Effect of casting solvent interactions on proton conductivity in sulfonated polymer (SPEEK) proton exchange membranes
Bibliographic record
Abstract
The proton conductivity of solvent-cast proton exchange membranes (PEM) prepared from sulfonated poly(ether ether ketone) (SPEEK) was found to be sensitive to the choice of casting solvent and the membrane drying conditions. Using proton nuclear magnetic resonance (NMR) spectroscopy, several factors were shown to affect proton conductivity in PEMs. NMR revealed the formation of strong interactions between SPEEK polymer and the casting solvents dimethylformamide (DMF) and dimethylacetamide (DMAc), commonly used in PEM preparation. DMF readily hydrogen-bonds with the polymers' sulfonic acid groups starting at temperatures as low as 60 C. However, DMAc is much less susceptible to hydrogen bonding, which occurs only above 100 C. Both DMF and DMAc solvent interactions with SPEEK during PEM preparation significantly affect the proton conductivity properties. In addition, both solvents are prone to thermal decomposition resulting in dimethylamine which also strongly hydrogen-bonds with SPEEK. The presence of residual sulfuric acid originating from SPEEK preparation has a catalytic effect on the thermal degradation of DMF and DMAc. Under high temperature treatment of films, residual sulfuric acid reacts with both DMF and DMAc, causing their degradation resulting in the formation of N,N-dimethylaminium hydrogensulfate which is also believed to have a negative effect on proton 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".