Ion Transport by Nanochannels in Ion-Containing Aromatic Copolymers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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 it