Using silica nanoparticles for modifying sulfonated poly(phthalazinone ether ketone) membrane for direct methanol fuel cell : A significant improvement on cell performance
Bibliographic record
Abstract
Sulfonated poly(phthalazinone ether ketone) (sPPEK) with a degree of sulfonation of 1.23 was mixed with silica nanoparticles to form hybrid materials for using as proton exchange membranes. The nanoparticles were found homogeneously dispersed in the polymer matrix and a high 30 phr (parts per hundred resin) loading of silica nanoparticles can be achieved. The hybrid membranes exhibited improved swelling behavior, thermal stability, and mechanical properties. The methanol crossover behavior of the membrane was also depressed such that these membranes are suitable for a high methanol concentration in feed (3 M) in cell test. The membrane with 5 phr silica nanoparticles showed an open cell potential of 0.6V and an optimum power density of 52.9mWcm−2 at a current density of 264.6mAcm−2, which is better than the performance of the pristine sPPEK membrane and Nafion® 117.
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 | 0.000 |
| 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".