Incorporating sepiolite and kaolinite to improve the performance of SPEEK composite membranes for proton exchange membrane fuel cells
Bibliographic record
Abstract
Abstract SPEEK polymer based thermally crosslinked polymer membranes are prepared by sol‐gel synthesis using kaolinite and sepiolite clays as additives. Characterization tests, ie, mechanical stability, thermal gravimetric analysis, ion exchange capability, swelling properties, water uptake capacities, electrochemical impedance spectroscopy analysis, and Fourier transform infrared spectroscopy (FTIR) analysis of the membranes were conducted. The sepiolite and kaolinite addition enhanced the thermal stability and the thermal crosslinking reduced the swelling capacity of the synthesized membranes. Proton conductivity results were increased from 0.172 to 0.268 S cm−1 by adding 9% of kaolinite, and to 0.329 S cm−1 at 80°C by adding 9% of sepiolite to the SPEEK membrane's polymer structure. The fuel cell current density and potential measurements of 141 mA cm−2 and 84.6 mW cm−2 were found respectively at 0.6 V for the SPEEK/S9 membrane, whereas values of 600 mA cm−2 and 348 mW cm−2 were found for the Nafion commercial membrane.
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".