Building Fe atom–cluster composite sites using a site occupation strategy to boost electrochemical oxygen reduction
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The high‐temperature pyrolysis process for preparing M–N–C single‐atom catalyst usually results in high heterogeneity in product structure concurrently contains multiscale metal phases from single atoms (SAs), atomic clusters to nanoparticles. Therefore, understanding the interactions among these components, especially the synergistic effects between single atomic sites and cluster sites, is crucial for improving the oxygen reduction reaction (ORR) activity of M–N–C catalysts. Accordingly, herein, we constructed a model catalyst composed of both atomically dispersed FeN 4 SA sites and adjacent Fe clusters through a site occupation strategy. We found that the Fe clusters can optimize the adsorption strength of oxygen reduction intermediates on FeN 4 SA sites by introducing electron‐withdrawing –OH ligands and decreasing the d‐band center of the Fe center. The as‐developed catalyst exhibits encouraging ORR activity with half‐wave potentials ( E 1/2 ) of 0.831 and 0.905 V in acidic and alkaline media, respectively. Moreover, the catalyst also represents excellent durability exceeding that of Fe–N–C SA catalyst. The practical application of Fe(Cd)–CN x catalyst is further validated by its superior activity and stability in a metal–air battery device. Our work exhibits the great potential of synergistic effects between multiphase metal species for improvements of single‐atom site catalysts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it