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.