Prediction of segregation tendency in dry particulate pharmaceutical mixtures: Application of an adapted mathematical tool to cohesive and non-cohesive mixtures
Bibliographic record
Abstract
The measurement of average residence times and their variance, used to calculate the deviation of chemical reactors from the ideal behaviour of a perfectly-mixed vessel, or a plug flow pattern, has already been proposed in the literature to evaluate the segregation tendency of granular mixtures. The method consists of introducing pulse perturbation (of another material) to the established regular flow of a single granular material or a granular mixture and to assess the response of the system in terms of pulsed material concentration at the process outlet. The particles' average residence time and its standard deviation are then related to segregation tendency. Results from the application of this new method are useful when compared to those obtained from a reference mixture to be chosen according to a particular formulation development or process understanding need. This work applies the proposed method for various mixtures, both cohesive and non-cohesive, and derives phenomenological mathematical models expressing segregation tendency as a function of the parameters shown to be critical (i.e. statistically significant) to component segregation. The models were shown to be statistically and experimentally robust in the case of non-cohesive to slightly cohesive mixtures. Although the mathematical models are phenomenological, the findings allow for deriving mechanistic explanations on segregation tendency.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".