3D printing of biodegradable biocomposites based on forest industrial residues by fused deposition modeling
Bibliographic record
Abstract
This study investigated the impact of industrial forest residues (IFR) type and proportion on 3D-printed biocomposites (BC). Fused deposition modeling (FDM) produced BC containing up to 20 % Jack pine sawdust, wood ashes, cellulose fibers , and polylactic acid (PLA). The BC thermal stability, surface chemistry, microstructure, and physical and mechanical were investigated. Thermal stability investigations revealed that adding IRF resulted in a decline in the PLA degradation temperature, an increase in the residual mass, a decrease in the glass transition temperature , and an improvement in crystallinity. Add IRF to PLA showed an important reduction in ductility, a consistent reduction of the tensile and flexural strengths with increasing proportion but only a slight or a non-significant reduction the modulus of elasticity . Adding forest waste filler to pure PLA increased the water absorption (WA) and dimensional accuracy (TA) of the biocomposites , which was expected due to the hydrophilic character of fillers. The microstructural analysis demonstrated that a higher filler level resulted in a greater porosity, roughness, and visible filler pull-out on the filament and printed parts surfaces. Surface chemistry analysis suggested poor interactions between the PLA and the fillers and consequently poor interfacial adhesion. Furthermore, the rheological investigations confirmed that the complex viscosity and storage modulus of PLA-Jack pine's sawdust and PLA-cellulose fibers increased with filler. In contrast, the PLA-wood ashes showed opposite results. Using 3D-printed biodegradable biocomposites provided a sustainable option for reducing dependence on non-renewable plastic, valorizing the value chain of forest products, and promoting the circular economy.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".