Selective Laser Sintering of Lignin-Based Composites
Bibliographic record
Abstract
Lignin is introduced as a suitable component for selective laser sintering (SLS) of polyamide (PA12) to reduce costs while maintaining or improving processability and performance. Alkali lignin (sourced as a polydisperse, amorphous powder) was used at a volume concentration of up to 60 vol % for three-dimensional (3D) printing of complex, layered structures. The latter were obtained as high axial aspect objects, produced in flat, flipped (90°), and vertical directions, which were further examined to elucidate the effect of lignin as a suitable component in SLS. The composite withstood heating during SLS, and sintered PA/lignin showed 30% less degradation at elevated temperatures compared to pure PA. The morphological, wetting, mechanical, and thermal characteristics associated with the 3D-printed structures were compared. For instance, the strength and wettability were highly dependent on processing orientation. Compared to objects produced from neat PA, those that included lignin presented a higher porosity (∼10%) with a simultaneous increase in stiffness (increased Young modulus, by ∼16%, and reduced tensile strength, by ∼7%). Owing to differences in surface roughness and composition, an important difference in the water contact angle (CA) of the samples printed in the flipped and flat orientations was observed (55 and 126°, respectively). Overall, SLS is shown as a developmental step toward lignin valorization in composites while allowing reduced cost, scalability, and facile processing.
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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".