Fluidization of fine lactose for dry powder inhalation: A comparison of assisting methods
Bibliographic record
Abstract
Abstract Fluidization of cohesive pharmaceutical powders is difficult to achieve and typically requires the introduction of external forces. This study investigates the fluidization of the fine inhalation grade of lactose powders (size range from 0.1‐20 μm) that are specifically developed for dry powder inhalation (DPI) applications. The fluidization behaviour of fine lactose powders was evaluated under six conditions: without fluidization aids, with only vertical vibration (VFA), with only a downward‐pointing micro‐jet (MFA), with both vibration and pre‐mixing with coarse particles (VCFA), with both vibration and micro‐jet (VMFA), and with the combined assistance of vibration, micro‐jet, and addition of coarse particles (VMCFA). The enhancement of fluidization due to the use of different assistance methods is reflected by the increase of bed expansion and the decrease in both the minimum fluidization velocity and agglomerate formation. However, applying micro‐jet results in considerable powder losses due to the high fraction of fine particles stuck to the wall. Combining any two assisting methods leads to better fluidization than using a single approach. In particular, the combination of vibration and micro‐jet shows the best performance in improving fluidization. Further addition of coarse particles does not play a significant influence on promoting fluidization. Finally, the analysis of the forces acting on the lactose agglomerates shows the enhancement of separation forces by introducing the fluidization assistance, which leads to a decrease in agglomerate size.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".