Facet Strain Strategy of Atomically Dispersed FeNC Catalyst for Efficient Oxygen Electrocatalysis
Bibliographic record
Abstract
Abstract Increasing the portion of highly active metal centers in atomically dispersed MNC catalysts is significant for the overall oxygen reduction reaction (ORR) performance. A “facet strain strategy” is designed by using a trans‐layer compressive strain of the {110} facet of FeCo nanoparticles encapsulated in graphitic FeNC layers to further activate the primitive FeN4 catalytic centers on the graphitic sub‐layer that are omitted in commonly direct access activation strategies. Using X‐ray absorption near‐edge spectroscopy and extended X‐ray absorption fine structure, the highly active FeN4 type is detected with compressed FeN bonds. Density functional theory calculation discloses that, in virtue of lattice mismatch, FeCo {110} facets transmit a trans‐layer compressive strain to reconstruct the FeN4 sites on surrounding graphitic sub‐layers to optimize the Fe‐OH* adsorption energy in the rate‐determining step. The redesigned catalyst exhibits enhanced ORR activity, outperforming the primitive FeNC and commercial Pt/C benchmarks. This study will enrich insights toward developing MN4 and nanoparticle composite electrocatalysts.
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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) | 0.001 | 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".