Decorating wood flour/HDPE composites with wood veneers
Bibliographic record
Abstract
The low surface free energy makes wood plastic composites difficult to bond with traditional adhesives, such as urea formaldehyde resin (UF). This study investigates the decoration of wood flour/high‐density‐polyethylene composite (WF/HDPE) board with poplar wood veneers. High‐density polyethylene (HDPE) or maleic anhydride grafted polyethylene (MAPE) film was introduced between the veneer and the WF/HDPE composite board as an intermediate component to strengthen the bond. Surface bonding strength, hot water resistance, and bending performance of the composites were then evaluated. Contact angle, Attenuated total reflection‐Fourier transform infrared (ATR‐FTIR) spectroscopy, and scanning electron microscopy (SEM) were used to analyze the interface of the wood veneer and the WF/HDPE composites. The addition of MAPE film enhanced the polar characteristic of the WF/HDPE composite surface and increased the number of functional groups reacting with the –OH groups present in the wood veneer. A MAPE intermediate film increased the surface bonding strength of the wood veneer to WF/HDPE composite surface, and also endured boiling water without delimitation failure. After being covered with the wood veneer, the flexural strength and modulus of WF/HDPE composite panels increased four times comparing to that of the uncovered composites. POLYM. COMPOS., 39:1144–1151, 2018. © 2016 Society of Plastics Engineers
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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".