Effect of Spent Coffee Grounds on the Crystallinity and Viscoelastic Behavior of Polylactic Acid Composites
Bibliographic record
Abstract
This work investigated the addition of spent coffee grounds (SCG) as a valuable resource to produce biocomposites based on polylactic acid (PLA). PLA has a positive biodegradation effect but generates poor proprieties, depending on its molecular structure. The PLA and SCG (0, 10, 20 and 30 wt.%) were mixed via twin-screw extrusion and molded by compression to determine the effect of composition on several properties, including mechanical (impact strength), physical (density and porosity), thermal (crystallinity and transition temperature) and rheological (melt and solid state). The PLA crystallinity was found to increase after processing and filler addition (34–70% in the 1st heating) due to a heterogeneous nucleation effect, leading to composites with lower glass transition temperature (1–3 °C) and higher stiffness (~15%). Moreover, the composites had lower density (1.29, 1.24 and 1.16 g/cm3) and toughness (30.2, 26.8 and 19.2 J/m) as the filler content increased, which is associated with the presence of rigid particles and residual extractives from SCG. In the melt state, polymeric chain mobility was enhanced, and composites with a higher filler content became less viscous. Overall, the composite with 20 wt.% SCG provided the most balanced properties being similar to or better than neat PLA but at a lower cost. This composite could be applied not only to replace conventional PLA products, such as packaging and 3D printing, but also to other applications requiring lower density and higher stiffness.
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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".