Optimization of hydrophilic/hydrophobic phase separation in sPEEK membranes by hydrothermal treatments
Bibliographic record
Abstract
Via SAXS, herein, we studied how a sPEEK microstructure evolves when it is immersed in water at a wide range of temperatures (20-100 °C) and time scales (from a few hours to dozens of days). In particular, we scrutinized the behavior of sPEEK at the temperature and time associated with the appearance of a well-defined nanosegregated morphology. At 80 °C, we observed nanoscale swelling along with smoothing of the water/polymer interface over a long period of time (several days). Herein, two of the main membrane properties, i.e., water uptake and proton conductivity, were studied for different immersion times and temperatures. It was found that the abovementioned properties were remarkably correlated with the evolution of the membrane microstructure, which was partly conserved after drying. The present findings helped us to understand that the thermally activated evolutions observed at both the nanoscale and macroscale were associated with the sPEEK β-relaxation crossover. Therefore, the very different swelling behaviors of sPEEK and Nafion are correlated to the much higher β-relaxation of sPEEK vs. Nafion (75 °C vs. -20 °C - dry state). From a practical viewpoint, this study emphasizes, for membranes alternative to Nafion, the importance and impact of the membrane pretreatment on their functional 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 | 0.000 |
| 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".