Thermal Degradation of Polypropylene Pine Sawdust Composite Filaments through Successive Heating and Reprocessing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it