Effect of processing methods and functional groups on the properties of multi-walled carbon nanotube filled poly(dimethyl siloxane) composites

Integrating Benzoxazine-PDMS 3D Networks with Carbon Nanotubes for flexible Pressure Sensors

Shapeable and flexible pressure sensors with superior mechanical and electrical properties are of major interest as they can be employed in a wide range of applications. In this regard, elastomer-based composites incorporating carbon nanomaterials in the insulating matrix embody an appealing solution for designing flexible pressure sensors with specific properties. In this study, PDMS chains of different molecular weight were successfully functionalized with benzoxazine moieties in order to thermally cure them without adding a second component, nor a catalyst or an initiator. These precursors were then blended with 1 weight percent of multi-walled carbon nanotubes (CNTs) using an ultrasound probe, which induced a transition from a liquid-like to a gel-like behavior as CNTs generate an interconnected network within the matrix. After curing, the resulting nanocomposites exhibit mechanical and electrical properties making them highly promising materials for pressure-sensing applications.

Recycling as a Key Enabler for Sustainable Additive Manufacturing of Polymer Composites: A Critical Perspective on Fused Filament Fabrication

Additive manufacturing (AM, aka 3D printing) is generally acknowledged as a "green" technology. However, its wider uptake in industry largely relies on the development of composite feedstock for imparting superior mechanical properties and bespoke functionality. Composite materials are especially needed in polymer AM, given the otherwise poor performance of most polymer parts in load-bearing applications. As a drawback, the shift from mono-material to composite feedstock may worsen the environmental footprint of polymer AM. This perspective aims to discuss this chasm between the advantage of embedding advanced functionality, and the disadvantage of causing harm to the environment. Fused filament fabrication (FFF, aka fused deposition modelling, FDM) is analysed here as a case study on account of its unparalleled popularity. FFF, which belongs to the material extrusion (MEX) family, is presently the most widespread polymer AM technique for industrial, educational, and recreational applications. On the one hand, the FFF of composite materials has already transitioned "from lab to fab" and finally to community, with far-reaching implications for its sustainability. On the other hand, feedstock materials for FFF are thermoplastic-based, and hence highly amenable to recycling. The literature shows that recycled thermoplastic materials such as poly(lactic acid) (PLA), acrylonitrile-butadiene-styrene (ABS), and polyethylene terephthalate (PET, or its glycol-modified form PETG) can be used for printing by FFF, and FFF printed objects can be recycled when they are at the end of life. Reinforcements/fillers can also be obtained from recycled materials, which may help valorise waste materials and by-products from a wide range of industries (for example, paper, food, furniture) and from agriculture. Increasing attention is being paid to the recovery of carbon fibres (for example, from aviation), and to the reuse of glass fibre-reinforced polymers (for example, from end-of-life wind turbines). Although technical challenges and economical constraints remain, the adoption of recycling strategies appears to be essential for limiting the environmental impact of composite feedstock in FFF by reducing the depletion of natural resources, cutting down the volume of waste materials, and mitigating the dependency on petrochemicals.

Heterogeneous Structure Omnidirectional Strain Sensor Arrays With Cognitively Learned Neural Networks

Mechanically stretchable strain sensors gain tremendous attention for bioinspired skin sensation systems and artificially intelligent tactile sensors. However, high-accuracy detection of both strain intensity and direction with simple device/array structures is still insufficient. To overcome this limitation, an omnidirectional strain perception platform utilizing a stretchable strain sensor array with triangular-sensor-assembly (three sensors tilted by 45°) coupled with machine learning (ML) -based neural network classification algorithm, is proposed. The strain sensor, which is constructed with strain-insensitive electrode regions and strain-sensitive channel region, can minimize the undesirable electrical intrusion from the electrodes by strain, leading to a heterogeneous surface structure for more reliable strain sensing characteristics. The strain sensor exhibits decent sensitivity with gauge factor (GF) of ≈8, a moderate sensing range (≈0-35%), and relatively good reliability (3000 stretching cycles). More importantly, by employing a multiclass-multioutput behavior-learned cognition algorithm, the stretchable sensor array with triangular-sensor-assembly exhibits highly accurate recognition of both direction and intensity of an arbitrary strain by interpretating the correlated signals from the three-unit sensors. The omnidirectional strain perception platform with its neural network algorithm exhibits overall strain intensity and direction accuracy around 98% ± 2% over a strain range of ≈0-30% in various surface stimuli environments.

