Optimization of reaction parameters affecting crystal phase growth and purity of BaCeO<sub>3</sub> and BaCe<sub>1-</sub><sub><i>x</i></sub>Yb<sub><i>x</i></sub>O<sub>3-</sub><sub>δ</sub> nanopowders and investigating high protonic conductivity of sulfonated poly(ether ether ketone) – BaCe<sub>0.85</sub>Yb<sub>0.15</sub>O<sub>3-</sub><sub>δ</sub> composite membrane
Bibliographic record
Abstract
Pure and ytterbium-doped BaCeO3 nanostructures were synthesized by solid-state reaction with the mixtures of Ba(NO3)2, BaCO3, (NH4)2Ce(NO3)6, and Yb2O3 at 800 °C for 10 and 24 h. Doping of ytterbium ions in the BaCeO3 host matrix has been studied and confirmed using powder X-ray diffraction. The results from the Rietveld analysis indicated that the sample has a main BaCeO3 structure with the space group of [Formula: see text]. Through intensive experiments and analysis, optimum reaction conditions for the synthesis of doped nanoparticles including the crystal phase impurity and reaction time are proposed. The results of the study showed that for the reaction time of 24 h, BaCO3 reacted more effectively with (NH4)2Ce(NO3)6 than Ba(NO3)2 did. On the other hand, the purity values of 97% and 95% were obtained for pure and Yb3+ doped BaCeO3 samples, respectively. Field emission scanning electron microscope images revealed that the synthesized BaCeO3 nanomaterials have mono-shaped sphere morphology. Furthermore, ytterbium-doped nanoparticles were incorporated into the matrix of sulfonated poly(ether ether ketone) (SPEEK) membranes (SPYb) with the aim of enhancing proton conductivity. The prepared SPYb nanocomposite membrane containing 1.7 wt.% of BaCe0.85Yb0.15O3-δ nanoparticles exhibited a high proton conductivity (67 mS/cm) at 80 °C.
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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".