Does power ultrasound affect Nafion® dispersions?
Bibliographic record
Abstract
The effect of low frequency power ultrasound on Nafion® ionomer used for fabricating proton exchange membrane fuel cell (PEMFC) and water electrolyzer (PEMWE) catalyst inks was investigated. In this study, a series of Nafion® dispersions having three concentrations (10, 5, and 2.5% w/v) were studied under various irradiation durations (tus), at fixed ultrasonic frequency (f = 42 kHz) and ultrasonic power (P > 2 W), under either controlled or unregulated bulk solution temperature conditions using a laboratory ultrasonic cleaning bath. Viscosity (η), thermal degradation, and glass transition temperature (Tg) for all Nafion® dispersion samples was measured and compared to untreated Nafion® samples. In our conditions, it was found that power ultrasound lowered the viscosity of all tested Nafion® dispersion samples; whilst thermogravimetric and differential scanning calorimetry analyses showed that for all ultrasonically irradiated samples, a negligible overall polymer degradation and no obvious change in Tg was observed under controlled and unregulated bulk temperature conditions. It was found that it is possible that acoustic cavitation causes depolymerisation followed by a polymerisation initiation step during ultrasonication. By comparing the ultrasonically treated and high-shear mixed samples, it was also observed that acoustic and hydrodynamic cavitation played an important role in the reduction of dispersion viscosity.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".