Valorization of Spent Coffee Grounds as Precursors for Biopolymers and Composite Production
Bibliographic record
Abstract
Spent coffee grounds (SCG) are a current subject in many works since coffee is the second most consumed beverage worldwide; however, coffee generates a high amount of waste (SCG) and can cause environmental problems if not discarded properly. Therefore, several studies on SCG valorization have been published, highlighting its waste as a valuable resource for different applications, such as biofuel, energy, biopolymer precursors, and composite production. This review provides an overview of the works using SCG as biopolymer precursors and for polymer composite production. SCG are rich in carbohydrates, lipids, proteins, and minerals. In particular, carbohydrates (polysaccharides) can be extracted and fermented to synthesize lactic acid, succinic acid, or polyhydroxyalkanoate (PHA). On the other hand, it is possible to extract the coffee oil and to synthesize PHA from lipids. Moreover, SCG have been successfully used as a filler for composite production using different polymer matrices. The results show the reasonable mechanical, thermal, and rheological properties of SCG to support their applications, from food packaging to the automotive industry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".