Joule heating to grain-boundary-rich RuP<sub>2</sub> for efficient electrocatalytic hydrogen evolution in a wide pH range
Bibliographic record
Abstract
The production of storable hydrogen fuel through water splitting, powered by renewable energy sources such as solar photovoltaics, wind turbines, and hydropower systems, represents a promising path toward achieving sustainable energy solutions. Transition-metal phosphides (TMPs) have excellent physicochemical properties, making them the most promising electrocatalysts for hydrogen evolution reaction (HER). Traditionally, achieving good crystallinity in these TMPs typically requires prolonged (≥ 2 h) high-temperature pyrolysis, which is time-consuming and generally yields samples with large particle sizes, adversely affecting the catalytic activities. Herein, for the first time, we present a groundbreaking discovery in the synthesis of grain-boundary-rich RuP2 nanoparticles within a very short time frame of nine seconds, using a fast Joule heating strategy (RuP2 JH). Subsequent electrochemical tests reveal that the as-synthesized RuP2 JH not only exhibits platinum-like HER activity, achieving overpotentials of 22 mV, 22 mV and 270 mV to reach a current density of 10 mA cm-2 in 0.5 M H2SO4, 1.0 M KOH, and 0.1 M phosphate buffered solutions, respectively, but also exhibits exceptional long-term stability. Moreover, it exhibits a Faradaic efficiency exceeding 96%. This work significantly contributes to the expanding repertoire of TMPs synthesized via Joule heating by showcasing exceptional performance toward HER and other energy-related catalytic applications.
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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".