Design of a single aspheric beam homogenizer for accurate particle sizing application
Bibliographic record
Abstract
Understanding, detection, and accurate monitoring of particles are of utmost importance in various industrial fields and environmental science. Optical sensors allow for real-time monitoring of particles at the single species level by analyzing the elastically scattered light intensities. Nevertheless, since most laser diodes employed for illuminating the particle generally follow a Gaussian-type intensity distribution, the non-uniform energy distribution across the aerosol channel causes considerable errors in the conversion of the scattered light intensities into the actual particle sizes. In order to achieve uniform illumination of particles across the aerosol channel and improve the particle sizing and classification accuracy, we design and customize a single aspheric lens, which efficiently converts the divergent Gaussian beam profile of a TO packaged laser diode into a one-dimensional flattop beam profile along the fast axis at the desired working distance. A beam uniformity better than 5% has been achieved. Furthermore, we demonstrate a practical sensing application using the designed lens for accurate particle sizing, and an obvious improvement in the accuracy has been achieved compared to that based on off-the-shelf aspheric lenses. The singlet beam homogenizer developed in this work has many appealing features (e.g., high uniformity and energy efficiency, compactness, and low stray light), which is especially relevant for building portable particle sensors in order to address various industrial applications where on-site or remote metrology and classification of particles are required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".