Ion Conductivity of Polyelectrolyte Hydrogels with Varying Compositions
Bibliographic record
Abstract
Ion transport in polyelectrolyte (PE) hydrogels is governed by a complex interplay between charge distribution, network architecture, and ionic interactions; however, the role of hydrogel composition in ion conductivity remains elusive. Here, we report the results of an experimental and simulation study of ion conductivity and ion mobility in PE gels formed from random copolymers containing charged and charge-neutral repeat units. For anionic or cationic copolymers with H + or Cl – counterions, respectively, control over charge concentration and pore size was achieved by systematically varying the fraction of charged monomers and the cross-linking density of the hydrogel. We show that the dependence of ion mobility on charge concentration becomes stronger in hydrogels formed by the copolymers with a reduced fraction of charged repeat units. Moreover, the variation in the mobility of H + ions is more sensitive to hydrogel composition than that of the Cl – ions, thus highlighting ion-specific effects. The experimental results are in agreement with the simulation. These findings provide insight into the mechanisms of ion transport in compositionally heterogeneous PE networks and offer design principles for creating functional biomimetic hydrogels with tunable ionic conductivity.
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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".