(429d) High Shear De-Agglomeration of Nanoparticle Agglomerates in Stirred Tanks

The use of nanoparticles in liquid suspensions is becoming increasingly common in the pharmaceutical industry, where applications involving nano-sized active pharmaceutical ingredients offer significant potential benefits. However, processing of nano-particles often results in nanoparticle agglomeration, due to the high surface area and inter-particle forces per unit area acting between these particles. Therefore, de-agglomeration of nanoparticle agglomerates is a critical requirement for the target dosage form, since the size of the agglomerates obtained influences the final properties of the material.

In this work, de-agglomeration of nano-particles agglomerates in stirred tanks was experimentally studied using a stirred tank provided with a high-shear homogenizer. The agitation system consisted of a centrally mounted pitched-blade turbine (PBT) as the primary impeller, and a high-shear homogenizer, mounted in an off-set position, as the secondary impeller. The former impeller was used to suspend the particles, while the latter was used to impart the necessary shear for the breakage of the nano-particle agglomerates. The system examined here consisted of a suspension of silica nanoparticles in water, where the nanoparticles initially formed agglomerates about 30 µm in size. Experiments were conducted in which the system was subjected to high-shear mixing under different operating conditions. Samples were taken at different times and the agglomerate size distribution was measured by laser diffraction.

The results show that breakage of the nano-particle agglomerates is time dependent and is a function of the shear applied by the high-speed homogenizer. For a given set of operating conditions, the size of the agglomerate eventually reaches an asymptotic value, which is primarily a function of the homogenizer speed. Other parameters, such as tank size, primary impeller type and speed, also play a role but they only affect the time required by the system to approach the asymptotic value. The data were regressed accordingly and the results could be interpreted using a correlation relating the agglomerate size to the energy input and the aggregative strength of the particle agglomerates.