Technical note: Characterization of a single-beam gradient force aerosol optical tweezer for droplet trapping, phase transition monitoring, and morphology studies
Bibliographic record
Abstract
Single particle analysis is essential for a better understanding of the particle transformation process and to predict its environmental impact. In this study, we developed an aerosol optical tweezer (AOT) Raman spectroscopy system to investigate the phase state and morphology of suspended aerosol droplets in real time. The system comprises four modules: optical trapping, reaction, illumination and imaging, and detection. The optical trapping module utilizes a 532 nm laser and a 100 × oil immersion objective to stably trap aerosol droplets within 30 s. The reaction module allows us to adjust relative humidity (RH) and introduce reaction gases into the droplet levitation chamber, facilitating experiments to study liquid–liquid phase transitions. The illumination and imaging module employs a high-speed camera to monitor the trapped droplets, while the detector module records Raman scattering light. We trapped sodium chloride (NaCl) and 3-methyl glutaric acid (3-MGA) mixed droplets to examine RH-dependent morphology changes. Liquid–liquid phase separation (LLPS) occurred when RH was decreased. Additionally, we introduced ozone and limonene/pinene to generate secondary organic aerosol (SOA) particles in situ, which collided with the trapped droplet and dissolved in it. To determine the trapped droplet's characteristics, we utilized an open-source program based on Mie theory to retrieve diameter and refractive index from the observed whispering gallery modes (WGMs) in Raman spectra. It is found that mixed droplets formed core–shell morphology when RH was decreased, and the RH dependence of the droplets' phase transitions generated by different SOA precursors varied. Our AOT system serves as an essential experimental platform for in situ assessment of morphology and phase state during dynamic atmospheric processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".