Stability and Efficiency Improvement of Sulfonated Poly(para-phenylene): Study of Random Co-Polymer for Proton Exchange Membrane for Fuel Cell
Bibliographic record
Abstract
Sterically-encumbered, sulfonated poly(phenylene)s are an interesting group of hydrocarbon materials for use in low Pt-content catalyst layers because they potentially offer high conductivity, high thermo-oxidative stability, and appear less-prone to strong adsorption on Pt – a process that typically reduces the electroactive surface area. Frequently, polymers like poly(phenylene)s are randomly post-functionalized in order to introduce proton-conducting properties.(1-3) However, as randomly-functionalized materials are not ideal as proton conductors, a new synthetic route has been developed in order to obtain well-defined pre-functionalized monomers for the synthesis of sulfonated poly(phenylene)s. Using a variety of NMR pulse sequences, the isomers resulting from different couplings (e.g., meta-meta) of arylene moieties has been studied through an examination of several small molecule, model compounds. Furthermore, a series of random copolymers has been made in which the ionic content was varied by alteration of the monomer feed ratios. Finally, the properties (e.g., proton conductivity, chemical stability, fuel cell performance as membrane and catalyst ionomer) have been extensively studied.(4) (1) Maier, G.; Meier-Haack, J. Advances in Polymer Science 2008, 216, 1 (2) Otsuki, T.; Kanaoka, N.; Iguchi, M.; Mitsuta, N.; Soma, H.; (Honda Motor Co., Ltd., Japan; JSR Corporation). Application: US, 2004, 13 (3) Fujimoto, C. H.; Hickner, M. A.; Cornelius, C. J.; Loy, D. A. Macromolecules 2005, 38, 5010 (4) T. J. G. Skalski, B. Britton, T. J. Peckham and S. Holdcroft, Journal of the American Chemical Society, 2015, 137, 12223 Figure 1
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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".