The Use of Low-pressure Plasma on Enhancing the Attachment of Al<sub>2</sub>O<sub>3</sub> Nanoparticles to Wood–Plastic Composites
Bibliographic record
Abstract
Wood–plastic composites (WPC) are widely used for many purposes due to their interesting properties, but they have poor surface adhesion due to the chemical inertness of the matrix. Thus, in this work, the effect of plasma treatment on the WPC was investigated regarding its influence in the aluminum oxide (Al2O3) nanoparticles attachment to the surface. WPC were prepared in a polypropylene (PP) matrix, plasma-treated at 100 W for 600 s, and then covered with Al2O3 nanoparticles dispersion. The WPC/Al2O3 surfaces have been investigated by means of morphology, surface roughness, chemical structure, wettability, and nanohardness. Plasma treatment improved the attachment of Al2O3 onto WPC, which was confirmed by the higher presence of aluminum and oxygen-containing functional groups and the reduction of the intensity of peaks of methylene (CH2) and methyl (CH3) groups on the WPC surface. The higher surface reactivity of plasma-treated WPC resulted in a better distribution of the nanoparticles over the entire surface. In addition, plasma treatment avoided the formation of coffee-ring phenomenon but it was able to create cone-like structures on the WPC, increasing the surface roughness due to the etching effect and the attachment of Al2O3 nanoparticles. Plasma treatment followed by Al2O3 nanoparticles attachment increased the wettability, hardness, and elastic modulus of WPC at nanometric scale.
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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".