Tracking Water Sorption in Glassy Aerosol Particles using Morphology-Dependent Resonances
Bibliographic record
Abstract
Morphology-dependent resonances (MDRs) can serve as a sensitive probe of the size and composition of microspheres. While the utilization of MDRs to characterize homogeneous spheres is now routine, analysis of spherical particles with more complicated refractive index profiles can be extremely difficult and time consuming. In ultraviscous and glassy aerosol particles, the concentration profile of water during sorption often contains a sharp front that propagates from the particle surface to the particle center over time. Here we show that the MDR positions associated with this type of concentration profile closely match those of a spherical core-shell profile. Due to the similarities, a core-shell model can be used to simplify the analysis of MDR positions that are observed during water uptake by high-viscosity aerosol particles. We examined the applicability and limitations of this core-shell model in the tracking of water sorption by single particles. Overall, the core-shell model allows for the radial position of a sharp diffusion front to be readily found using MDR positions observed during water sorption, making the analysis of light-scattering measurements much faster and less error prone than previously used fitting schemes. Additionally, methods for calculating MDRs in spherical core-shell particles are also discussed.
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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 | 0.000 |
| Open science | 0.000 | 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".