Effects of Wood Preheat Treatment on Thermal Stability of HDPE Composites
Bibliographic record
Abstract
In this study, wood treated at different temperatures (175, 190, and 205°C) was used to ease the thermal instability caused by the introduction of wood to the composites. Composites consisting of different amounts of wood and coupling agent and HDPE were produced by the injection molding method. The mass loss measured by thermogravimetric analysis (TGA) was used as a tool to evaluate thermal stability of the materials. The results of this study show that heat treatment of wood increased DTG max degradation temperatures and ash contents of wood especially when treatments took place at 190 and 205°C. An increase in wood content from 25 to 50% made the composites more sensitive to high temperatures. In comparison with untreated wood/HDPE composites, adding heat treated wood to the composites increased the thermal stability and ash contents of the composites. Coupling agent enhanced thermal stability of the composites when untreated wood was used as filler. The role of coupling agent on thermal stability was imperceptible when heat treated wood was used as filler.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".