Tribo-Electrification and Associated Segregation of Pharmaceutical Bulk Powders
Bibliographic record
Abstract
Powder handling operations can give rise to the tribo-electrification of particles, causing a number of problems such as risk of fire and explosion, particle adhesion to the walls of processing equipment and segregation. Current methods available for measuring the dynamic charging of bulk powders are unsuitable for testing/handling small quantities of powders, some of which are highly active. Furthermore, very little work has been reported on the effect of tribo-electrification on the segregation of components of mixtures.A methodology has recently been developed for investigating the tribo-electrification of small quantities of bulk powders using a shaking device. Two common pharmaceutical excipients, namely α-lactose monohydrate (α-LM) and hydroxypropyl cellulose (HPC) were used as model materials. The electric charge transferred to the particles was quantified as a function of shaking time, frequency and container material. The temporal trend follows a first-order rate process.Using numerical simulations based on the Distinct Element Method (DEM), the charge accumulation of an assemblage of alumina beads inside the shaking device was analysed based on the single particle contact charge obtained from the experiments. It was shown that the inclusion of electrostatic mechanisms into the DEM model leads to an improved prediction of the charge buildup, but the difference with experimental data is still notable.Using the above method, segregation induced by tribo-electric charging was characterised for binary mixtures comprising α-LM and HPC. The bulk and wall-adhered particles were analysed for the mass fraction of each component using selective dissolution of one component and filtration of the non-dissolving component, followed by a gravimetric analysis. The findings reveal that a considerable level of segregation can take place on the wall-adhered particles.The method described here has the potential to be used to characterise small quantities of pharmaceutical powders including active pharmaceutical ingredients (API), which are sparse in the early development stages.
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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 | 0.000 |
| 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 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".