Enhanced Swelling and Barrier Properties Stability of SPEEKbased Polymer Electrolytes Nanocomposite Membran
Bibliographic record
Abstract
The objective of this study is to prepare SPEEK/Cloisite15A®/TAP nanocomposite membranes and to characterize their physicochemical properties as polymeric electrolyte nanocomposites. SPEEK membranes with various degrees of sulfonation (DS) were first prepared. Subsequently, 1 wt. % of Cloisite15A® and 2,4, 6–triaminopyrimidine (TAP), respectively, were introduced into the SPEEK matrices via solution intercalation method. The Cloisite15A® and TAP additives were used as reinforcing material for the SPEEK/Cloisite15A®/TAP (SP/CL/TAP) nanocomposite membranes in terms of the barrier properties, swelling and morphological structure. The effect of DS on the SP/CL/ TAP nanocomposite membranes was studied in terms of their swelling behavior, proton conductivity and methanol permeability. Field emission scanning electron micrographs (FESEM) and X–Ray diffraction (XRD) patterns confirmed that the Cloisite15A® particles were completely distributed to allow the nanosize dispersion in the polymer matrix. The swelling behavior of SPEEK membrane at DS of 88% was improved in the presence of Cloisite15A® and TAP. All SPEEK nanocomposite membranes studied exhibited improved methanol barrier properties compared with the parent SPEEK membranes. Owing to significant conductivity, remarkable barrier properties, high swelling stability and outperformed DMFC performance, SP63/CL/TAP nanocomposite membranes can be considered as a polymer electrolyte membrane for DMFC applications.
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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 | 0.001 |
| 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".