Synthetic Strategies for Controlling the Morphology of Proton Conducting Polymer Membranes
Bibliographic record
Abstract
Abstract The nanostructure and morphology of proton conducting polymers is of considerable interest in the search for next generation materials and optimization of existing ones. Synthetic methodologies for tailoring molecular structures that promote nanoscopic phase separation of ionic and non‐ionic domains, and the effect of phase separation on parameters such as proton conductivity, are considered. Rather than distinguish proton conducting polymers according to chemical class, they are categorized under sub‐headings of random, block, and graft copolymers. The synthetic methodology available to access archetypal polymer structures is dependent on the nature of the monomers and restrictive compared to conventional non‐ionic polymer systems. Irrespective of the methodology, ionic aggregation and phase separation are consistently found to play an important role in the proton conductivity of low ion exchange capacity (IEC) membranes, but less of a role in highIECmembranes. Significant research is required to further develop relationships between polymer architecture, morphology, and electrolytic properties.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".