Microstructural orientation of anion exchange membrane through mechanical stretching for improved ion transport
Bibliographic record
Abstract
Synthesis of anion exchange membranes (AEMs) with orientated nano/micro-structure and with tunable ion-channels is of great interest for applications in fuel cells, water electrolyzers, and redox flow batteries. However, there is still a dearth of work in the detailed understanding of anion conductivity from a polymer structure‒property perspective. Herein, we demonstrate an easy and versatile strategy to fabricate highly conductive AEMs. By stretching the AEMs, an improvement in OH − conductivities of AEMs is achieved. The effect of elongation at different water contents on polymer structures and OH − conductivities was investigated by a combination of molecular dynamics (MD) simulation and experimental study, giving insights into macromolecular orientation at the atomic level. The morphological changes, which consist of oriented polymer chains and elongated water clusters, are quantified by a combination of two dimensional small angle X-ray scattering (SAXS), scanning electron microscopy (SEM) and radial distribution functions. Detailed analyses of interatomic distances reveal morphological variations of hydrophilic domains and their interactions with water and OH − under elongation at different hydration levels. Furthermore, the OH − conductivities of our synthesized quaternized poly(2,6-dimethyl-1,4-phenylene oxide) (QPPO) AEMs increased significantly after stretching to 20% elongation at all water contents. Specifically, OH − conductivity of stretched QPPO was 2.24 times more than the original AEM at 60% RH. The higher relative increase in OH − conductivity at lower water content may be caused by the lower flexibility of side chains at lower hydrated level. This work verifies the significance of porous and/or oriented AEM structure in the improvement on anion conductivity and water transport efficiency.
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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 | 0.001 |
| 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".