Granulation and particle coating
Bibliographic record
Abstract
Introduction Particle coating and granulation have attracted increasing attention in the past decade, with the objective of modifying particle physical and physicochemical properties. The increasing interest has appeared in several industrial sectors, such as the chemical, food, pharmaceutical, iron ore, agricultural, and nuclear industries. Cosmetics, flavorings, essences, enzymes, proteins, vegetables, seeds, fertilizers, sweets and candies, drugs, pigments, and nuclear fuel microspheres are examples of products that have been modified by coating or granulation processes. Until the 1950s, rotary panels or drums were the predominant types of equipment for particle coating and granulation. Since then, new equipment and processes have been implemented by the pharmaceutical industry, owing to the replacement of tablets coated with sugar solutions by those coated with polymer films. In this new equipment, a suspension or solution is atomized on particles suspended by hot air. A thin film is deposited on the particle surfaces and dried by the hot air as the particles circulate through the chamber. Among the equipment, spouted beds, including Wurster coaters (Chapter 14) and other designs, are intended to improve the process performance and fluid dynamics – for example, by applying vibration and adding draft tubes. The choice of the most adequate equipment depends on the physical properties of the particles to be coated, as well as on the coating material. The process conditions are also critical to obtain a good-quality coated product. Fluidized and spouted beds have been extensively used recently, mainly for the film coating of particles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".