Effect of crystallization kinetics on the properties of spray dried microparticles
Bibliographic record
Abstract
A droplet chain technique was used to study the influence of the crystallization process on the morphology of spray dried microparticles. A piezoceramic dispenser produced a chain of monodisperse solution droplets with an initial diameter in the range of 60–80 µm. Aqueous solutions of sodium nitrate were prepared in concentrations ranging from 5 mg/ml to 5⋅10−5 mg/ml. The solution droplets were injected into a laminar flow with gas temperatures varying from 25 to 150°C, affecting the droplet temperature and the evaporation rate, accordingly. Dried particles with diameters between 0.3 and 18 µm were collected. The properties of the collected microparticles were studied and correlated with a particle formation model which predicted the onset of saturation and crystallization. The model accounted for the dependence of the diffusion coefficient of sodium nitrate in water on droplet viscosity. The viscosity trend for sodium nitrate solutions was determined by studying the relaxation time observed during coalescence of two aqueous sodium nitrate droplets levitated in optical tweezers. The combination of theoretical derivations and experimental results showed that longer time available for crystallization correlates with larger crystal size and higher degrees of crystallinity in the final microparticles.© 2016 American Association for Aerosol Research
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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".