Lime-assisted hydrothermal humification and carbonization of sugar beet pulp: Unveiling the yield, quality, and phytotoxicity of products
Bibliographic record
Abstract
Hydrothermal carbonization (HTC) solid and liquid products may inhibit seed germination, necessitating post-treatment. The hydrothermal humification (HTH) method addresses this drawback by transforming inhibitory compounds, such as aromatics, into artificial humic acids (AHAs) and artificial fulvic acids (AFAs). This study introduces a novel approach by investigating the substitution of the commonly used alkaline agent in HTH, KOH, with hydrated lime to develop cost-effective hydrothermal fertilizers from sugar beet pulp, enriching them with AHAs. It assesses the effects of lime on AHA production and soluble organic compounds compared to KOH. The results indicate that lime significantly reduces furans (from 560 to 3.15 mg/kg DM in solid and from 344 to 3.86 mg/L in process liquid) and boosts sugars and organic acids, especially lactic acid (from 4.70 to 65.82 g/kg DM in solid and from 4.05 to 22.89 mg/L in process liquid), increasing hydrochar yield (68.8% with lime vs. 27.4% with KOH). Despite the lower AHA production with lime compared to KOH (3.47% vs. 15.50%), lime-treated hydrothermal products are abundant in calcium and magnesium, boasting a pH of 7. This property presents a safer and more efficient alternative to hydrothermal fertilizers. The characterization of AHAs aligns with standard and natural humic substances, while lime-assisted HTH products, applied at a level of 0.01% w/w, could significantly enhance wheat growth and nutrient uptake compared to the control group. Importantly, these products show no toxicity on Daphnia magna, underscoring their potential for sustainable agriculture.
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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".