Statistics-aided optimal design of the continuous aerosol-based synthesis of nanostructured silica supports

The present work aims to answer the question, which combinations of parameters lead to which texture, and which ones have the largest influence, in the continuous synthesis of nanoporous silica particles from an aerosol. A precursor solution, consisting of dissolved organosilicate species (as silica source) and micelles of a non-ionic surfactant, was fed to a pneumatic aerosol generator resulting in an aerosol consisting of fine droplets, which was subsequently fed into a heated aerosol reactor. The homogeneous microdroplets underwent rapid heating inducing co-operative self-assembly of the silica species with the surfactant micelles to form fine powders composed of an inorganic/organic composite. Further calcination at high temperature decomposed the organic part, resulting in silica particles with a well-defined nanostructured pore network. The number of experimental parameters that potentially influence the final texture of the materials is very large. In order to probe this high-dimensional experimental parameter space, a rigorous statistical methodology is applied, which allows us to greatly reduce the number of experiments. A factorial design was formulated and appropriate statistical techniques were used to evaluate the effect of four experimental variables involving precursor composition and furnace temperature on the BET surface area and total pore volume of the generated particles. The statistical methodology discussed here is generally applicable, easy to implement, and insightful. We recommend using it to efficiently and rigorously investigate in (nano) materials synthesis in general which combinations of synthesis parameters are statistically relevant and which ones are not.