Carbon Nanosheets Containing Discrete Co-N<sub><i>x</i></sub>-B<sub><i>y</i></sub>-C Active Sites for Efficient Oxygen Electrocatalysis and Rechargeable Zn–Air Batteries
Bibliographic record
Abstract
Structural and compositional engineering of atomic-scaled metal−N−C catalysts is important yet challenging in boosting their performance for the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Here, boron (B)-doped Co−N−C active sites confined in hierarchical porous carbon sheets (denoted as Co-N,B-CSs) were obtained by a soft template self-assembly pyrolysis method. Significantly, the introduced B element gives an electron-deficient site that can activate the electron transfer around the Co−N−C sites, strengthen the interaction with oxygenated species, and thus accelerate reaction kinetics in the 4e − processed ORR and OER. As a result, the catalyst showed Pt-like ORR performance with a half-wave potential ( E 1/2 ) of 0.83 V versus ( vs ) RHE, a limiting current density of about 5.66 mA cm −2, and higher durability (almost no decay after 5000 cycles) than Pt/C catalysts. Moreover, a rechargeable Zn–air battery device comprising this Co-N,B-CSs catalyst shows superior performance with an open-circuit potential of ∼1.4 V, a peak power density of ∼100.4 mW cm −2, as well as excellent durability (128 cycles for 14 h of operation). DFT calculations further demonstrated that the coupling of Co-N x active sites with B atoms prefers to adsorb an O 2 molecule in side-on mode and accelerates ORR kinetics.
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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".