The Role of Ion Hydration in the Performance of Li<sub>2</sub>SO<sub>4</sub>–Polyacrylamide Electrolyte Systems: Material Characterizations under Real-Time Conditions
Bibliographic record
Abstract
Li 2 SO 4 –polyacryalamide (PAM) neutral pH polymer electrolytes with different salt:polymer molar ratios (5000:1 and 10000:1) were characterized for their ionic conductivity and material properties. Their ionic conductivity over time showed different trends: Li 2 SO 4 –PAM(5000:1) increased while Li 2 SO 4 –PAM(10000:1) decreased. Materials characterizations of the freestanding films were conducted to identify the cause of the difference in conductivity trends. X-ray diffraction and IR spectroscopy suggested slight differences in film crystallinity and sulfate ion bonding structure, but they were not conclusive. Raman spectroscopy proved to be a better tool as it revealed distinct characteristic peaks for different sulfate ion and water interactions. To correlate the film properties with electrolyte performance, a real-time tracking technique to correlate ionic conductivity and the vibrational spectroscopic responses was developed. Leveraging this approach, the level of hydration surrounding the salt molecule was identified as the determining factor of ionic conductivity in this polymer system. This approach can be extended to predict the shelf and service life of this Li 2 SO 4 –PAM system and to optimize the next generations of electrolytes.
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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 | 0.001 |
| 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 | 0.000 |
| 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".