Optical trapping and light scattering in atmospheric aerosol science
Bibliographic record
Abstract
Aerosol particles are ubiquitous in the atmosphere, and currently contribute a large uncertainty to climate models. Part of the endeavour to reduce this uncertainty takes the form of improving our understanding of aerosol at the microphysical level, thus enabling chemical and physical processes to be more accurately represented in larger scale models. In addition to modeling efforts, there is a need to develop new instruments and methodologies to interrogate the physicochemical properties of aerosol. This perspective presents the development, theory, and application of optical trapping, a powerful tool for single particle investigations of aerosol. After providing an overview of the role of aerosol in Earth's atmosphere and the microphysics of these particles, we present a brief history of optical trapping and a more detailed look at its application to aerosol particles. We also compare optical trapping to other single particle techniques. Understanding the interaction of light with single particles is essential for interpreting experimental measurements. In the final part of this perspective, we provide the relevant formalism for understanding both elastic and inelastic light scattering for single particles. The developments discussed here go beyond Mie theory and include both how particle and beam shape affect spectra. Throughout the entirety of this work, we highlight numerous references and examples, mostly from the last decade, of the application of optical trapping to systems that are relevant to the atmospheric aerosol.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".