Exploring the Properties and Potential Uses of Biocarbon from Spent Coffee Grounds: A Comparative Look at Dry and Wet Processing Methods
Bibliographic record
Abstract
Coffee is among the most widely consumed beverages worldwide, leading to the annual generation of substantial quantities of spent coffee grounds (SCGs). This study explored the influence of fabrication methods on the properties and potential applications of the resulting biocarbon materials. Dry methods (torrefaction at 270 °C and slow pyrolysis at 500 °C) and wet methods (hydrothermal carbonization HTC at 210 °C and hydrothermal liquefaction HTL at 270 °C) were employed to fabricate SCG-based biochar and hydrochar, respectively. The carbonization degree followed the order of slow pyrolysis > HTL > HTC ≈ torrefaction, yielding significant differences in energy properties, elemental composition, morphology, and surface functionality. Slow pyrolysis biochar was suitable for energy applications due to a similar fuel ratio as and higher heating value than semianthracite coal. For agricultural applications, SCG biochar produced through dry methods could be utilized to mitigate acidic soil conditions, whereas HTC hydrochar, with its elevated surface area and porosity, could enhance soil microbiological diversity and water-holding capacity, as well as benefit environmental applications such as wastewater remediation. In summary, the findings of this study are anticipated to inform decision-making processes concerning sustainable waste management of SCGs and the exploration of carbon-based materials applications across diverse sectors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".