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 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 | 0.001 | 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) | 0.002 | 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".