The present work deals with the measurement of thermo-physical properties of a freestanding sheet of graphene (thermal diffusivity and thermal conductivity), and their dependence on sample density as result of uniform mechanical compression. Thermal diffusivity of graphene nano-platelets (thin slabs) was measured by the pulse flash method. Obtained response data were processed with a specifically developed least square data processing algorithm. GNP specific heat was assumed from literature and thermal conductivity derived from thermal diffusivity, specific heat and density. Obtained results show a significant difference with respect to other porous media: the thermal diffusivity decreases as the density increases, while thermal conductivity increases for low and high densities, and remain fairly constant for the intermediate range. This can be explained by the very high thermal conductivity values reached by the nano-layers of graphene and the peculiar arrangement of platelets during the compression applied to the samples to get the desired density. Due to very high thermal conductivity of graphene layers, the obtained results show that thermal conductivity of conglomerates increases when there is an air reduction due to compression, and consequent density increases, with the number of contact points between platelets also increased. In the intermediate range (250 ≤ ρ ≤ 700 kg·m-3) the folding of platelets reduces density, without increasing the contact points of platelets, so thermal conductivity can slightly decrease.

Thermal diffusivity of GNPs (graphene nano-platelets) is an important thermo-physical property as it is useful to predict the material behavior in many heat transfer applications. GNP samples were pressed at different loads to obtain different densities, and then thermal diffusivity was measured with the flash method. All samples were coated with a thin layer (~1 µm) of colloidal graphite (Aquadag®) on both sides to reduce reflectance of their surfaces and consequently increase the emissivity. Carrying out measurements on both samples with and without coating, a difference between the two series of measurements was found: This is attributed to a non-negligible transmittance of the uncoated samples due to the porosity of GNPs. Furthermore, assuming a spatial distribution of the light within the samples according to the Lambert-Bougert-Beer law, the extinction coefficient of GNP at different densities has been evaluated processing experimental data with a nonlinear least square regression, (NL-LSF, nonlinear least square fitting), whose model contains the extinction coefficient as unknown. The proposed method represents a further improvement of thermal diffusivity data processing, crucial to calculate the extinction coefficient when data with and without coating are available; or to correct biased thermal diffusivity data when the extinction coefficient is already known. Moreover, reflectance effects have been highlighted comparing asymptotic temperature reached during the tests on coated and uncoated samples at different densities. In fact, the decrease of asymptotic temperature of the uncoated samples gives the percentage of the light reflected and consequently an estimate of the reflectance of the GNP surface.

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