Nano-topography Enhances Communication in Neural Cells Networks

Abstract Neural cells are the smallest building blocks of the central and peripheral nervous systems. Information in neural networks and cell-substrate interactions have been heretofore studied separately. Understanding whether surface nano-topography can direct nerve cells assembly into computational efficient networks may provide new tools and criteria for tissue engineering and regenerative medicine. In this work, we used information theory approaches and functional multi calcium imaging (fMCI) techniques to examine how information flows in neural networks cultured on surfaces with controlled topography. We found that substrate roughness S a affects networks topology. In the low nano-meter range, S a  = 0–30 nm, information increases with S a . Moreover, we found that energy density of a network of cells correlates to the topology of that network. This reinforces the view that information, energy and surface nano-topography are tightly inter-connected and should not be neglected when studying cell-cell interaction in neural tissue repair and regeneration

Scalable, ultra-resistant structural colors based on network metamaterials

Structural colors have drawn wide attention for their potential as a future printing technology for various applications, ranging from biomimetic tissues to adaptive camouflage materials. However, an efficient approach to realize robust colors with a scalable fabrication technique is still lacking, hampering the realization of practical applications with this platform. Here, we develop a new approach based on large-scale network metamaterials that combine dealloyed subwavelength structures at the nanoscale with lossless, ultra-thin dielectric coatings. By using theory and experiments, we show how subwavelength dielectric coatings control a mechanism of resonant light coupling with epsilon-near-zero regions generated in the metallic network, generating the formation of saturated structural colors that cover a wide portion of the spectrum. Ellipsometry measurements support the efficient observation of these colors, even at angles of 70°. The network-like architecture of these nanomaterials allows for high mechanical resistance, which is quantified in a series of nano-scratch tests. With such remarkable properties, these metastructures represent a robust design technology for real-world, large-scale commercial applications

Nature Inspired Plasmonic Structures: Influence of Structural Characteristics on Sensing Capability

Surface enhanced Raman scattering (SERS) is a powerful analytical technique that allows the enhancement of a Raman signal in a molecule or molecular assemblies placed in the proximity of nanostructured metallic surfaces, due to plasmonic effects. However, laboratory methods to obtain of these prototypes are time-consuming, expensive and they do not always lead to the desired result. In this work, we analyse structures existing in nature that show, on a nanoscale, characteristic conformations of photonic crystals. We demonstrate that these structures, if covered with gold, change into plasmonic nanostructures and are able to sustain the SERS effect. We study three different structures with this property: opal, a hydrated amorphous form of silica (SiO2 center dot nH(2)O); diatoms, a kind of unicellular alga; and peacock tail feather. Rhodamine 6G (down to 10(-12) M) is used to evaluate their capability to increase the Raman signal. These results allow us to define an alternative way to obtain a high sensitivity in Raman spectroscopy, currently achieved by a long and expensive technique, and to fabricate inexpensive nanoplasmonic structures which could be integrated into optical sensors

Shaking the myosin family tree: Biochemical kinetics defines four types of myosin motor

Although all myosin motors follow the same basic cross-bridge cycle, they display a large variety in the rates of transition between different states in the cycle, allowing each myosin to be finely tuned for a specific task. Traditionally, myosins have been classified by sequence analysis into a large number of sub-families (?35). Here we use a different method to classify the myosin family members which is based on biochemical and mechanical properties. The key properties that define the type of mechanical activity of the motor are duty ratio (defined as the fraction of the time myosin remains attached to actin during each cycle), thermodynamic coupling of actin and nucleotide binding to myosin and the degree of strain-sensitivity of the ADP release step. Based on these properties we propose to classify myosins into four different groups: (I) fast movers, (II) slow/efficient force holders, (III) strain sensors and (IV) gates