Highly Active NiRu/C Cathode Catalyst Synthesized by Displacement Reaction for Anion Exchange Membrane Water Electrolysis
Bibliographic record
Abstract
Abstract Anion exchange membrane water electrolysis (AEMWE) is highly promising for cost‐effective green hydrogen production due to its basic operating conditions facilitating the use of non‐noble catalysts. While non‐noble Ni/Fe‐based catalysts are utilized at the anode, its cathode catalyst still requires precious Pt. Due to the high cost of Pt and the sluggish hydrogen evolution reaction (HER) at the cathode in basic conditions, developing alternative catalysts to replace Pt is highly important. Here, a synthesis procedure for a Ru‐based catalyst is reported and its high activity toward the HER in alkaline media is demonstrated in both half‐cell and single‐cell tests. The catalyst is synthesized in a two‐step approach. A highly dispersed Ni catalyst is prepared on carbon support in the first step. In the second step, Ru is deposited on its surface using a galvanic displacement reaction. The uniqueness of this method is that Ru is deposited over the entire electrically conductive surface, resulting in an isotropic and homogeneous Ru distribution within the catalyst powder. It is demonstrated that this material remarkably outperforms state‐of‐the‐art Pt/C catalysts in half‐cell and single‐cell tests. The single cell only requires 1.73 V at 1 A cm −2 with an overall PGM content of 0.05 mg cm −2 .
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".