Effect of Decoration AgNO₃ - BaTiO₃ Nanoparticles on the Structure and Optical Properties of PVA/PEO Polymer Blend
Bibliographic record
Abstract
This study investigates the effect of nanoparticles, namely silver nitrate (AgNO3) and barium titanate (BaTiO3), on polyvinyl alcohol (PVA)/ polyethylene oxide (PEO) blends prepared by casting at different weight ratios.Improving the structural and optical properties of polymer blends by incorporating nanoparticles is an important strategy for obtaining materials with advanced functions.X-ray diffraction (XRD) results confirm the semicrystalline nature of the pure polymer blend, the best peak was obtained at 2θ = 19.58°, with high-intensity other peaks at 2θ = 22.1° and 23.69° , which increased in crystallinity after the addition of nanoparticles due to the formation of distinct new crystalline phases at 2θ =19.61° , 22.4° , 23.59° , 19.4° , 23.46° , 23.51° , 31.92° , 39.25° , 47.74° , 50.94° , 56.48° , 65.71° , 54.61° , 31.65° , 39.03° , 45.15° , 56.29° , and 65.96° .Fourier transform infrared (FTIR) spectroscopy revealed distinct peaks at: 2884 cm -1 (C-H and C-O vibration in PEO/PVA), a broad band at 3300 to 3500 cm -¹ (O-H groups, more in PVA), 1340.73 cm -¹ (N-O vibration of AgNO3), and 606.30cm -¹ (Ti-O bond of BaTiO3).UV measurement results show that Pure PVA/PEO polymers blend absorb in the UV region 190 to 250 nm, and the addition of nanoparticles increases the absorption intensity between 300 and 600 nm.
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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".