Production of elastomer-based highly conductive hybrid nanocomposites and treatment with sulfuric acid

As an elastomer, poly(dimethylsiloxane) (PDMS) is used in various applications such as wearable technology and sealants, and is especially preferred in microelectromechanical device production due to its advantage in fabrication of microstructures. However, some of its applications such as sensor-based or electrode-based are limited due to its insulator aspect. Various conductive nanomaterials such as carbon nanotubes (CNTs), graphene, graphite, carbon black, and silver nanoparticles were incorporated into the PDMS matrix for the production of conductive nanocomposites. In this study, we produced highly conductive PDMS nanocomposites by addition of multiwalled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) in a three-dimensional network. Due to the synergistic effect between CNTs and GNPs inside a polymeric matrix, we expected to obtain PDMS nanocomposites more conductive than nanocomposites with only CNTs. Additionally, we investigated the effect of sulfuric acid treatment on the electrical conductivity and surface composition of prepared PDMS/MWCNT/GNP nanocomposites. Results indicated that the electrical conductivity in sulfuric acid-treated samples was significantly higher than in untreated samples. Levels of conductivity in the range of 270.7–1074.8 S/m were achieved; the higher ones were the samples treated with sulfuric acid solution.

Preparation of Boron Nitride and Silicone Rubber Composite Material for Application in Lithium Batteries

Hexagonal boron nitride and silicone rubber (h-BN/SR) composites were prepared by the mechanical stirring method, and their crystal morphology, chemical structure, thermal properties, and compression stress–strain performance were investigated. The experimental results suggest that silicone rubber combined with h-BN exhibits better thermal conductivity and mechanical properties. When the proportion of h-BN is 30 wt%, the thermal conductivity of the h-BN/SR composite material is 0.58 W/m∙K, which is 3.4 times that of pure silicone rubber. At the same time, the compressive strength of h-BN/SR is 4.27 MPa, which is 6.7 times that of pure silicone rubber. Furthermore, the finite element model was employed to numerically analyze the thermal behavior of a battery with a h-BN/SR composite as the thermal interface material. The analytical results show that the highest temperature of the battery decreased when using h-BN/SR as the thermal interface material in the battery thermal management system. The h-BN/SR composite can thus effectively improve the safety properties of batteries.

Thermo-Physical Characterization of Carbon Nanotube Composite Foam for Oil Recovery Applications

To meet the increasing demands for effective cleanup technologies to deal with the oil spill accidents that significantly affect the ecological and environmental systems, promising composite materials based on carbon nanotubes containing silicone foams were investigated. Pump oil, kerosene, and virgin naphtha had been used to assess, during sorption tests, foams behavior. Test results highlighted the advantage of the hydrophobic and oleophilic behavior of carbon nanotubes, and their high mechanical strength for oil spill recovery application was studied. In order to better relate the property-structure relationship for this class of materials, the role and influence of functionalized nanotubes on thermo-physical and morphological characteristics of the foams had been evaluated. The results showed how the pristine nanotubes fillers, despite functionalized ones, led to optimal composite foam performances with high hydrophobic (62 mg g⁻¹) and oleophilic (6830 mg g⁻¹ in kerosene oil) characteristics. The evidenced high oil selectivity was a relevant key point in order to consider the suitable material for oil spill recovery applications. Eventually, the proposed configuration exhibited the best thermo-physical performances and high reusability, leading to the optimal cost-benefits option.

