Engineering the synergistic effect of carbon dots‐stabilized atomic and subnanometric ruthenium as highly efficient electrocatalysts for robust hydrogen evolution
Bibliographic record
Abstract
Abstract Currently, the most efficient electrocatalyst for the hydrogen evolution reaction (HER) in water dissociation is Pt‐based catalyst. Unfortunately, the high cost and less than perfect efficiency hinder wide‐range industrial/technological applications. Here, by controlling the treatment temperature of tris (2,2‐bipyridine) ruthenium dichloride hexahydrate, synthesis of compounds with novel ruthenium single/dual atoms (Ru S/DAs) mixed with Ru nanoclusters (Ru S/DAs + Ru NCs) and supported by carbon dots is demonstrated. These compounds are shown to be highly efficient and competitive catalysts for hydrogen evolution. Ru S/DAs + Ru NCs exhibit very high activity, with overpotentials of 15 and 40 mV at a current density of 10 mA/cm2 in 1.0 mol/L KOH and 0.5 mol/L H2SO4, respectively. Furthermore, the composites are found to possess outstanding stability and rapid HER kinetics. X ray absorption fine structure analysis, supported by density functional theory calculations, shows charge rearrangement in single‐atomic Ru, and the Ru dual sites promote active hydrogen adsorption and recombination. Ru S/DAs and Ru NCs demonstrate high electroactivity due to the electroactive Ru 4d orbitals. The introduction of Ru NCs activates the carbon support, providing a high electronic conductivity to transfer electrons from Ru NCs to Ru S/DAs, and facilitates water dissociation for the HER process.
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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".