Polybenzimidazole/BaCe<sub>0.85</sub>Y<sub>0.15</sub>O<sub>3-δ</sub> nanocomposites with enhanced proton conductivity for high-temperature PEMFC application
Bibliographic record
Abstract
The present work reports the synthesis of polybenzimidazole (PBI)/BaCe0.85Y0.15O3-δ nanocomposite membrane. The obtained membranes were investigated to use as novel electrolytes in high-temperature proton exchange fuel cells. The PBCYx membranes were prepared with dispersing BaCe0.85Y0.15O3-δ into the polyimidazole membrane by solution casting method. The obtained membranes were used as novel proton conductors. The thermal stability and structural properties were investigated. The conductivity and morphology of the obtained materials were studied using impedance spectroscopy AC (IS) and a scanning electron microscope (SEM) equipped with energy dispersive X-ray spectroscopy (EDX). The maximum phosphoric acid adsorption (175%) and protonic conductivity (0.092 S/cm at 180 °C under dry conditions) were observed for all of the PBI nanocomposite membranes containing 5 wt.% of BaCe0.85Y0.15O3-δ in the membrane matrix. The polarization and power density curves were studied at 150 and 180 °C operating temperatures. The power density of about 0.42 W/cm2 and current density of about 0.84 A/cm at 0.5 V and 180 °C were achieved under dry conditions. The data obtained from our studies showed that the physicochemical properties of the novel nanocomposites were enhanced for using in the high-temperature proton transfer fuel cells.
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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".