Characterizing single airborne droplets using photoacoustic sensing and acoustic levitation
Bibliographic record
Abstract
The inhalation of viral airborne droplets is classified as the primary mode of COVID-19 transmission. However, there is a notable gap in current research with respect to real-time detection of viral droplets in open air. Previously, it was shown that photoacoustics can be used to distinguish between airborne particulates sprayed into the open air based on absorber concentration. It was determined that the peak photoacoustic amplitudes from water were highest, and as the absorber concentration within water increased, the signal decreased, opposite to expectations; the detected photoacoustic signals were suspected to be attributed to the spherical geometry of the droplets. To understand the impact of droplet geometry on the signal intensity in isolation, an open-air photoacoustic system was developed using a 500 kHz ultrasound transducer and 475nm nanosecond pulsed laser. An acoustic levitation system was constructed to examine light interactions with single droplets while maintaining the spherical geometry of the sample. Single droplets of acridine orange dye were analyzed at various concentrations and diameters. The transducer was positioned 5 cm away from the levitation setup to detect the generated photoacoustic signals, which were then analyzed to determine the characteristics of the droplets based on diameters ranging from 300 micron to 2.5 mm. The signals increased proportional to the absorber concentration of the dye droplet; as they evaporated, the signal increased with each subsequent pulse before the droplet vaporized. Current experiments aim to categorize the photoacoustic response based on droplet and laser parameters, with potential applications in sensing airborne viral material.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".