Graphene Quantum Dots Supported by Graphene Nanoribbons with Ultrahigh Electrocatalytic Performance for Oxygen Reduction
Bibliographic record
Abstract
A new class of oxygen reduction reaction (ORR) catalysts based on graphene quantum dots (GQDs) supported by graphene nanoribbons (GNRs) has been developed through a one-step simultaneous reduction reaction, leading to ultrahigh performance for O reduction with an excellent electrocatalytic activity (higher limiting current density and lower overpotential than those of platinum) and high selectivity and stability in alkaline media comparable to the best C-based ORR catalysts reported so far. Electron microscopy revealed numerous surface/edge defects on the GQD/GNR surfaces and at their interface to act as the active sites. This, coupled with efficient charge transfer between the intimately contacted GQDs and GNRs, rationalized the observed ultrahigh electrocatalytic performance for the resultant GQD-GNR hybrids. Thus, this study opens a new direction for developing low-cost, highly efficient, C-based ORR electrocatalysts.
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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".