Engineering Single‐Atom Cobalt Catalysts toward Improved Electrocatalysis
Bibliographic record
Abstract
The development of cost-effective catalysts to replace noble metal is attracting increasing interests in many fields of catalysis and energy, and intensive efforts are focused on the integration of transition-metal sites in carbon as noble-metal-free candidates. Recently, the discovery of single-atom dispersed catalyst (SAC) provides a new frontier in heterogeneous catalysis. However, the electrocatalytic application of SAC is still subject to several theoretical and experimental limitations. Further advances depend on a better design of SAC through optimizing its interaction with adsorbates during catalysis. Here, distinctive from previous studies, favorable 3d electronic occupation and enhanced metal-adsorbates interactions in single-atom centers via the construction of nonplanar coordination is achieved, which is confirmed by advanced X-ray spectroscopic and electrochemical studies. The as-designed atomically dispersed cobalt sites within nonplanar coordination show significantly improved catalytic activity and selectivity toward the oxygen reduction reaction, approaching the benchmark Pt-based catalysts. More importantly, the illustration of the active sites in SAC indicates metal-natured catalytic sites and a media-dependent catalytic pathway. Achieving structural and electronic engineering on SAC that promotes its catalytic performances provides a paradigm to bridge the gap between single-atom catalysts design and electrocatalytic applications.
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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 | 0.001 | 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".