Thermal Properties of Composites Made of Heat-treated Wood and Polypropylene
Bibliographic record
Abstract
Exposure of wood to high temperatures improves thermal stability of wood and it has been used as a means to boost dimensional stability of wood for centuries. Inclusion of wood in thermoplastic matrix composites reduces the thermal stability of the composites considerably because of poor adhesion between wood and the matrix, and lower thermal stability of wood in comparison to the matrix. In order to invest natural fiber/thermoplastic composites with thermal stability, wood flour was heat treated under different temperatures and time. Thermal stability measurements conducted by thermogravimetric analysis (TGA) indicated that heat-treatment of wood increased thermal stability, ash content, and DTG max degradation temperature of wood. As the heat-treated wood flour used as filler for PP composites, thermal stability, ash content, and DTG max degradation temperatures of the composites were affected markedly as well. The higher the temperature and longer the time employed in heat-treatment, the more was the improvement gained. The melting point (T m ) of composites measured by differential scanning calorimetry (DSC) showed that wood content and preheat treatment had no effect on melting points (T m ).
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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".