Thermal Degradation of Polypropylene Pine Sawdust Composite Filaments through Successive Heating and Reprocessing
Bibliographic record
Abstract
Considering the use of the new molding thermoplastic technique, where viscous filaments can be artistically or technically manipulated to create three-dimensional pieces using an extruder, this paper discusses the optimal PP/wood fiber filament preparation conditions especially the thermal degradation. Not only is it essential to know the best processing conditions of the composites but also gain durability and/or advantageous color change when the final products made with viscous filaments are subjected to thermal treatments. Very few papers have been published on polypropylene-pine wood filament composites and the thermal degradation of such filaments. This paper presents the preparation and characterization of filament composites using 5, 10, and 20wt.% pine sawdust with a compatibilizer obtained by hot molding through the use of an extruder, and discusses the effect of both drying time and temperature on the prepared filament composites to understand thermal degradation when subjected to 60°C and/or 120°C. Prepared filament composites are characterized for physical (density, water absorption, and crystallinity), thermal and tensile properties besides their morphology along with fractography. X-ray diffraction results confirmed the data obtained in thermal studies indicating that increased fiber content decreased both the crystallinity and the thermal resistance while decreasing the melting temperature of the filament composites. Fractographic studies revealed low adhesion between the sawdust and the matrix, evidenced by the presence of loose and some unattached sawdust particles in some composites, thus, supporting the observed low strength in these composites, besides the influence of drying time and temperature on the mechanical properties of the composites.
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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".