Carbon Nanotubes Readily Disperse in Linear Silicones and Improve the Thermal Stability of Dimethylsilicone Elastomers

Stable silicone fluid-carbon nanotube dispersions were prepared in minutes by simple mixing processes, without the addition of solvents or surfactants and without the chemical modification of the nanotubes. With linear silicones of sufficient viscosity, a dual asymmetric centrifuge (SpeedMixer) was sufficient for dispersion; lower viscosity silicones required a brief ultrasound treatment. Optical microscopy indicates a homogeneous dispersion of multiwalled carbon nanotube (MWCNT) bundles in linear poly(dimethylsiloxane) (PDMS) oils. The facile dispersion of carbon nanotubes in PDMS has been reported in several previous publications and this appears to be general for silicones. MWCNTs also disperse readily, and to a greater extent, as assessed by optical microscopy, in poly(methylphenylsiloxane) and, in particular, poly(diethylsiloxane). Linear PDMS/MWCNT dispersions are stable against agglomeration for months. Platinum-catalyzed hydrosilylation of MWCNT-containing vinyl-/hydride-functionalized PDMS liquids yielded filled elastomers that unexpectedly exhibit significantly increased thermal stability. This enhancement occurs with only fractions of a weight percent of MWCNTs. Thermal gravimetric analysis shows a 54 °C increase in peak weight loss temperature (446-500 °C), an increased decomposition activation energy (158-233 kJ/mol), a second higher temperature decomposition process, and doubled char formation (20-40%) with only 0.5 wt %-added MWCNT. Pyrolysis combustion flow calorimetry confirmed the enhancement in thermal stability. Improvements in electrical conductivity were observed at loadings as low as 0.025 wt %. Spontaneous adsorption of dialkylsiloxane chains to MWCNT surfaces (wetting) and the resulting changes in the composite structure are implicated as the basis for dispersion and thermal behavior changes.

Low-cost sensor-integrated 3D-printed personalized prosthetic hands for children with amniotic band syndrome: A case study in sensing pressure distribution on an anatomical human-machine interface (AHMI) using 3D-printed conformal electrode arrays

Interfacing anatomically conformal electronic components, such as sensors, with biology is central to the creation of next-generation wearable systems for health care and human augmentation applications. Thus, there is a need to establish computer-aided design and manufacturing methods for producing personalized anatomically conformal systems, such as wearable devices and human-machine interfaces (HMIs). Here, we show that a three-dimensional (3D) scanning and 3D printing process enabled the design and fabrication of a sensor-integrated anatomical human-machine interface (AHMI) in the form of personalized prosthetic hands that contain anatomically conformal electrode arrays for children affected by amniotic band syndrome, a common birth defect. A methodology for identifying optimal scanning parameters was identified based on local and global metrics of registered point cloud data quality. This method identified an optimal rotational angle step size between adjacent 3D scans. The sensitivity of the optimization process to variations in organic shape (i.e., geometry) was examined by testing other anatomical structures, including a foot, an ear, and a porcine kidney. We found that personalization of the prosthetic interface increased the tissue-prosthesis contact area by 408% relative to the non-personalized devices. Conformal 3D printing of carbon nanotube-based polymer inks across the personalized AHMI facilitated the integration of electronic components, specifically, conformal sensor arrays for measuring the pressure distribution across the AHMI (i.e., the tissue-prosthesis interface). We found that the pressure across the AHMI exhibited a non-uniform distribution and became redistributed upon activation of the prosthetic hand's grasping action. Overall, this work shows that the integration of 3D scanning and 3D printing processes offers the ability to design and fabricate wearable systems that contain sensor-integrated AHMIs.

Thickness-Gradient Films for High Gauge Factor Stretchable Strain Sensors

High-gauge-factor stretchable strain sensors are developed by utilizing a new strategy of thickness-gradient films with high durability, and high uniaxial/isotropic stretchability based on the self-pinning effect of SWCNTs. The monitoring of detailed damping vibration modes driven by weak sound based on such sensors is demonstrated, making a solid step toward real applications.