Upcycling Spent Coffee Grounds-Based Composite for 3D Printing: A Review of Current Research
Bibliographic record
Abstract
Driven by the growing demand for sustainable materials, spent coffee grounds have emerged as a promising bio-based reinforcement in polymer composites, particularly for additive manufacturing applications. As a readily available byproduct of the coffee industry, spent coffee grounds contain cellulose, hemicellulose, lignin, proteins, and oils, making them attractive fillers for both thermoplastic and thermoset matrices. Incorporating spent coffee grounds into composites supports waste valorization, cost reduction, and environmental sustainability by transforming organic waste into functional materials. This review first examines the issue of spent coffee ground waste, addressing its environmental footprint and disposal challenges. It then explores the composition and properties of spent coffee grounds. The paper provides a comprehensive overview of composites based on spent coffee grounds for 3D printing, covering processing methods, potential applications, and current challenges in additive manufacturing. Special attention is given to the preparation and processing of these composites, including key steps such as drying, grinding, sieving, and surface modification to enhance compatibility with polymer matrices. Various additive manufacturing techniques influence the printability, processability, and mechanical performance of such composites. While spent coffee grounds offer notable sustainability advantages, challenges such as weak interfacial adhesion, moisture sensitivity, and reduced mechanical properties necessitate optimized processing conditions, surface treatments, and tailored material formulations. This review highlights recent advancements and outlines future research directions, emphasizing the need for stronger interactions between spent coffee grounds and polymer matrices, improved recyclability, and scalable additive manufacturing solutions to establish spent coffee grounds as a viable and eco-friendly alternative for 3D printing applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".