Non-PGM Electrocatalysts for PEM Fuel Cells: Thermodynamic Stability and DFT Evaluation of Fluorinated FeN<sub>4</sub>-Based ORR Catalysts
Bibliographic record
Abstract
Two types of FeN 4 -based catalytic sites have been proposed in the literature to perform the oxygen reduction reaction (ORR) in PEM fuel cells running with a Fe-based catalyst.They are FeN 4 /C, derived from the molecular structure of iron porphyrin, and FeN (2+2) /C, derived from the structure of the iron complex with two 1,10-phenanthroline molecules.The energetics and thermodynamic stability (equilibrium constant, K c , for iron acid leaching at pH ∼ 0) of these two types of sites were determined and were already reported in this journal [J.Electrochem.Soc., 164(9) F948-F957 ( 2017)].This work deals with the same FeN 4 sites but this time after their fluorination on the central Fe atom and on their carbon support.All evaluated fluorinated FeN 4 -based sites are stable at both 298 and 353 K, especially F-FeN 4 /C(F) and F 2 -FeN (2+2) /C(F).Interatomic d Fe-N , d Fe-F , and bond energies E Fe-F derived in the frame of the thermodynamic calculations were compared with the values obtained by density functional theory (DFT) calculations, and an agreement was found.DFT was also used to assess the ORR capability of both F-FeN 4 /C and F-FeN (2+2) /C.Both fluorinated sites were found to be ORR active when O 2 was bonded to the free side of the Fe atom, opposite to the F atom.
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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) | 0.001 | 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 | 0.000 | 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".