Carbon supported NiRu nanoparticles as effective hydrogen evolution catalysts for anion exchange membrane water electrolyzers
Bibliographic record
Abstract
Abstract Establishing anion exchange membrane water electrolysis (AEMWE) as a new technology for efficient hydrogen production requires cost-effective and high-performance catalyst materials. Here, we report the synthesis and comprehensive characterization of carbon supported NiRu alloy nanoparticles as a cost-effective hydrogen evolution reaction catalyst for AEMWEs. Different NiRu catalysts were synthesized using a facile and scalable impregnation method. Half-cell results showed the ‘NiRu’ catalyst with ca. 10 wt.% Ru to exhibit an increased noble metal mass activity and slightly decreased Tafel slope compared to a commercial Pt/C catalyst with 60 wt.% Pt. Further, we report the application of NiRu/C as a cathodic catalyst in AEMWE full cell for the first time. In full cell tests, the synthesized catalysts exhibit 2 A cm−2 at 1.95 V with a low loading of 0.1 mgPGM cm−2 at the cathode.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".