Enhanced NiFe2O4 Catalyst Performance and Stability in Anion Exchange Membrane Water Electrolysis: Influence of Iron Content and Membrane Selection
Bibliographic record
Abstract
Anion exchange membrane (AEM) water electrolysis is a potentially inexpensive and efficient source of hydrogen production as it uses effective low-cost catalysts. The catalytic activity and performance of nickel iron oxide (NiFeOx) catalysts for hydrogen production in AEM water electrolyzers were investigated. The NiFeOx catalysts were synthesized with various iron content weight percentages, and at the stoichiometric ratio for nickel ferrite (NiFe2O4). The catalytic activity of NiFeOx catalyst was evaluated by linear sweep voltammetry (LSV) and chronoamperometry for the oxygen evolution reaction (OER). NiFe2O4 showed the highest activity for the OER in a three-electrode system, with 320 mA cm−2 at 2 V in 1 M KOH solution. NiFe2O4 displayed strong stability over a 600 h period at 50 mA cm−2 in a three-electrode setup, with a degradation rate of 15 μV/h. In single-cell electrolysis using a X-37 T membrane, at 2.2 V in 1 M KOH, the NiFe2O4 catalyst had the highest activity of 1100 mA cm−2 at 45 °C, which increased with the temperature to 1503 mA cm−2 at 55 °C. The performance of various membranes was examined, and the highest performance of the tested membranes was determined to be that of the Fumatech FAA-3-50 and FAS-50 membranes, implying that membrane performance is strongly correlated with membrane conductivity. The obtained Nyquist plots and equivalent circuit analysis were used to determine cell resistances. It was found that ohmic resistance decreases with an increase in temperature from 45 °C to 55 °C, implying the positive effect of temperature on AEM electrolysis. The FAA-3-50 and FAS-50 membranes were determined to have lower activation and ohmic resistances, indicative of higher conductivity and faster membrane charge transfer. NiFe2O4 in an AEM water electrolyzer displayed strong stability, with a voltage degradation rate of 0.833 mV/h over the 12 h durability test.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".