Highly stretchable strain sensors based on Marangoni self-assemblies of graphene and its hybrids with other 2D materials.

Graphene and other two-dimensional materials (2DMs) have been shown to be promising candidates for the development of flexible and highly-sensitive strain sensors. However, the successful implementation of 2DMs in practical applications is slowed down by complex processing and still low sensitivity. Here, we report on a novel development of strain sensors based on Marangoni self-assemblies of graphene and of its hybrids with other 2DMs that can both withstand very large deformation and exhibit highly sensitive piezoresistive behaviour. By exploiting the Marangoni effect, reference films of self-assembled reduced graphene oxide (RGO) are first optimized, and the electromechanical behaviour has been assessed after deposition onto different elastomers demonstrating the potential of producing strain sensors suitable for different fields of application. Hybrid networks have been then prepared by adding hexagonal boron nitride (hBN) and fluorinated graphene (FGr) to the RGO dispersion. The hybrid integration of 2D materials is demonstrated to become a potential solution to increase substantially the sensitivity of the produced resistive strain sensors without compromising the mechanical integrity of the film. In fact, for large quasi-static deformations, a range of gauge factor values up to 2000 were demonstrated, while retaining a stable performance under cyclic deformations.

Mesoscopic Modeling and Experimental Validation of Thermal and Mechanical Properties of Polypropylene Nanocomposites Reinforced By Graphene-Based Fillers.

The development of nanocomposites relies on structure-property relations, which necessitate multiscale modeling approaches. This study presents a modeling framework that exploits mesoscopic models to predict the thermal and mechanical properties of nanocomposites starting from their molecular structure. In detail, mesoscopic models of polypropylene (PP)- and graphene-based nanofillers (graphene (Gr), graphene oxide (GO), and reduced graphene oxide (rGO)) are considered. The newly developed mesoscopic model for the PP/Gr nanocomposite provides mechanistic information on the thermal and mechanical properties at the filler-matrix interface, which can then be exploited to enhance the prediction accuracy of traditional continuum simulations by calibrating the thermal and mechanical properties of the filler-matrix interface. Once validated through a dedicated experimental campaign, this multiscale model demonstrates that with the modest addition of nanofillers (up to 2 wt %), the Young's modulus and thermal conductivity show up to 35 and 25% enhancement, respectively, whereas the Poisson's ratio slightly decreases. Among the different combinations tested, the PP/Gr nanocomposite shows the best mechanical properties, whereas PP/rGO demonstrates the best thermal conductivity. This validated mesoscopic model can contribute to the development of smart materials with enhanced mechanical and thermal properties based on polypropylene, especially for mechanical, energy storage, and sensing applications.

3D Printing Processability of a Thermally Conductive Compound Based on Carbon Nanofiller-Modified Thermoplastic Polyamide 12.

A polyamide (PA) 12-based thermoplastic composite was modified with carbon nanotubes (CNTs), CNTs grafted onto chopped carbon fibers (CFs), and graphene nanoplatelets (GNPs) with CNTs to improve its thermal conductivity for application as a heat sink in electronic components. The carbon-based nanofillers were examined by SEM and Raman. The laser flash method was used to measure the thermal diffusivity in order to calculate the thermal conductivity. Electrical conductivity measurements were made using a Keithley 6517B electrometer in the 2-point mode. The composite structure was examined by SEM and micro-CT. PA12 with 15 wt% of GNPs and 1 wt% CNTs demonstrated the highest thermal conductivity, and its processability was investigated, utilizing sequential interdependence tests to evaluate the composite material behavior during fused filament fabrication (FFF) 3D printing processing. Through this assessment, selected printing parameters were investigated to determine the optimum parametric combination and processability window for the composite material, revealing that the selected composition meets the necessary criteria to be processable with FFF.

Shape Memory Composite Sandwich Structures with Self-Healing Properties.

In this study, Polyurea/Formaldehyde (PUF) microcapsules containing Dicyclopentadiene (DCPD) as a healing substance were fabricated in situ and mixed at relatively low concentrations (<2 wt%) with a thermosetting polyurethane (PU) foam used in turn as the core of a sandwich structure. The shape memory (SM) effect depended on the combination of the behavior of the PU foam core and the shape memory polymer composite (SMPC) laminate skins. SMPC laminates were manufactured by moulding commercial carbon fiber-reinforced (CFR) prepregs with a SM polymer interlayer. At first, PU foam samples, with and without microcapsules, were mechanically tested. After, PU foam was inserted into the SMPC sandwich structure. Damage tests were carried out by compression and bending to deform and break the PU foam cells, and then assess the structure self-healing (SH) and recovery capabilities. Both SM and SH responses were rapid and thermally activated (120 °C). The CFR-SMPC skins and the PU foam core enable the sandwich to exhibit excellent SM properties with a shape recovery ratio up to 99% (initial configuration recovery). Moreover, the integration of microcapsules (0.5 wt%) enables SH functionality with a structural restoration up to 98%. This simple process makes this sandwich structure ideal for different industrial applications.