Effects of Chromic Treatment on the Surface Properties of Polypropylene (PP) Wood Composites
Bibliographic record
Abstract
The moisture sensitivity of wood–polymer composites (WPCs) is mainly related to their hydrophilic wood components. Coatings are among the alternatives that improve the dimensional stability of these composites. However, the adhesion of most coatings to the WPC surface is generally poor. Thus, chemical and/or mechanical treatments should be applied to the WPC surface to improve the coating adhesion. Therefore, the main objective of this study was to improve the adhesion coating of polypropylene (PP) WPCs through a chromic treatment. PP was reinforced by three different pulp fibers (kraft, thermomechanical (TMP), and chemothermomechanical (CTMP)) at three fiber contents (50, 60, and 70% w/w). A chromic treatment was applied to the PP-based WPCs to activate the surface of the composites and alter their roughness parameters, creating a higher interfacial zone that improved the bonding of the epoxy coating to the surface of the PP composites. The chromic treatment increased the roughness of the surface. An increase in profile and surface parameters was observed after treatment. This treatment modified the chemical composition of the surface by creating polar carbon–oxygen groups and increasing the carbonyl and hydroxyl indexes.
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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 | 0.001 |
| 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".