Tuning the dual-active sites of ZIF-67 derived porous nanomaterials for boosting oxygen catalysis and rechargeable Zn-air batteries
Bibliographic record
Abstract
The rational control of the active site of metal-organic frameworks (MOFs) derived nanomaterials is essential to build efficient bifunctional oxygen reduction/evolution reaction (ORR/OER) catalysts. Accordingly, through designing and constructing a Co 3 O 4 -Co heterostructure embedded in Co, N co-doped carbon polyhedra derived (Co 3 O 4 -Co@NC) from the in-situ compositions of ZIF-67 and cobalt nanocrystals synthesized by the strategy of in-situ NaBH 4 reduction, the dual-active site (Co 3 O 4 -Co and Co-N x ) is synchronously realized in a MOFs derived nanomaterials. The formed Co 3 O 4 -Co@NC shows excellent bifunctional electrocatalytic activity with ultra-small potential gap (Δ E = E j =10 (OER) − E 1/2 (ORR)) of 0.72 V, which surpasses the commercial Pt/C and RuO 2 catalysts. The theory calculation results reveal that the excellent bifunctional electrocatalytic activity can be attributed to the charge redistribution of Co of Co-N x induced by the synergistic effects of well-tuned active sites of Co 3 O 4 -Co nanoparticle and Co-N x , thus optimizing the rate-determining step of the desorption of O 2 * intermediate in ORR and OH* intermediate in OER. The rechargeable Zn-air batteries with our bifunctional catalysts exhibit superior performance as well as high cycling stability. This simple-effective optimization strategy offers prospects for tuning the active site of MOF derived bifunctional catalyst in electrochemical energy devices.
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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) | 0.000 | 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 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".