Tailoring Hydrogen Evolution Performance: Size and Phase Engineering of Ruthenium Nanoparticles
Bibliographic record
Abstract
Ruthenium-based nanomaterials have seen increased interest as an alternative to platinum electrocatalysts for the hydrogen evolution reaction (HER). In this study, ruthenium nanoparticles supported on a graphene-based composite consisting of expanded graphite and reduced graphene oxide were successfully prepared by using a one-step thermal method. The nanocomposite was optimized for alkaline HER performance by varying the expanded graphite content and annealing temperature, exhibiting an overpotential of 54 mV to achieve 10 mA cm –2, outperforming the benchmark 20% Pt/C. Through surface characterization of the nanocomposite, the high electrocatalytic activity and stability were found to originate from the interconnected microstructure, optimized porosity, tuned Ru particle size, and homogeneous particle dispersion, revealing the key roles of each component. Using X-ray absorption and X-ray total scattering techniques, the electrochemical performance of the nanocomposite was found to depend on a balance between the size and quality of the ruthenium nanoparticles. The catalyst design principles demonstrated in this work can be applied to streamline and simplify the processes used to develop advanced HER electrocatalysts and other energy storage and conversion materials.
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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".