We study the effect of three types of mesoporous silica (MPS) particles on the flow of three common excipients: microcrystalline cellulose, lactose and maize starch. While MPS are commonly considered as excipient and also as drug delivery carrier, the effects of MPS as flow aid additive and as powder stabilizer are investigated. MPS particles, called additive in the present study, are found to decrease powder cohesiveness, in particular for powders having higher water content and higher initial cohesiveness. According to both particle and pore size of MPS particles, the effect can be immediate (for small MPS particles having small pore size) or on the longer term (for larger MPS particles having higher pore size). Moreover, the electrostatic properties of the blends are modified by the presence of MPS. The quantity of electrostatic charge created in the blends during a flow in contact with stainless steel is decreased by the addition of MPS. We show that this decrease is induced by a modification of electric resistivity.

The high reactivity of nanostructured materials makes their use very attractive for various industrial applications. However, these materials may also have an important impact on health / environment / climate and on the performances of protective devices (i.e. high efficiency particulate air filters, electrostatic precipitators). Those properties are mainly due to their high specific surface area, which is directly related to the size of the non-porous primary nanoparticles and to the nature of the bridging between them (from point contact for agglomerates to partial fusion for aggregates). In this paper, a straightforward image processing has been developed to measure, assuming a log-normal size distribution, the primary particle diameter (D pp), the geometric standard deviation GSD (or σ g), the projected overlap coefficient (C ov, p) and the specific surface area (SS) directly from TEM images according to the approach introduced by Bau et al. (2010). Measurements have been performed from TEM images obtained for 22 different kinds of nanoparticles, from simple spheres to soot particles and virtual aggregates. The results show a good agreement (within +/-20 %) between automatic and manual analysis of D pp , σ g and SS while the overlap coefficient has been compared to the manual analysis showing a reasonable agreement (within +/-40 %).

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