Direct single-atom/cluster interactions induce electron decoupling to promote the oxygen reduction reaction
Bibliographic record
Abstract
Nitrogen-doped carbon-supported transition metal single atoms (TM-N-C) catalysts have demonstrated advanced oxygen reduction reaction (ORR) kinetics. However, the strong coupling of electrons between metal sites and the substrate results in suboptimal O 2 adsorption and activation behavior, which still needs to be addressed. In this work, we construct Fe-O-Fe electronic bridges between single atoms and clusters via an in situ cluster trimming strategy, effectively alleviating the electron confinement at single-atom sites and achieving optimized O 2 catalytic behavior. Specifically, in situ electrochemical experiments and theoretical analyses demonstrate that the optimized electron transfer capability not only provides thermodynamic advantages in the O 2 adsorption process and reduces adsorption resistance but also significantly activates the O O bonds, effectively breaking the trade-off between activity and stability. The target catalyst exhibited outstanding ORR catalytic activity (onset potential, E onset = 1.001 V; half-wave potential, E 1/2 = 0.945 V) as well as enhanced catalytic stability in practical zinc-air batteries (over 1500 h at 10 mA cm −2 ). The electronic bridge structure linking single atoms and clusters reported in this study unlocks new avenues for the rational design of next-generation TM-N-C catalysts. • In situ cluster-trimming enables direct single atom - cluster interaction. • Direct interaction decouples active site electronics, enhancing oxygen adsorption and activation. • DFT and in situ spectroscopy reveal boosted O O bond cleavage and 4e − ORR pathway. • The catalyst delivers over 1500 h of stability and high performance in practical Zn-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) | 0.002 | 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".