Effect of dispersion state of Cloisite15A® on the performance of SPEEK/Cloisite15A nanocomposite membrane for DMFC application
Bibliographic record
Abstract
Abstract The introduction of 2,4,6‐triaminopyrimidine (TAP) into sulfonated poly(ether ether ketone) (SPEEK)/Cloisite15A® nanocomposite membranes were investigated for the purpose of maintaining low methanol permeability and suppressing swelling in direct methanol fuel cell (DMFC). SPEEK with 63% of degree of sulfonation (DS) was prepared by sulfonation of PEEK. Cloisite15A (7.5 wt %) along with various weight loading of TAP was incorporated into SPEEK matrix via solution intercalation method. The effect of TAP loading on the SPEEK/Cloisite15A/TAP morphology was studied. The beneficial impact of the SPEEK/Cloisite15A/TAP morphology on the physicochemical properties of the membrane was further discussed. Swelling behavior, ion exchange capacity (IEC), proton conductivity, and methanol permeability of the resultant membranes were determined as a function of Cloisite15A and TAP loadings. Uniform distribution of Cloisite15A particles in the SPEEK polymer matrix in the homogenous SPEEK/Cloisite15A/TAP nanocomposite membranes was confirmed by scanning electron microscopy and X‐ray diffraction. The water uptake of the SPEEK nanocomposite membranes decreased dramatically in the presence of TAP. The significant selectivity of SP/7.5/7.5 nanocomposite membranes could indicate a potential feasibility as a promising electrolyte for DMFC. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012
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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".