Exploring the use of atmospheric freeze drying for dehydrating pharmaceutics in vials: Baseline water sublimation investigation
Bibliographic record
Abstract
Atmospheric freeze-drying (AFD) is a relatively new freeze-drying technology without the need for a vacuum, making it easy to operate and cost-effective. Although there have been studies using AFD to dehydrate solid foods, there is currently no research on using AFD to dehydrate frozen liquids in pharmaceutical vials. In this study, several approaches were evaluated to enhance the atmospheric sublimation of water in pharmaceutical vials. Using −4 °C impinging jet airflow to dry frozen water samples in the vial, it was found that convective action significantly affected the sublimation rate. On this basis, a 3D-printed air-guide model was designed to improve airflow circulation in the vial, and it was found that the drying rate was highest when airflow energy loss was minimized, and airflow velocity at the sample surface was maximized. Additionally, the geometric characteristics of the vial also influenced the sublimation rate; vials with a larger bottom area and shorter height showed the highest sublimation rate. Increasing the vial’s bottom radius from 11 mm to 13 mm, under atmospheric pressure and using cold air at approximately −5 °C, reduced the drying time of 1 g of frozen water from 8.5 h to 6 h; each 5 mm height increase added 0.5 h to the drying time. Using cold air at −10 °C to dry 1 g of frozen water in a 5 mL vial, the combination of ultrasonic-induced energy (at a frequency of 39.46 kHz) and the air-guide model effectively reduced the sublimation time from 7 h to 5 h, compared to using only the air-guide model. However, this technique may be vulnerable to melting at the vial-transducer contact point, should the transducer be directly attached to the vials.
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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 | 0.001 | 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 | 0.001 |
| 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".