Impact of batch variability on physicochemical properties of manufactured TiO2 and SiO2 nanopowders

International audienceThe development, manufacturing and commercialization of nanomaterials require traceable characterisation processes for quality control and safety of both the exposed workers and final customers. Even if the production batches are considered to be compliant with the industrial applications intended by manufacturers, it is necessary to study the reproducibility of the manufacturing process of nanomaterials independently, so as to determine the variability of key physico-chemical properties of nano-objects from one batch to another.In this study, a metrological approach was employed, using different traceable analytical techniques (X-Ray Diffraction, Transmission Electron Microscopy, Nitrogen physisorption with Brunauer–Emmett–Teller method, X-Ray Fluorescence, Scanning Mobility Particle Sizer and Aerodynamic Particle Sizer) to develop robust, reproducible and statistical methods to evaluate the impact of batch variability on physico-chemical properties of manufactured titanium dioxide and silicon dioxide nano-powders (crystalline structure, crystallite size, primary particle size, specific surface area, chemical composition and the dustiness of nanopowders).Five references of manufactured titanium dioxide nanoparticles and silicon dioxide nanoparticles were characterized with the developed measurement protocols. The reproducibilities of five batches by reference were overall inferior to 10% for crystalline structures, primary particle sizes, specific surface areas and the chemical composition of major components (TiO2 and SiO2) of the nanopowders studied (k = 1). As for the size distributions of released particles from dustiness tests, reproducibility for the modal and mean diameters ranged between 2% and 27%. Moreover, a large variation of nanopowder dustiness was obtained for the same material type (TiO2 or SiO2). This could point out that the physico-chemical properties of nanopowders, linked to the manufacturing process, have a strong impact on the dustiness parameter