Nutrient release characteristics and coating homogeneity of biopolymer coated urea as a function of fluidized bed process variables
Bibliographic record
Abstract
Abstract The present study investigates the effect of fluid‐bed process parameters on the diffusion coefficient of nitrogen release and coating homogeneity of controlled‐release urea (CRU) produced in a rotary fluidized bed using polyvinyl‐alcohol‐modified starch as a coating material. An existing mathematical model was used to estimate the diffusion coefficient. The coefficient of variance of size distribution and coating mass variation are reported as a measure of coating homogeneity. Statistical analysis suggested that the most influential process variables that govern urea release characteristics and coating homogeneity were (a) fluidizing gas temperature and (b) coating time. A moderate spray rate combined with longer coating time yielded the lowest diffusion coefficient for nutrient release. Elutriation as the result of elevated fluidizing gas temperature allowed a higher diffusion coefficient due to lower coating thickness. Burst release patterns were observed for granules with coating imperfections. The augmented temperature of fluidizing gas had a negative effect on coating mass and size distribution of CRU granules but the influence of longer coating time was positive.
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 | 0.001 | 0.001 |
| 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".