Valorization of lignin by crosslinking for slow-release urea fertilizer systems: A promising approach for agricultural applications
Bibliographic record
Abstract
Slow-release fertilizers hold promise for their extensive use in agriculture. Urea, a commonly employed fertilizer, poses the risk of elevated nitrogen levels in soil due to its water solubility when used improperly. To address this issue, the development of a cost-effective and biodegradable urea slow-release matrix was pursued by crosslinking wheat straw-derived lignin with epichlorohydrin at varying ratios (1:1, 1:2, and 2:1) and subsequently impregnating it with urea. The crosslinked lignin matrices exhibited superior properties compared to unmodified lignin. After 8 days of immersion in water, the percentage release of urea from the crosslinked lignin matrices reached 48%, whereas it was 95% for unmodified lignin. Characterization techniques such as Fourier transform infrared spectroscopy, 13C nuclear magnetic resonance, and thermogravimetry analysis were employed to gain insights into the structural and functional aspects of the crosslinked lignin. The results revealed that the crosslinked lignin possessed a higher surface area and exhibited more suitable functional groups for urea impregnation. The controlled hydrophilic-lipophilic balance of the crosslinked lignin matrices offers significant advantages in fertilizer systems, enabling a controlled and gradual release of urea. This innovative approach not only offers enhanced control over nutrient delivery but also demonstrates the potential of lignin as a sustainable and cost-effective alternative for slow-release fertilizer formulations. This development provides an effective solution to address the issue of excessive nitrogen levels and opens up new avenues in material development, holds promise for sustainable agriculture and paves the way for further advancements in the field.
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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) | 0.001 | 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".