Does power ultrasound affect hydrocarbon Ionomers?
Bibliographic record
Abstract
The effect of low-frequency high-power ultrasound on hydrocarbon-based ionomers, cation exchange sulfonated phenylated polyphenylene (sPPB-H+) and anion exchange hexamethyl-p-terphenyl poly(benzimidazolium) (HMT-PMBI), was studied. Ionomer solutions were subjected to ultrasonication at fixed ultrasonic frequencies (f = 26 and 42 kHz) and acoustic power (Pacous = 2.1 – 10.6 W) in a laboratory-grade ultrasonication bath, and a probe ultrasonicator; both commonly employed in catalyst ink preparation in research laboratory scale. Power ultrasound reduced the polymer solution viscosity of both hydrocarbon-based ionomers. The molecular weight of sPPB-H+ decreased with irradiation time. Changes in viscosity and molecular weight were exacerbated when ultrasonicated in an ice bath; but reduced when the solutions contained carbon black, as typically used in Pt/C-based catalyst inks. Spectroscopic analyses revealed no measurable changes in polymer structure upon ultrasonication, except for very high doses, where evidence for free-radical induced degradation was observed. Ionomers subjected to ultrasound were used to prepare catalyst layers and membrane electrode assemblies (MEA)s. Despite the changes in the ionomer described above, no significant differences in electrochemical performance were found between MEAs prepared with ionomers pre-subjected to ultrasound and those that were not, suggesting that fuel cell performance is tolerant to ionomers subjected to ultrasound.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".