The Physiochemical Properties of Pellets Made from the Foliage of Vegetable Crops
Bibliographic record
Abstract
The increasing demand for renewable energy has driven interest in utilizing agricultural residues for bioenergy applications. This study investigates the pelletization of foliage from six vegetable crops, including tomato, eggplant, summer squash, cucumber corn, and soybean, to assess their potential as bioenergy feedstocks. The physiochemical properties of these biomasses, including particle size and shape, lignin, and elemental composition, were analyzed to determine their impact on pellet density and durability. The results reveal significant variations in pellet quality across different biomasses. Cucumber and summer squash demonstrated the highest pellet densities (1.48–1.51 g/cm3) and superior durability (98.1% and 94.2%, respectively), making them the most promising candidates for pelletization. In contrast, eggplant exhibited the lowest density (1.14 g/cm3) and durability (47.2%), indicating poor pellet quality. The correlation between pellet durability and pellet density was positive and modest at r=0.647. The study further highlights the impact of inorganic elements on pellet properties, where the high silica and chlorine content of cucumber, summer squash, tomato, and eggplant reduced energy efficiency and increased ash-related challenges. The resulting color parameters analysis (L*, a*, and b*) shows that the pellets from eggplant, tomato, summer squash, and cucumber foliage are darker than pellets from sawdust, corn stover, and soybean residues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".