The study of aerosolized droplets with nanometer absorbing structures using a contactless photoacoustic technique and the finite-difference time-domain method
Bibliographic record
Abstract
This study explores a novel approach to detect virus-laden droplets in the ambient air. An air-coupled photoacoustic (PA) technique is considered for this purpose. The free space PA system is developed using an air-coupled transducer with a center frequency of 350 kHz and a nanosecond pulsed laser operating at wavelength 533 nm. Water droplets containing 80 nm gold (Au) nanoparticles were aerosolized using a custom-built spraying system. The size of the droplets generated was in the range of a few hundred nanometers to 100 μm. Au nanoparticles of four concentrations (0, 8x10-12, 16x10-12, and 32x10-12 mol/L) were sprayed into the investigation domain interrogated by a laser beam, where the average PA signal from the droplets was 3.11±2.35, 1.28±1.26, 0.99±0.97, and 0.92±1.11 mV/mJ, respectively. The study showed, surprisingly, that water droplets without Au nanoparticles had a higher PA signal than those containing Au nanoparticles. A numerical analysis using a finite difference time domain method was used to explore the reasons for this unexpected finding. Results suggested that the undoped droplets could potentially focus the light, significantly increasing the fluence at the focus. When Au nanoparticles were present, the fluence within the droplet decreased, resulting in a lower PA signal.
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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 | 0.001 |
| 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".