Experimental Methods in Chemical Engineering: Particle Size Distribution by Laser Diffraction—PSD
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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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".