Role of Ionomers in Anion Exchange Membrane Water Electrolysis: Is Aemion the Answer for Nickel-Based Anodes?
Bibliographic record
Abstract
The anode oxygen evolution reaction (OER) in anion exchange membrane water electrolysis (AEMWE) limits the process’ overall hydrogen production efficiency. Studies show that nickel-iron (NiFe)-based catalysts show excellent activity toward the OER. In anode catalyst layers, electrocatalysts must be paired with anion exchange ionomers (AEI) to bind the catalyst and conduct hydroxide ions. This work covers the first investigation of the commercial Aemion AEI with a non-noble-metal Ni 90 Fe 10 nanoparticle anode catalyst for applications in AEMWE. The effects of Aemion are also studied for the first time in a three-electrode cell and compared to the commercial Fumion and Nafion ionomers. Cyclic voltammetry (CV) results show that Aemion distinctly interacts with NiFe to suppress the Ni(OH) 2 /NiOOH transition peak current by 39% (vs 11 and 17% for Nafion and Fumion, respectively), thus decreasing the OER activity of NiFe with a high overpotential of 369 mV at 10 mA cm –2 in 1 M potassium hydroxide (KOH). This effect was not alleviated by prolonged CV cycling, preconditioning the electrode in KOH, stabilizing the electrode deposition, or modifying the Aemion solvent. NiFe anode catalytic layers were also prepared for AEMWE testing with varying amounts of Aemion (7, 15, 25, and 35 wt %). Scanning electron microscopy (SEM) of the catalyst layers show catalyst-rich and ionomer-rich phases, each becoming more prominent with increasing ionomer. AEMWE testing shows that 7 wt % Aemion is the best ionomer loading, achieving a cell voltage of 1.941 V at 0.4 A cm –2 in 1 M KOH at 50 °C, 62 mV higher than our previously optimized 15 wt % Fumion anode. While less performing than Fumion, Ni 90 Fe 10 with 7 wt % Aemion is more stable over time. Ex situ Raman spectroscopy of the spent 7 wt % Aemion electrode supports the CV results, where the electrode remains mostly in the Ni(OH) 2 phase after polarization.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".