Optical deformation of single aerosol particles
Bibliographic record
Abstract
Advancements in designing complex models for atmospheric aerosol science and aerosol-cloud interactions rely vitally on accurately measuring the physicochemical properties of microscopic particles. Optical tweezers are a laboratory-based platform that can provide access to such measurements as they are able to isolate individual particles from an ensemble. The surprising ability of a focused beam of light to trap and hold a single particle can be conceptually understood in the ray optics regime using momentum transfer and Newton's second law. The same radiation pressure that results in stable trapping will also exert a deforming optical stress on the surface of the particle. For micron-sized aqueous droplets held in the air, the deformation will be on the order of a few nanometers or less, clearly not observable through optical microscopy. In this study, we utilize cavity-enhanced Raman scattering and a phenomenon known as thermal locking to measure small deformations in optically trapped droplets. With the aid of light-scattering calculations and a model that balances the hydrostatic pressure, surface tension, and optical pressure across the air-droplet interface, we can accurately determine surface tension from our measurements. Our approach is applied to 2 systems of atmospheric interest: aqueous organic and inorganic aerosol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".