Effect of Spent Coffee Grounds on the Crystallinity and Viscoelastic Behavior of Polylactic Acid Composites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it