Thermal energy storage (TES) is popular to shift peak-load, eliminate the intermittency of renewable energy and recover industrial waste heat. Molten nitrate salts are the TES materials widely used in solar power plants. Pure nitrate salts show some unfavourable properties and hence composite salts are usually required. Adding nanoparticles into nitrate salts was reported as a good method to enhance their properties. However, it does not always show positive results and the enhancements are sensitive to a number of parameters. Till now, there is also no a robust theoretical model to explain these phenomena due to the lack of enough experimental data. Hence, more experimental studies are needed for promoting the development of theoretical models. In this paper, three molten nitrate salts based nanofluids are synthesized by doping SiO2 nanoparticles into three popular single salts (NaNO3, LiNO3 and KNO3). Effects of nanoparticle sizes (15 nm - 5μm), mass fractions (0.5 - 4%) and temperature (200 - 380 oC) are considered. Thermo-physical properties of the composite materials are tested, including material structure, melting point, latent heat, specific heat capacity and thermal conductivity. Results show that the addition of SiO2 nanoparticles has little effect on melting points of LiNO3 and KNO3 , but it increases their latent heat by ~1.7%; for NaNO3, it smelting points are decreased by up to 2.5 oC while latent heat keeps stable with the reasonable SiO2 nanoparticle size and mass fraction. The specific heat capacities of NaNO3 and LiNO3 are enhanced by 27.6% and 12.3% with the addition of 60-70 nm 2% and 0.5% SiO2 nanoparticles, respectively; for KNO3, the highest enhancement of 26% is achieved with 15-20 nm 1% SiO2 nanoparticles. It is surprising to find that the addition of SiO2 nanoparticles leads to a decreased thermal conductivity for all the three nitrate salts, which is probably due to the thermal resistance. This paper provides comprehensive and valuable experimental data, contributing to the development of robust models for predicting the effect of nanoparticles.