The Performance of Nickel and Nickel-Iron Catalysts Evaluated As Anodes in Anion Exchange Membrane Water Electrolysis
Bibliographic record
Abstract
Anion exchange membrane water electrolysis (AEMWE) is an efficient, cost-effective solution to renewable energy storage. The process includes oxygen and hydrogen evolution reactions (OER and HER); the OER is kinetically unfavourable. Studies have shown that nickel (Ni)- iron (Fe) catalysts enhance activity towards OER, and cerium oxide (CeO2) supports have shown positive effects on catalytic performance. This study covers the preliminary evaluation of Ni, Ni90Fe10 (at%) and Ni90Fe10/CeO2 (50 wt%) nanoparticles (NPs), synthesized by chemical reduction, as OER catalysts in AEMWE using commercial membranes. Transmission electron microscopy (TEM) images of the Ni-based NPs indicate NPs roughly 4–6 nm in size. Three-electrode cell measurements indicate that Ni90Fe10 is the most active non-noble metal catalyst in 1 and 0.1 M KOH. AEMWE measurements of the anodes show cells achieving overall cell voltages between 1.85 and 1.90 V at 2 A cm−2 in 1 M KOH at 50 °C, which is comparable to the selected iridium-black reference catalyst. In 0.1 M KOH, the AEMWE cell containing Ni90Fe10 attained the lowest voltage of 1.99 V at 2 A cm−2. Electrochemical impedance spectroscopy (EIS) of the AEMWE cells using Ni90Fe10/CeO2 showed a higher ohmic resistance than all catalysts, indicating the need for support optimization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".