Anion Exchange Ionomer Binders for Alkaline Fuel Cells
Bibliographic record
Abstract
Anion exchange ionomer (AEI) binders are critical to the performance of alkaline electrochemical devices (i.e., fuel cells, electrolyzers, and batteries), as they facilitate ion transport, provide structural integrity, and improve the overall performance and lifespan of these devices. These binders not only ensure ion transport but also provide mechanical stability to the electrode materials. Recently, there has been significant progress in designing AEIs that are more compatible with existing electrode materials and electrolytes. This review summarizes the different types of AEI binders, focusing on their chemical structure, functionalization, conductivity, and how they affect the performance of alkaline fuel cells, specifically, anion exchange membrane fuel cells (AEMFCs). It also discusses how factors like functional groups, polymer backbone and side-chain flexibility, and ion exchange capacity balance conductivity, mechanical strength, and water uptake (WU). Recent advances in material design, such as polymer blends, composites, and crosslinked ionomers, as well as electrode setup, such as asymmetric ionomer electrodes, are explored as methods for improving stability and ion transport. The main challenges facing AEIs, including water management, alkaline degradation, phase separation, mechanical robustness, and long-term durability, are discussed along with strategies for overcoming them. Finally, we outline future research directions for developing scalable, economical solutions and integrating these binders with new electrode materials to help improve the performance and stability of next-generation AEMFCs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".