Ion Transport by Nanochannels in Ion-Containing Aromatic Copolymers
Bibliographic record
Abstract
The search for the next generation of highly ion-conducting polymer electrolyte membranes has been a subject of intense research because of their potential applications in energy storage and transformation devices, such as fuel cells, vanadium flow batteries, membrane-based artificial photosynthesis, water electrolysis, or water treatment processes such as electrodialysis desalination. Nanochannels that contain ionic groups, through which “hydrated” ions can pass, are believed to be of key importance for efficient ion transport in polymer electrolytes membranes. In this Perspective, we present an overview of the approaches to induce ion-conducting nanochannel formation by self-assembly, using polymer architecture such as block or comb-shaped copolymers. The transport properties of ion-containing aromatic copolymers are examined to obtain an insight into the fundamental behavior of these materials, which are targeted toward applications in fuel cells and other electrochemical devices. Challenges in obtaining well-defined nanochannel morphologies, and possible strategies to improve transport properties in aromatic copolymers having structures with the potential to withstand operation in electrochemical/chemical devices, are discussed. Opportunities for the application of ion-containing aromatic copolymer membranes in fuel cells, vanadium flow batteries, membrane-based artificial photosynthesis, electrolysis, and electrodialysis are also reviewed. Research needs for further improvements in ionic conductivity and durability, and their applications are identified.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".