Influence of Inclusion Sb2O3/NiO Nanostructures on the Morphological, Microstructural, and Optical Characteristics of PVA Polymeric for Gamma-Ray Shielding Applications
Bibliographic record
Abstract
This work describes the steps to make PVA composites with varying amounts of Sb2O3 and NiO NPs using the solution casting process.The amounts used are 2.3, 4.6 and 6.9 wt.%.Field Emission-Scanning Electron Microscopy (FE-SEM) showed that the Sb2O3 NPs, and NiO NPs were evenly distributed across the PVA polymer matrix.Fourier transform-infrared (FT-IR) study revealed that the Sb2O3 and NiO NPs embedded in the polymer matrix interacted with one another.FT-IR research shows that the PVA matrixpolymer and Sb2O3/NiO NPs physically interact with one another.As the ratio of Sb2O3 and NiO NPs in the PVA increased, the absorption coefficient, and refractive index, also increased.Moreover, a significant reduction of 25.83% in the allowed optical band gap was observed, suggesting improved electronic transition behavior.An indirect electron transition has occurred since the absorbance coefficient is less than 10 4 cm -1 .Ultimately, the PVA/Sb2O3/NiO nanocomposites exhibit a high radiation shielding efficiency (RSE) of 18.84% for gamma rays, indicating its promising potential as a protective material.The Sb2O3/NiO combination provides an optimal compromise between environmental safety, mechanical flexibility, and radiation attenuation, rendering it a suitable option for non-toxic gamma-ray shielding.Enhancing nanoparticle loading and composite thickness may improve RSE, potentially exceeding that of traditional fillers.
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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".