orbitals. Complexes with dioxygen are more resistant to the action of acid than the ones formed by chemisorption of OH-groups.
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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) | 0.000 | 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".