Surface characterization of biodegradable nanocomposites by dynamic speckle analysis
Bibliographic record
Abstract
Starch/kefiran/ZnO nanocomposite films may exhibit different physicochemical properties depending on the distribution of ZnO nanoparticles. As a result of UV exposure, the hydrophobicity of the nanoparticles may be modified, resulting in their dispersion in the polymer matrix. The aim of this paper is to characterize starch/kefiran/ZnO nanocomposite films using dynamic speckle analysis. In this experiment, speckle patterns of the nanocomposite are acquired in situ under controlled moisture, pressure, and temperature conditions. This is followed by a statistical postprocessing procedure to determine the deformation pattern of the nanocomposite. A numerical analysis of the successive speckle patterns is used to determine the time evolution of sample deformation. There is a correlation between the intensity and contrast of speckle patterns and the temporal alteration of the polymer. Several factors have been considered to examine the structural evolution of the nanocomposite, including time history speckle pattern, co-occurrence, graphical speckle contrast, roughness parameter, auto-correlation, and Shannon entropy. The variation and overall viscoelastic properties of the nanocomposites are expressed via several statistical parameters. The changes in the computed parameters are attributed to the time-varying activity of the samples during their higher hydrophilicity.
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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".