Current research on tactile sensors is mostly focused on the improvement of sensitivity and multi-functionality to emulate the function of natural skin. However, natural skin can sense external pressure and help form haptic memory, while current flexible tactile sensors for electronic skin can only perform sensing functions. This functionality gap between state-of-the-art tactile sensing devices and natural skin inspired a team of researchers to develop haptic memory devices that integrate sensor and memory functions.
Bubble-pen lithography (BPL) is a novel optically controlled nanofabrication technique that can be widely applied to pattern colloidal and biological particles on substrates in order to build functional optic, electronic, and magnetic devices. In BPL, an optically controlled microbubble is generated to capture and immobilize colloidal particles on the plasmonic substrates. With this new lithographic technique, the researchers can generate bubbles down to 1 micron in diameter. The smaller bubbles provide an enhanced patterning resolution.
Researchers have demonstrated a system that provides photo-triggered release of local anesthetics in a manner that could be adjusted by varying the irradiance and the duration of irradiation. From the clinical point of view, this is important in that it demonstrates a method by which patients would be able to take control of relatively local pain, being able to deliver local analgesia on demand, for the duration and with the intensity desired.
So far, most of the applications of plasmonic nanostructures rely on solid two-dimensional substrates such as silicon, glass, plastic, or paper. Such substrates offer rather limited accessible surface area, thus severely limiting the volumetric density of the nanostructures. Researchers now have developed a 3D material with a high density of plasmonic nanostructures that are completely accessible. The SERS and photothermal performance of this novel 3D material is superior compared to that of conventional 2D plasmonic surfaces.
One of the challenges of fabricating flexible electronics has been the trade-off between a material's high flexibility and adaptability, and its conductivity. Exploring feasible methods for guiding conducting or semiconducting nanomaterials into elastomeric matrices will be key to further progress in this area. A promising approach has just been reported by scientists, who have developed a facile printing strategy to assemble silver nanoparticles into micro- and nano-curve structures via a pillar-patterned silicon template. The curves with various tortuosity morphologies have differential resistive strain sensitivity, which can be integrated into a multi-analysis flexible sensor to perform complex-recognition of human facial expressions.
Researchers have approached the preparation of artificial analogs of nacre by using various methods and the resulting materials have captured some of the characteristics of the natural composite - but so far never have fully replicated it. Now, researchers have reported the first successful attempt to mimic the structure of nacre while maintaining the same characteristic geometry, aspect ratio and phase proportions. They used 10-20 nm thick layered double hydroxide (LDH) platelets with an aspect ratio similar to the aragonite platelets in nacre and 'glued' them together with a simple organic 'mortar' (PSS).
Researchers have successfully used amorphous metal tungsten nitride to demonstrate nanoelectromechanical switches that are capable of sub-1 volt operation. In the past, attaining sub-1 volt operation at dynamic state and faster switching was extremely difficult. These efforts required the use of very expensive lithography systems to pattern nanoscale free-hanging switches. This is the first ever demonstration of a 3-terminal NEM switch.
Applying multivariate statistical techniques to the study of nanocarbons, researchers have presented a methodology to identify nanoparticles with unique combinations of features and, in general, a feasible way of in silico characterization of intractable nanomaterial spaces. These analyses are based on structural features characterizing geometry, interatomic distances, bond angle, surface-to-volume ratio, carbon-to-hydrogen ratio, and hybridization fraction; many of which can be preselected without undertaking expensive electronic structure simulations.