Utilizing Spent Coffee Ground for Sustainable Ceramic Planters: A Material-Driven Innovation Approach
Bibliographic record
Abstract
To address sustainability concerns, product designers have actively sought to repurpose waste into innovative materials, a phenomenon known as Material Driven Design.The global rise in coffee consumption, particularly in Indonesia, has led to an increase in coffee waste generation, contributing to environmental pollution and climate change.This study focuses on repurposing Spent Coffee Ground (SCG) as an additional material in ceramic production to mitigate environmental impact.Recognizing the need for a new design methodology to guide material-driven processes, this study combines qualitative literature review with a research-through-design approach.Drawing from existing studies on material innovation processes such as material-driven design and design-driven material innovation methodologies, the study proposes a comprehensive approach built upon these existing studies.This methodology, outlined in a canvas named Material Driven Innovation Canvas (MDIC), comprises seven building blocks: understanding the material, conceptualization, prototyping, setup, user testing, reflection, and iterations.The canvas is implemented in a design project to repurpose the SCG into a functional product.From a series of experiments, it was found that the strength of the clay and Spent Coffee Ground mixed material lies in its porosity, which is enhanced by incorporating 5% of fine coffee grounds.The coffee ground mixtures increase ceramic porosity and water absorption without compromising structural integrity.Hence, the proposed solution aims to develop two types of self-watering planters adaptable to various planting methods and species.To craft the ceramic planters, each planter needs a blend of 550 grams of clay and 5% Spent Coffee Ground (27.5 grams) is utilized.This mixture undergoes handthrowing and carving to achieve the desired shape, followed by drying and firing at 900℃.Through the design project, it was determined that utilizing the MDIC adds simplicity, clear visual representation, holistic views, and facilitates collaboration.This research not only offers practical insights into leveraging Spent Coffee Ground (SCG) in ceramics but also showcases the effectiveness of the Material-Driven Innovation approach in transforming waste materials into innovative products.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".