Preparation and Characterization of Blending Sulfonated Poly(ether ether ketone) with Charged Surface Modifying Macromolecules
Bibliographic record
Abstract
Charged surface modifying macromolecule (cSMM) end capped with sulfonic group was prepared and blended into sulfonated poly(ether ether ketone) (SPEEK). The modified membrane was characterized for polymer electrolyte membrane application; i.e. a SPEEK/cSMM blend membrane was compared to a SPEEK membrane and a Nafion 112 membrane for the thermal and mechanical stability, hydrophilicity and water uptake. Thermal and mechanical stability of the blend membrane were slightly reduced from that of the SPEEK membrane but still higher than that of the Nafion 112 membrane. The cSMM which was end–capped with sulfonic group has increased the total –SO3H group in the blend membrane. As a result, the blend membrane showed an increase in hydrophilicity and water uptake as compared to the pristine membrane. The addition of cSMM with an additional –SO3– charge has enabled the water uptake to be increased which is crucial to enhance the proton conductivity that of SPEEK. Moreover, the extra –SO3– can facilitate the transfer of proton better. This study found that cSMM is a good candidate as an additive for SPEEK in improving its function as polymer electrolyte membrane.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".