In this study, the structure, electrical and thermal properties of ten polymer compositions based on polylactic acid (PLA), low-cost industrial graphene nanoplates (GNP) and multi-walled carbon nanotubes (MWCNT) in mono-filler PLA/MWCNT and PLA/GNP systems with 0–6 wt.% filler content were investigated. Filler dispersion was further improved by combining these two carbon nanofillers with different geometric shapes and aspect ratios in hybrid bi-filler nanocomposites. Scanning electron microscopy (SEM), transmission electron microscopy (TEM) and Raman spectroscopy exhibited uniform dispersion of nanoparticles in a polymer matrix. The obtained results have shown that for the mono-filler systems with MWCNT or GNP, the electrical conductivity increased with decades. Moreover, a small synergistic effect was observed in the GNP/MWCNT/PLA bi-filler hybrid composites when combining GNP and CNT at a ratio of 3% GNP/3% CNT and 1.5% GNP:4.5% CNT, showing higher electrical conductivity with respect to the systems incorporating individual CNTs and GNPs at the same overall filler concentration. This improvement was attributed to the interaction between CNTs and GNPs limiting GNP aggregation and bridging adjacent graphene platelets thus, forming a more efficient network. Thermal conductivity increases with higher filler content; this effect was more pronounced for the mono-filler composites based on PLA and GNP due to the ability of graphene to better transfer the heat. Morphological analysis carried out by electron microscopy (SEM, TEM) and Raman indicated that the nanocomposites present smaller and more homogeneous filler aggregates. The well-dispersed nanofillers also lead to a microstructure which is able to better enhance the electron and heat transfer and maximize the electrical and thermal properties. The obtained composites are suitable for the production of a multifunctional filament with improved electrical and thermal properties for different fused deposition modelling (FDM) 3D printing applications and also present a low production cost, which could potentially increase the competitiveness of this promising market niche.

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The limitation of poor mechanical stability and difficulties in printing electrically conductive components can be overcome owing to the recent introduction of nanotechnology into the field of additive manufacturing (AM) and the consequent development of nonconventional polymer nanocomposites suitable for 3D printing. In the present work, different weight percentages (up to 6 wt % in total) of carbon-based nanostructures—multiwalled carbon nanotubes (MWCNTs), graphene nanoplatelets (GNPs), and a combination of both fillers (MWCNTs/GNPs)—were incorporated into poly(lactic) acid (PLA, Ingeo™) in an attempt to overcome several limitations of conventional 3D manufacturing based on insulating materials. Solution blending and melt mixing were the two fabrication methods adopted for preparation of the samples under test. A comparison of the morphological, rheological, and electrical properties of the resulting nanocomposites was carried out. Moreover, for the same weight concentrations, the influence of physical and geometrical features (i.e., functionalization and aspect ratio) of the embedded fillers was also investigated. Rheological methods were applied to control the quality of fillers dispersion in PLA matrix. The rheological percolation threshold was considered as reference in order to evaluate the internal structure of nanodispersions. TEM visualization, combined with rheological characterizations, was used for efficient control of the nanofiller dispersion. DC characterization revealed that lower electrical percolation thresholds and higher values of electrical conductivity were achieved using fillers with a larger aspect ratio and melt mixing, respectively. Moreover, given the possibility of obtaining complex and appropriate shapes for electromagnetic compatibility (EC) applications, electromagnetic (EM) response of the nanocomposites at the highest filler concentration was investigated in GHz and THz regions. It was found that the electromagnetic shielding efficiency (EMI) of nanocomposites strongly depended on the aspect ratio of the nanofillers, whereas the type of processing technique did not have a significant effect. Therefore, a careful choice of methods and materials must be made to address the final application for which these materials and further 3D printed architectures are designed.

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This paper aims to address current limitations of 3D printed conductive materials through the development of a novel formulation of a thermoplastic composite. In particular, a conductive filament suitable for three-dimensional printing is obtained on the basis of Polylactic acid (PLA) filled with two types of highly conductive nanocarbon materials, i.e. multi-walled carbon nanotubes (MWCNTs), graphene nanoplates (GNPs) and a combination of both fillers (MWCNT/GNP). A systematic rheological and electrical characterization of the resulting nanocomposites is presented. Viscoelastic properties and rheological percolation threshold are determined for the binary and ternary composites and related to the size of nanoparticles. Comparable values for the percolation threshold are found by means of rheological and electrical studies. Low electrical percolation thresholds and high values of the electrical conductivity of the order of S/m are achieved for the investigated formulations. At the highest filler loading (i.e. 12 wt%) the electrical conductivity reaches the value of 4.54 S/m, 6.27 S/m and 0.95 S/m for the composites based on MWCNTs, GNPs and multiphase system, respectively. These results, together with the good stability shown by the nano-reinforced PLA in the frequency range [100 Hz-1MHz] make these composites promising candidates for 3D printed conductive devices for electromagnetic (EM) applications.

In this study, the effects of three processing stages: filament extrusion, 3D printing (FDM), and hot-pressing are investigated on electrical conductivity and tensile mechanical properties of poly(lactic) acid (PLA) composites filled with 6 wt.% of multiwall carbon nanotubes(MWCNTs), graphene nanoplatelets (GNPs), and combined fillers. The filaments show several decades’ higher electrical conductivity and 50–150% higher values of tensile characteristics, compared to the 3D printed and the hot-pressed samples due to the preferential orientation of nanoparticles during filament extrusion. Similar tensile properties and slightly higher electrical conductivity are found for the hot-pressed compared to the 3D printed samples, due to the reduction of interparticle distances, and consequently, the reduced tunneling resistances in the percolated network by hot pressing. Three structural types are observed in nanocomposite filaments depending on the distribution and interactions of fillers, such as segregated network, homogeneous network, and aggregated structure. The type of structural organization of MWCNTs, GNPs, and combined fillers in the matrix polymer is found determinant for the electrical and tensile properties. The crystallinity of the 3D printed samples is higher compared to the filament and hot-pressed samples, but this structural feature has a slight effect on the electrical and tensile properties. The results help in understanding the influence of processing on the properties of the final products based on PLA composites.

[1] Landslide inventories show that the statistical distribution of the area of recorded events is well described by a power law over a range of decades. To understand these distributions, we consider a cellular automaton model based on a dissipative dynamical variable associated to a time and position dependent factor of safety. The model is able to reproduce the complex structure of landslide distribution, as experimentally reported. In particular, we investigate the role of the rate of change of the system dynamical variables, induced by an external drive, on landslide modeling and its implications on hazard assessment. As the rate is increased, the model has a crossover from a critical regime with power-laws to non power-law behaviors. We suggest that the detection of patterns of correlated domains in monitored regions can be crucial to identify the response of the system to perturbations, i.e., for hazard assessment. Citation: Piegari, E., V. Cataudella, R. Di Maio, L. Milano, and M. Nicodemi (2006), A cellular automaton for the factor of safety field in landslides modeling, Geophys. Res. Lett., 33, L01403,