Proton Exchange Membranes for Application in Fuel Cells: Grafted Silica/SPEEK Nanocomposite Elaboration and Characterization
Bibliographic record
Abstract
Hydrogen technologies and especially fuel cells are key components in the battle to find alternate sources of energy to the highly polluting and economically constraining fossil fuels in an aim to preserve the environment. The present paper shows the synthesis of surface functionalized silica nanoparticles, which are used to prepare grafted silica/SPEEK nanocomposite membranes. The nanoparticles are grafted either with hexadecylsilyl or aminopropyldimethylsilyl moieties or both. The synthesized particles are analyzed using XRD, NMR, TEM, and DLS to collect information on the nature of the particles and the functional groups, on the particle sizes, and on the hydrophilic/hydrophobic character. The composite membranes prepared using the synthesized particles and two SPEEK polymers with sulfonation degrees of 69.4% and 85.0% are characterized for their proton conductivity and water uptake properties. The corresponding curves are very similar for the composites prepared with both polymers and the nanoparticles bearing the two functional groups. The composites prepared with the nanoparticles bearing solely the aminopropyldimethylsilyl moiety exhibit lower conductivity and water uptake, possibly due to higher interaction of the polymer sulfonic acid sites with the amine groups. The composites prepared with the nanoparticles bearing solely the hexadecylsilyl moiety were not further investigated because of very high particles segregation. A study of the proton conductivity as a function of temperature was performed on selected membranes and showed that nanocomposites made with nanoparticles bearing both functional moieties have a higher conductivity at higher temperatures.
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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".