FeNC Oxygen Reduction Electrocatalyst with High Utilisation Penta-coordinated sites
Bibliographic record
Abstract
Atomic Fe in N-doped carbon (FeNC) electrocatalysts for oxygen (O2) reduction at the cathode of proton exchange membrane fuel cells (PEMFCs) are the most promising alternative to platinum-group-metal catalysts. Despite recent progress on atomic FeNC O2 reduction, their controlled synthesis and stability for practical applications remains challenging. A two-step synthesis approach has recently led to significant advances in terms of Fe-loading and mass activity; however, the Fe utilisation remains low owing to the difficulty of building scaffolds with sufficient porosity that electrochemically exposes the active sites. Herein, we addressed this issue by coordinating Fe in a highly porous nitrogen doped carbon support (~3295 m2 g-1), prepared by pyrolysis of inexpensive 2,4,6-triaminopyrimidine and a Mg2+ salt active site template and porogen. Upon Fe coordination, a high electrochemical active site density of 2.54×10^19 sites gFeNC-1 and a record 52% FeNx electrochemical utilisation based on in situ nitrite stripping was achieved. The Fe single atoms are characterised pre- and post-electrochemical accelerated stress testing by aberration-corrected high-angle annular dark field scanning transmission electron microscopy, showing no Fe clustering. Moreover, ex situ X-ray absorption spectroscopy and low-temperature Mössbauer spectroscopy suggest the presence of penta-coordinated Fe sites, which were further studied by density functional theory calculations.
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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) | 0.000 | 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".