Investigating the Evaporation of Metered-Dose Inhaler Formulations in Humid Air: Single Droplet Experiments
Bibliographic record
Abstract
The effect of excipients and humidity on the evaporation of propellant from metered-dose inhaler (MDI) formulations was examined by means of single, pendant droplet experiments. Droplets of pure hydrofluoroalkane (HFA) 227ea propellant, as well as propellant-ethanol and propellant-ethanol-sorbitan-trioleate mixtures, were suspended by needle into a conditioned viewing chamber. Droplet evaporation was recorded through a microscope-coupled CCD camera for each mixture, with viewing chamber conditions of 37 degrees C and either 100% or <10% relative humidity, over a size range from approximately 4 to approximately 1 microL. Volume versus time data was collected for each droplet through digital processing of image frames, according to Axisymmetric Drop Shape Analysis (ADSA) routines. No significant difference was observed in the rate of change of droplet volume (i.e., evaporation rate) between dry and humid conditions, regardless of the formulation studied. In addition, the rate of propellant evaporation appeared unchanged despite the addition of 15% w/w ethanol to HFA 227ea, while only slight reductions in evaporation rate were observed for a mixture containing 15% ethanol and 0.2% sorbitan trioleate. It was concluded that the rate of evaporation of propellant from HFA-based MDI formulations likely remains unchanged in the presence of high levels of humidity. Therefore, alternative explanations should be explored to explain the increase in MDI particle deposition in highly humid, confined airways.
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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) | 0.001 | 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".