Aerosol generation by ultrasonic atomization of nanoliter liquid volumes
Bibliographic record
Abstract
Ultrasonic atomization is a promising technique for aerosol generation applications, including drug delivery and mass spectrometry. However, the challenge of consistently generating monodisperse mists with sub-10-micron droplets limits its broader adoption. Ultrasonic atomization of liquid volumes (30 nL–20 μL) placed on a piezoelectric transducer (PZT) is studied. The atomization process is categorized based on the size of a sessile droplet, placed on the PZT, where the droplet size is R¯0=R0/λa, normalized by the acoustic wavelength λa=c0/f, where c0 is the sound speed in the liquid and f is the excitation frequency. Previous studies have identified two regimes: R¯0>1, characterized by jetting and large droplet ejection, and R¯0<1, where bimodal or trimodal size distributions are observed. In contrast to the multimodal size distributions reported in previous works, this study introduces a new regime at R¯0=0.4, where atomization produces a monomodal fine mist with droplet sizes below 10 μm. Upon acoustic excitation, the geometry of the liquid adjusts, and the base diameter of the droplet aligns with the acoustic wavelength (Da≈λa). This behavior depends only on acoustic excitation frequency and the sound speed in the liquid, and is independent of surface type, surface velocity, or coating. At this critical regime, large surface waves disappear, the size distribution becomes time-independent, and the atomization process generates a narrow-band micrometer-sized mist. The critical liquid volume for fine mist production is determined solely by the acoustic wavelength and is given by πλa3/12.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".