Sustainable and Biodegradable Copolymers from SO<sub>2</sub> and Renewable Eugenol: A Novel Urea Fertilizer Coating Material with Superio Slow Release Performance
Bibliographic record
Abstract
To enhance SO 2 utilization, improve fertilizer use efficiency, and minimize the negative environmental impact, the novel slow release sulfur-containing urea fertilizers with good biodegradation performance were developed by coating with the sustainable poly(eugenol sulfone) derived from renewable eugenol and SO 2 . The poly(eugenol sulfone) was synthesized by a simple free radical polymerization under mild conditions, and structural features of the synthesized copolymers were studied by various characterization techniques. Characterization results revealed that the copolymers exhibited the strict alternating copolymerization structures containing O═S═O. A set of systematically designed experiments were carried out to determine the influences of the amount of initiator, reaction time, and reaction temperature on the molecular structure, release, and biodegradation behavior of the coated fertilizers. The obtained results proved that the coated fertilizers showed excellent release and biodegradation features. Moreover, the release and biodegradation rate of the coated fertilizers can be adjusted by changing the molecular weight of poly(eugenol sulfone). In addition, the kinetic study on the slow release characteristics of poly(eugenol sulfone)-coated fertilizers showed that the best fitting effect was obtained by the Ritger–Peppas equation. This work offers a simple and useful strategy for designing sulfur-containing urea fertilizers with excellent slow release and biodegradation performance and provides a new route for sulfur recycling. In the future, the fertilizer will be deeply tested to evaluate their impact on plant growth, chemical, and biological soil properties.
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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".