Exploring properties of hyperbranched polymers in anion exchange membranes for fuel cells and its potential integration for water electrolysis: A review
Bibliographic record
Abstract
The graphical abstract highlights the hyperbranched anion exchange membranes with linear, dendritic, and terminal units for efficient hydrogen production in water electrolysis. Anion-exchange membrane water electrolysers (AEMWEs) and fuel cells (AEMFCs) are critical technologies for converting renewable resources into green hydrogen (H 2 ), where anion-exchange membranes (AEMs) play a vital role in efficiently transporting hydroxide ions (OH − ) and minimizing fuel crossover, thus enhancing overall efficiency. While conventional AEMs with linear, side-chain, and block polymer architectures show promise through functionalization, their long-term performance remains a concern. To address this, hyperbranched polymers offer a promising alternative due to their three-dimensional structure, higher terminal functionality, and ease of functionalization. This unique architecture provides interconnected ion transport pathways, fractional free volume, and enhanced long-term stability in alkaline environments. Recent studies have achieved conductivities as high as 304.5 mS cm −1 , attributed to their improved fractional free volume and microphase separation in hyperbranched AEMs. This review explores the chemical, mechanical, and ionic properties of hyperbranched AEMs in AEMFCs and assesses their potential for application in AEMWEs. Strategies such as blending and structural functionalisation have significantly improved the properties by promoting microphase separation and increasing the density of cationic groups on the polymer surface. The review provides essential insights for future research, highlighting the challenges and opportunities in developing high-performance hyperbranched AEMs to advance hydrogen energy infrastructure.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".