Thin, sensitive skin electronics: The properties of the human sense of touch, including high sensitivity to differences in temperature, pressure, or surface roughness, are challenging to replicate in robotics because skin materials must be highly conductive, stretchable, and thin. Jung et al . developed a process to assemble nanomaterials as a monolayer that is partially embedded in an ultrathin elastomer. The process works by depositing a mixed solvent containing nanostructured silver and/or gold, along with elastomer, onto deionized water. This results in a layer of nanoparticles residing at the interface coating with elastomer, which is further densified by the addition of surfactant. The process is scalable, and the resulting elastomer membranes can be transferred to other substrates. —MSL

A tissue-like skin-device interface was prepared by using ultrathin functionalized hydrogels for wearable bioelectronics.

Wearable electronic devices are used to perform various electronic functions on the human skin, and their mechanical softness while maintaining high performances is critical. Therefore, there is a need to develop novel materials with outstanding softness and high electrical and ionic conductivity for wearable electronics. Here, we present an intrinsically stretchable and conductive nanocomposite based on alginate hydrogels and silver nanowires (AgNWs). The developed nanocomposite was applied to highly conductive soft electrodes that can be used in various wearable electronic devices. The nanocomposite electrode was prepared by cross-linking alginate molecules in the presence of AgNWs, exhibiting higher electrical, ionic conductivity, higher stretchability, and lower modulus than conventional conducting rubbers. By forming a bilayer structure with the nanocomposite and the ultrasoft hydrogel layer, the mechanical properties of the nanocomposite device could be matched to that of the human skin. We used the nanocomposite electrode for fabricating key device components of wearable electronics, such as a wearable antenna and a skin-mountable supercapacitor. Such demonstrations successfully proved the effectiveness of the proposed nanocomposite as a soft conducting material for wearable electronics.

To develop highly sensitive flexible pressure sensors, the mechanical and piezoresistive properties of conductive thermoplastic materials produced via additive manufacturing technology were investigated. Multi-walled carbon nanotubes (MWCNTs) dispersed in thermoplastic polyurethane (TPU), which is flexible and pliable, were used to form filaments. Specimens of the MWCNT/TPU composite with various MWCNT concentrations were printed using fused deposition modelling. Uniaxial tensile tests were conducted, while the mechanical and piezoresistive properties of the MWCNT/TPU composites were measured. To predict the piezoresistive behaviour of the composites, a microscale 3D resistance network model was developed. In addition, a continuum piezoresistive model was proposed for large-scale simulations.

Advanced high-strength steels (AHSSs) exhibit large, and sometimes anisotropic, springback recovery after forming. Accurate description of the anisotropic elasto-plastic behaviour of sheet metals is critical for predicting their anisotropic springback behaviour. For some materials, the initial anisotropy is maintained while hardening progresses. However, for other materials, anisotropy changes with hardening. In this work, to account for the evolution of anisotropy of a dual-phase steel, an elastoplastic material constitutive model is developed. In particular, the combined isotropic–kinematic hardening model was modified. Tensile loading–unloading, uniaxial and biaxial tension, and tension–compression tests were conducted along the rolling, diagonal, and transverse directions to measure the anisotropic properties, and the parameters of the proposed constitutive model were determined. For validation, the proposed model was applied to a U-bending process, and the measured springback angles were compared to the predicted ones.

To improve the formability in the deep drawing of tailor-welded blanks, an adjustable drawbead was introduced. Drawbead movement was obtained using the multi-objective optimization of the conflicting objective functions of the fracture and centerline deviation simultaneously. Finite element simulations of the deep drawing processes were conducted to generate observations for optimization. The response surface method and artificial neural network were used to determine the relationship between variables and objective functions; the procedure was applied to a circular cup drawing of the tailor-welded dual-phase steel blank. The results showed that the artificial neural network had better prediction capability and accuracy than the response surface method. Additionally, the non-dominated sorting-based genetic algorithm (NSGA-II) could effectively determine the optima. The adjustable drawbead with the optimized movement was confirmed as an efficient and effective solution for improving the formability of the deep drawing of tailor-welded blanks.