3D-Printed Thermoset Biocomposites Based on Forest Residues by Delayed Extrusion of Cold Masterbatch (DECMA)
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide We developed a 3D-printing process based on thermoset biocomposites termed Delayed Extrusion of Cold Masterbatch (DECMA). DECMA is a processing method, based on controlling the degree of curing, that takes some responsibility of the 3D printing from materials and as such can be used to 3D print otherwise unprintable materials. First, a masterbatch was produced by mixing a bio-based resin (bioepoxy) and sawdust and lignin. This paste was partially cured at room temperature until reaching an apparent viscosity suitable for extrusion (≈10 5 mPa·s at 1 s –1 ). The system was next cooled (5–10 °C) to delay subsequent hardening prior to 3D printing. The printability of the biocomposite paste was systematically investigated and the merits of the delayed extrusion, via DECMA, were assessed. It was found that DECMA allowed the revalorization of sawdust and lignin via 3D printing, as direct printing led to failed prints. Our approach afforded cost-effective, shear-thinning dopes with a high bio-based content (58–71%). The bio-based 3D-printed materials demonstrated good machinability by computer numerical control (CNC). Overall, the benefits of the introduced DECMA method are shown for processing bio-based materials and for on-demand solidification during additive manufacturing.
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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) | 0.004 | 0.001 |
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".