Performance Evaluation and Durability Analysis of NiFeCoOx Catalysts for Alkaline Water Electrolysis in Anion Exchange Membrane Electrolyzers
Bibliographic record
Abstract
This study examines the catalytic activity of NiFeCoOx catalysts for anion exchange membrane (AEM) water electrolysis. The catalysts were synthesized with a Ni to Co ratio of 2:1 and Fe content ranges from 2.5 to 12.5 wt%. The catalysts were characterized using scanning electron microscopy (SEM) and X-ray diffraction (XRD) techniques. The catalytic activity of the NiFeCoOx catalysts was evaluated through linear sweep voltammetry (LSV) and chronoamperometry (CA) experiments for the oxygen evolution reaction (OER). The catalyst with 5% Fe content exhibited the highest catalytic activity, achieving an overpotential of 228 mV at a current density of 10 mA cm−2. Long-term catalyst testing for the OER at 50 mA cm−2 showed stable electrolysis operation for 100 h. The catalyst was further analyzed in an AEM water electrolyzer in a single-cell test, and the NiFeCoOx catalyst with 5% Fe at the anode demonstrated the highest current densities of 1516 mA cm−2 and 1620 mA cm−2 at 55 °C and 70 °C at 2.1 V. The maximum current density of 1880 mA cm−2 was achieved at 2.2 V and 70 °C. The Nyquist plot analysis of electrolysis at 55 °C showed that the NiFeCoOx catalyst with 5% Fe had lower activation resistance compared with the other Fe loadings, indicating enhanced performance. The durability test was performed for 8 h, showing stable AEM water electrolysis with minimum degradation. An overall cell efficiency of 70.5% was achieved for the operation carried out at a higher current density of 0.8 A 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".