Impact of process variables on the micromeritic and physicochemical properties of spray-dried porous microparticles, part I: introduction of a new morphology classification system
Bibliographic record
Abstract
OBJECTIVES: This work investigated the impact of spray drying variables such as feed concentration, solvent composition and the drying mode, on the micromeritic properties of chlorothiazide sodium (CTZNa) and chlorothiazide potassium (CTZK). METHODS: Microparticles were prepared by spray drying and characterised using thermal analysis, helium pycnometry, laser diffraction, specific surface area analysis and scanning electron microscopy. KEY FINDINGS: Microparticles produced under different process conditions presented several types of morphology. To systematise the description of morphology of microparticles, a novel morphology classification system was introduced. The shape of the microparticles was described as spherical (1) or irregular (2) and the surface was classified as smooth (A) or crumpled (B). Three classes of morphology of microparticles were discerned visually: class I, non-porous; classes II and III, comprising differing types of porosity characteristics. The interior was categorised as solid/continuous (α), hollow (β), unknown (γ) and hollow with microparticulate content (δ). Nanoporous microparticles of CTZNa and CTZK, produced without recirculation of the drying gas, had the largest specific surface area of 72.3 and 90.2 m²/g, respectively, and presented morphology of class 1BIIIα. CONCLUSIONS: Alteration of spray drying process variables, particularly solvent composition and feed concentration can have a significant effect on the morphology of spray dried microparticulate products. Morphology of spray dried particles may be usefully described using the morphology classification system.
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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) | 0.000 | 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".