Determining the size and refractive index of homogeneous spherical aerosol particles using Mie resonance spectroscopy
Bibliographic record
Abstract
Methods for determining the size and refractive index of single, homogeneous, micrometer-sized aerosol particles using Mie resonance spectroscopy are studied using measurements from optically trapped particles and light-scattering calculations based on Mie theory. We consider both single-particle broadband light scattering and cavity-enhanced Raman scattering (CERS) and demonstrate that, when resonances observed in either type of spectroscopy are fitted using Mie theory, the accuracy of the best fits are similar. However, broadband measurements can yield more resonances than CERS, thus reducing the uncertainty in the retrieved parameters of best fit and increasing the range of particles that can be characterized. Resonance fitting methods are also compared to methods that fit the entire Mie scattering spectrum. Through calculations, it is shown that measured scattering spectra are sensitive to small changes in how light is collected, while Mie resonance positions are much less sensitive. This means that additional parameters are required to accurately fit entire light-scattering spectra using Mie theory, but these parameters are not needed to accurately determine Mie resonance positions.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".