Enhancement of Optical Properties in In2O3-Doped PVA/PEG Nanostructured Films for Optoelectronic Applications
Bibliographic record
Abstract
In this investigation, nanostructured films comprising polyvinyl alcohol (PVA)/polyethylene glycol (PEG) blended with indium oxide (In2O3) nanoparticles were fabricated using a casting technique.The study focused on examining the impact of varying the ratios of PVA/PEG blend and In2O3 nanoparticle content on the optical properties of these nanostructures.Optical assessments were conducted across a spectrum ranging from 220 nm to 820 nm.It was observed that an increase in In2O3 nanoparticle concentration resulted in elevated absorbance levels in the PVA/PEG matrix, particularly within the ultraviolet spectrum, while simultaneously causing a decrease in transmittance.This effect was attributed to the interaction between the polymer matrix and the In2O3 nanoparticles, leading to altered electronic and photonic interactions within the material.A notable reduction in the energy band gap was also recorded with increasing In2O3 content, suggesting enhanced electron mobility and photon interaction within the nanostructures.These findings underscore the potential of In2O3-doped PVA/PEG films in optoelectronic applications, particularly in fields requiring controlled optical properties such as photonics and advanced optical systems.The improved optical parameters, specifically in terms of absorbance and band gap manipulation, highlight the versatility of these nanostructures in various optoelectronic applications.
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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) | 0.000 | 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".