Engineering Molecular Heterostructured Catalyst for Oxygen Reduction Reaction
Bibliographic record
Abstract
Introducing a second metal species into atomically dispersed metal–nitrogen–carbon (M–N–C) catalysts to construct diatomic sites (DASs) is an effective strategy to elevate their activities and stabilities. However, the common pyrolysis-based method usually leads to substantial uncertainty for the formation of DASs, and the precise identification of the resulting DASs is also rather difficult. In this regard, we developed a two-step specific-adsorption strategy (pyrolysis-free) and constructed a DAS catalyst featuring FeCo “molecular heterostructures” (FeCo-MHs). In order to rule out the possibility of the two apparently neighboring (in the electron microscopy image) Fe/Co atoms being dispersed respectively on the top/bottom surfaces of the carbon support and thus forming “false” MHs, we conducted in situ rotation (by 8°, far above the critical angle of 5.3°) and directly identified the individual FeCo-MHs. The formation of FeCo-MHs could modulate the magnetic moments of the metal centers and increase the ratio of low-spin Fe(II)–N 4 moiety; thus the intrinsic activity could be optimized at the apex of the volcano-plot (a relationship as a function of magnetic moments of metal–phthalocyanine complexes and catalytic activities). The FeCo-MHs catalyst displays an exceptional ORR activity ( E 1/2 = 0.95 V) and could be used to construct high-performance cathodes for hydroxide exchange membrane fuel cells and zinc–air batteries.
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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".