Silicalite-1/SPEEK composite membranes: influence of the zeolite particles loading or size on proton conductivity and water uptake
Bibliographic record
Abstract
With the aim of finding alternative sources of energy to the highly polluting and economically constraining fossil fuels, while preserving the environment, governments and corporations are investing in hydrogen technologies and especially in fuel cells. The present paper shows the synthesis of silicalite-1 particles, the siliceous parent of the ZSM-5 zeolite, used to prepare composite membranes to be used as electrolytes in PEM fuel cells. The synthesized particles are analyzed using XRD, NMR, N2 adsorption, DLS and TEM to collect information on the nature, morphology and size of the particles. Almost all samples are well crystallized silicalite-1 particles. Their size is growing regularly with the increase of the H2O/SiO2 ratio during crystallization and consequently particles growth dominates particles nucleation. The composite membranes prepared using two SPEEK polymers with sulfonation degrees of 69.4 and 85.0% are characterized for their proton conductivity and water uptake properties. The curves showing the evolution of these properties as a function of particles loading are very similar for the composites prepared with both polymers. This resemblance of conductivity and water uptake behaviors is probably related to similar variations of the aqueous phase structure present in the entangled polymer matrix. When varying the particles size, both conductivity and water uptake properties are evolving similarly for both DS values. This similar behavior is either correlated to the restructuration of the aqueous phase entangled in the polymer matrix and/or to the increase of the specific surface area of the zeolite particles with their increasing size, leading to an increase in the water content and conductivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".