## 3 Results and Discussion
### Electrical Characterization  
Due to the limitations of the instrumentation used for electrical measurements, the resistance of pure PA12 and 2% xGNP samples could not be detected under any of the applied compressive loads, leading to their classification as electrical insulators. Figure 7 shows the results of the samples with a content of xGNPs from 4% to 10%. For better visualization, the \(y\)-axis is represented in the logarithmic scale. As can be seen, there is an abrupt increase in the electrical conductivity with a filler content greater than 4% of xGNPs, from, e.g., at 1 kN of compressive load, around 1.07\(\cdot\)10\({}^{-9}\) S/cm up to 2.02\(\cdot\)10\({}^{-6}\) S/cm for 6% (i.e., similar to silicon [54]), 6.22\(\cdot\)10\({}^{-5}\) S/cm for 8% (i.e., similar to boron [55]), and 3.93\(\cdot\)10\({}^{-4}\) S/cm for 10% (i.e., similar to germanium [56]). This finding can be ascribed to the higher number of conductive paths developed at the nanoscale between xGNPs and PA12 for increasing filler content [57]. It is worth noting that the electrical conductivity increases linearly with the applied load. Such a phenomenon is due to the compacting effect induced by the compressive load, which closes porosities and promotes an interconnected path between the nanoplatelets, thus increasing the electrical conductivity [58]. \begin{table}
\begin{tabular}{c c c c c c} \hline
**xGNPs [\%]** & \(\mathbf{T_{m}}\) [\({}^{\circ}\)C] & \(\mathbf{T_{m,On}}\) [\({}^{\circ}\)C] & \(\mathbf{T_{r}}\) [\({}^{\circ}\)C] & \(\mathbf{T_{r,On}}\) [\({}^{\circ}\)C] & \(\mathbf{\Delta_{Onset}}\) [\({}^{\circ}\)C] \\ \hline
0 & 180.7 & 173.7 & 155.6 & 157.8 & 15.9 \\
2 & 180.3 & 173.6 & 155.7 & 157.8 & 15.9 \\
4 & 179.0 & 174.0 & 156.0 & 158.1 & 16.0 \\
6 & 180.4 & 174.0 & 156.2 & 158.4 & 15.6 \\
8 & 180.4 & 174.4 & 156.2 & 158.6 & 15.8 \\
10 & 180.5 & 174.6 & 156.2 & 158.4 & 16.2 \\ \hline \end{tabular}
\end{table} 
Table 4: DSC results in melting (\(T_{m}\)), onset melting (\(T_{m,On}\)), recrystallization (\(T_{r}\)), and onset recrystallization (\(T_{r,On}\)) temperatures for the identification of the sintering window (\(\Delta_{Onset}\)). Figure 6: DSC results with magnification of melting and recrystallization peaks. Thanks to the distribution of the electrically conductive nanoplatelets within the insulating matrix, the resulting composite behaves as a conductor. It is therefore possible to define a percolation threshold, where the critical amount of conducting particles for the onset of electrical conduction is reached and the electrical properties of the material typically exhibit nonlinear behavior [59], as shown in Figure 8.  
The relation between the composite conductivity (\(\sigma_{C}\)) and the filler concentration (\(\varphi_{f}\)) can be found through the percolation model described by Equation (8) [60]:  
\[\sigma_{C}=\sigma_{f}\left(\varphi_{f}-\varphi_{cr}\right)^{t}, \tag{8}\]  
where \(\sigma_{f}\) is the filler conductivity, \(\varphi_{cr}\) is the percolation threshold, and \(t\) is the critical exponent. The values of \(\sigma_{f}\), \(\varphi_{cr}\), and \(t\) were determined through the fitting of the experimental data. It is worth noting that the volume fraction of xGNPs was evaluated by weighing the samples after fabrication with a precision balance and measuring the actual dimensions with a digital caliper to determine their volumes. Therefore, since the weight percentage was known, together with  
Figure 8: Percolation models for varying compressive loads. Figure 7: Electrical conductivity results. Only the values for 4%, 6%, 8%, and 10% xGNP contents are illustrated, since no resistance was measured for 0% and 2% contents due to the instrumentation limit.