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In order to make aerogel available for a wide application range, the synthesis complexity, use of hazardous components and high cost price need to be lowered. In this work, silica aerogel powders were obtained using water glass as a precursor, but without performing ion exchange resulting in a faster and easier synthesis. For this study, 6 and 8 wt% silica sols were prepared and the difference in properties of the resulting aerogels was investigated. A combined solvent exchange, silylation and washing out of sodium ions was carried out using a hexane:trimethylchlorosilane:isopropylalcohol solution. A molar ratio trimethylchlorosilane/pore water of only 0.11 was used. The resulting hydrophobic organogels were then dried at ambient pressure and temperature. The aerogel powder was finally dried in an oven at 150°C to remove the residual moist. No notable shrinkage was observed during and after drying for samples with 8 wt% silica. The obtained aerogel showed low density of around 100-120 kg/m 3 , high porosity of 93-94%, pore volume of 0.6-4.1 cm 3 /g, an average pore size of 32-50 nm, and a thermal conductivity of 23-25 mW m-1 K-1 , all depending on the weight percentage of silica and pH of the sol.

Since regulations on insulation building materials become more stringent, particular interest is growing in thermal low conductive composite building materials. By adding very expensive, low thermal conductive nanoparticles in widely used inorganic solid materials, a new high performance building material can be developed. To sufficiently balance the production cost and the use of the nanofillers, an automatic tool, PreCon, was developed to provide sufficient process information and to generate a general purpose numerical model which can be used for thermal insulation simulations. It is expected that a total random distribution of the particles in the inorganic matrix will not result in a significant increase in insulation properties and hence will not enhance the performance. PreCon is used to investigate the influence of a random distributed nanomaterial combined with a more normal distribution through thickness. Based on this information, one can gauge the efforts needed to localize the nanoparticles in particular regions. However, accurate and repetitive thermal conductivity measurements of real dispersions are very difficult and precarious to obtain. Nevertheless, combining PreCon with Siemens simulation software provides appropriate nanofiller distributions with respect to cost. The realized tool is also able to introduce fibrous material distributions as a composite reinforcement. POLYM. ENG. SCI., 58:568-585, 2018.

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