Rational Design of Graphene‐Supported Single Atom Catalysts for Hydrogen Evolution Reaction
Bibliographic record
Abstract
Abstract The proper choice of nonprecious transition metals as single atom catalysts (SACs) remains unclear for designing highly efficient electrocatalysts for hydrogen evolution reaction (HER). Herein, reported is an activity correlation with catalysts, electronic structure, in order to clarify the origin of reactivity for a series of transition metals supported on nitrogen‐doped graphene as SACs for HER by a combination of density functional theory calculations and electrochemical measurements. Only few of the transition metals (e.g., Co, Cr, Fe, Rh, and V) as SACs show good catalytic activity toward HER as their Gibbs free energies are varied between the range of –0.20 to 0.30 eV but among which Co‐SAC exhibits the highest electrochemical activity at 0.13 eV. Electronic structure studies show that the energy states of active valence d z 2 orbitals and their resulting antibonding state determine the catalytic activity for HER. The fact that the antibonding state orbital is neither completely empty nor fully filled in the case of Co‐SAC is the main reason for its ideal hydrogen adsorption energy. Moreover, the electrochemical measurement shows that Co‐SAC exhibits a superior hydrogen evolution activity over Ni‐SAC and W‐SAC, confirming the theoretical calculation. This systematic study gives a fundamental understanding about the design of highly efficient SACs for HER.
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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".