Determining Aerosol Particle Size Distribution Using Time-Resolved Laser-Induced Incandescence
Bibliographic record
Abstract
Time-resolved laser-induced incandescence is a powerful tool for determining the physical characteristics of aerosol dispersions of refractory nano-particles. In this procedure, particles within a small aerosol volume are heated with a nano-second laser pulse, and the temporal incandescence of the particles is then measured as they return to the ambient gas temperature. It is possible to infer particle size distribution from the temporal decay of the LII signal since the cooling rate of an individual particle depends on its area-to-volume ratio. This requires solving a mathematically ill-posed inverse problem, however, since the measured LII signal is due to the incandescence contributed by all particle sizes within the aerosol volume. This paper reviews techniques proposed in the literature for recovering particle size distributions from time-resolved LII data. The characteristics of this ill-posed problem are then discussed in detail, particularly the issues of solution stability and uniqueness. Finally, the accuracy and stability of each method is evaluated by performing a perturbation analysis, and the overall performance of the techniques is compared.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".