Engineering the Morphology and Microenvironment of a Graphene‐Supported Co‐N‐C Single‐Atom Electrocatalyst for Enhanced Hydrogen Evolution
Bibliographic record
Abstract
Abstract Graphene‐supported single‐atom catalysts (SACs) are promising alternatives to precious metals for catalyzing the technologically important hydrogen evolution reaction (HER), but their performances are limited by the low intrinsic activity and insufficient mass transport. Herein, a highly HER‐active graphene‐supported Co‐N‐C SAC is reported with unique design features in the morphology of the substrate and the microenvironment of the single metal sites: i) the crumpled and scrolled morphology of the graphene substrate circumvents the issues encountered by stacked nanoplatelets, resulting in improved exposure of the electrode/electrolyte interfaces (≈10 times enhancement); ii) the in‐plane holes in graphene preferentially orientate the Co atoms at the edge sites with low‐coordinated Co‐N3 configuration that exhibits enhanced intrinsic activity (≈2.6 times enhancement compared to the conventional Co‐N4 moiety), as evidenced by detailed experiments and density functional theory calculations. As a result, this catalyst exhibits significantly improved HER activity with an overpotential (η) of merely 82 mV at 10 mA cm−2, a small Tafel slope of 59.0 mV dec−1 and a turnover frequency of 0.81 s−1 at η = 100 mV, ranking it among the best Co‐N‐C SACs.
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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".