<i>In Situ</i> Fabrication of Nafion–Titanate Hybrid Electrolytes for High-Temperature Direct Ethanol Fuel Cell
Bibliographic record
Abstract
The synthesis and characterization of a novel Nafion-matrix nanocomposite by the in situ conversion of titania particles into titanate nanofilaments such as nanotubes and nanorods are reported. Titania nanoparticles grown inside Nafion hydrophilic domains were converted by a microwave-assisted hydrothermal reaction into the proton conducting titanate nanotubes and nanorods. Detailed characterization by Raman spectroscopy, X-ray diffraction, small-angle X-ray scattering, and transmission electron microscopy evidenced an intimate interaction between titanate nanostructures and the ionomer hydrophilic phase. The favored localization of such high aspect ratio nanofilaments in the ionic phase of Nafion has a marked impact on the properties of the composites. The experimental data showed enhanced mechanical stability at high temperature (∼130 °C) that was correlated to a strong temperature dependence of the proton conductivity in the same temperature range. Such properties contributed to a significant increase of the performance of direct ethanol fuel cells operating at high temperature (∼130 °C) using hybrid Nafion–titanate electrolytes compared to commercial Nafion.
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".