Experimental Methods in Chemical Engineering: Particle Size Distribution by Laser Diffraction—PSD
Why this work is in the frame
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Bibliographic record
Abstract
Particle size is the top cited physical property researchers report in The Canadian Journal of Chemical Engineering and among the top properties in all science disciplines. [1] Techniques to measure particle size distribution (PSD) include physical operations like sieving and sedimentation, and spectroscopic techniques like laser diffraction image analysis based on optical and electron microscopy, and elecro‐zone instruments. Here we concentrate on laser diffraction analysis (LDA) and review its basic principles, operations, limitations, uncertainties, and mention how it compares to other techniques. LDA is an instantaneous, user‐friendly, convenient, and non‐destructive method to assess PSD of inorganic powders. It measures the scattering angle and intensity of light after it passes through diluted particle dispersions suspended in either a gas or liquid. The Mie theory is an exact solution to resolve the diffraction intensity of light caused by particles that applies to while the Fraunhoffer approximation applies only to particles greater than 20 m. The 95 % confidence interval of five measurements of 56 m and 0.1 m irregularly shaped polyhedrons was . Based on a bibliometric analysis of LDA of the top 10 000 cited articles in 2016 and 2017, the major research clusters are: particle measurement, powder behaviour, pharmacy, comminution, and adsorption. Future work will continue to introduce more laser sources, combine multiple technologies, implement mobile light sources (dynamic light scattering), and better define characterize irregularly shaped particles.
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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) | 0.001 | 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 it