One-Step Twin-Screw Extrusion Process to Fibrillate Deep Eutectic Solvent-Treated Wood to Be Used in Wood Fiber-Polypropylene Composites
Bibliographic record
Abstract
Abstract Bio-based wood materials are preferable for composites because of their sustainability, but adequately dispersing wood fibers in polymers can be difficult and costly. Our approach was to pretreat the wood with a green solvent system, allowing the composite to be extruded in a single step, simplifying the process, and reducing the overall cost. This study investigates the fibrillation of untreated wood sawdust (W) and deep eutectic solvent-treated wood sawdust (DESW) using a one-step twin-screw extrusion (TSE) process. The results of the analysis of wood fractions and optical microscopy confirmed that the one-step extrusion process resulted in fibrillation of both treated and untreated wood material. The width of the original wood particles was reduced by more than 99% after a one-step TSE for both untreated and DES-treated wood. The size reduction of the DESW was slightly greater than that of the untreated wood, and fibrillation was further confirmed by rheological analysis. The fibrillated wood was then compounded with polypropylene (PP) to produce a wood fiber-polypropylene composite with 50 wt % wood content. The elastic modulus of both untreated and treated extruded composites was higher than that of neat PP. The tensile strength and strain at break for the DESW-PP composite slightly increased in comparison to the untreated W-PP composite. Furthermore, DES treatment of wood resulted in a darker color and increased hydrophobicity of the material.
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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 | 0.001 |
| 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".