Ionomer Content Optimization in Ni-Based Anodes for Alkaline Exchange Membrane Water Electrolysis
Bibliographic record
Abstract
Anion exchange membrane water electrolysis (AEMWE) is an efficient and cost-effective solution to renewable energy storage due to its compact cell design and potential to operate using non-noble metal catalysts. [1] A known challenge to water electrolysis is the kinetically unfavourable oxygen evolution half-cell reaction (OER). Studies have shown that combining nickel (Ni) with small quantities of iron (Fe) can significantly enhances activity towards OER [2], and it has been shown that adding cerium oxide (CeO2), an oxygen conducting support, to catalyst materials can enhance catalytic processes. [3] AEMWE electrodes typically consist of a catalyst material, an ionomer and a solvent. For an efficient cell design, it is crucial to have an active catalyst, as well as the right amount of ionomer to properly stabilize and bind the catalytic layer, while still allowing for high hydroxide ion transport [4]. As such, this study covers the ionomer content optimization of NiFe-based nanoparticles (NPs) supported on CeO2 for anodes in AEMWE using a catalyst coated surface (CCS) electrode configuration. Preliminary AEMWE testing of the NiFe materials without ink optimization is covered in our previous work [5]. The materials tested were synthesized by chemical reduction in ethanol with sodium borohydride, and in-situ testing was done with a Fumatech membrane and ionomer. The ionomer content was tested on pure Ni NPs and the amounts tested were 7, 15, 25, 35 and 45 wt%. Scanning Electron Microscopy (SEM) of the resulting electrodes, Particle-size Distribution (PSD) of the catalyst inks, and in-situ testing showed that the best and most active catalytic layer was formed with 15 wt% ionomer. With the optimal ionomer content found, a more rigorous evaluation of AEMWE performance was carried out on NixFe(100-x) (x=90, 80 at%) both of which were also tested with 10 wt% CeO2. Polarization curves and electrochemical impedance spectroscopy results in 1 M and 0.1 M KOH are summarized and discussed. Additionally, catalyst stability of each of the materials was characterized by holding 0.5 A cm-2 for 12 hours, while measuring impedance every hour. Future work of this study will include long-term stability testing as well as testing the performance of different commercial and lab produced ionomer-membrane combinations with the Ni-based materials. [1] M. Tahir, L. Pan, F. Idrees, X. Zhang, L. Wang, J. Zou and Z. L. Wang, Nano Energy 2017, 37, 136. [2] M. Gong and H Dai, Nano Research 2015, 8, 23. [3] H. A. E. Dole and E. A. Baranova, ChemCatChem 2016, 8, 1977. [4] G. Li, D. Yang and P. A. Chuang, ACS Catal. 2018, 8, 11688. [5] E. Cossar, A. O. Barnett, F. Seland, E. A. Baranova, Catalysts 2019, 9, 814.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".