Valorisation of Cranberry Residues through Pyrolysis and Membrane Filtration for the Production of Value-Added Agricultural Products
Bibliographic record
Abstract
Pyrolysis is a thermochemical conversion process producing biochar, gas, and bio-oil at high temperatures in an oxygen-free environment. Specific pyrolysis conditions enable a significant production of the aqueous phase of bio-oil, commonly known as wood vinegar. Wood vinegar contains organic compounds such as acetic acid and phenols derived from bio-oil. These compounds have herbicidal properties against weeds and biostimulant properties for plant growth. This study reveals the potential for efficient management of cranberry residues consisting of stems and leaves by producing wood vinegar through pyrolysis at 475 °C with a humidity level of 20%. Membrane separation of wood vinegar, using nanofiltration (NF) and reverse osmosis (RO) membranes, yielded phenols in the retentate and acetic acid in the permeate with respective yields of 44.7% with NF membrane and 45% with RO membrane. Biostimulation tests using 2% of the retentate showed significant germination rates for basil, sage, and parsley plants. Additionally, using 40 mL of the wood vinegar permeate (30 mL injected at the base and 10 mL sprayed on the leaves) resulted in leaf damage, measured by conductivity (leakage of electrolytes released by the leaves), of 62.3% and 20.5% respectively for quack grass and white clover, two weeds found in cranberry production.
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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 | 0.000 | 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) | 0.000 | 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".