Energetics and thermodynamic stability of potential Fe<sup>(II)</sup>‐hexa‐aza‐active sites for O<sub>2</sub> reduction in PEM fuel cells
Bibliographic record
Abstract
Abstract We present here a thermodynamic assessment of the stability behavior in acid environment at 298 and 353 K (80°C) of two iron (II) hexa‐aza‐macrocyclic complexes and of an hexa‐aza‐iron‐based site (FeIIN(4+2)/C) that should potentially be active for the oxygen reduction reaction in proton exchange membrane (PEM) fuel cells. The calculations of the equilibrium constant (Kc) for the demetallation reaction indicate that the iron (II)‐hexa‐aza‐macrocyclic complexes and FeIIN(4+2)/C are chemically stable in an acid medium at 298 and 353 K. Compared with two other potential model sites (FeIIN4/C and FeIIN(2+2)/C) that were thought to be present in the same Fe‐based catalysts, Kc of FeIIN(4+2)/C is two to three orders of magnitude smaller at 353 K, and three to four orders of magnitude smaller at 298 K, than Kc for FeIIN4/C or FeIIN(2+2)/C, revealing the great chemical stability of FeIIN(4+2)/C. In this work, we discuss about a novel proposition that the two catalytic sites active in these Fe‐based catalysts are FeIIN4/C and FeIIN(4+2)/C. This proposition is in agreement with the durability behavior of these catalysts in PEM fuel cells and also with their known physico‐chemical characterizations. The origin of the fast and slow decay behaviors of the different sites, which are active at the Fe–N–C‐based cathode of PEM fuel cells, is also discussed.
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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 | 0.001 | 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".