Recommended implementation of electrical resistance tomography for conductivity mapping of metallic nanowire networks using voltage excitation

open6noThe knowledge of the spatial distribution of the electrical conductivity of metallic nanowire networks (NWN) is important for tailoring the performance in applications. This work focuses on Electrical Resistance Tomography (ERT), a technique that maps the electrical conductivity of a sample from several resistance measurements performed on its border. We show that ERT can be successfully employed for NWN characterisation if a dedicated measurement protocol is employed. When applied to other materials, ERT measurements are typically performed with a constant current excitation; we show that, because of the peculiar microscopic structure and behaviour of metallic NWN, a constant voltage excitation protocols is preferable. This protocol maximises the signal to noise ratio in the resistance measurements-and thus the accuracy of ERT maps-while preventing the onset of sample alterations.openCultrera, Alessandro; Milano, Gianluca; De Leo, Natascia; Ricciardi, Carlo; Boarino, Luca; Callegaro, LucaCultrera, Alessandro; Milano, Gianluca; De Leo, Natascia; Ricciardi, Carlo; Boarino, Luca; Callegaro, Luc

Mapping Time-Dependent Conductivity of Metallic Nanowire Networks by Electrical Resistance Tomography toward Transparent Conductive Materials

partially_open7Metallic nanowire (NW) networks have attracted great attention as promising transparent conductive materials thanks to the low sheet resistance, high transparency, low cost production, and compatibility with flexible substrates. Despite many efforts having been devoted to investigating the conduction mechanism, a quantitative characterization of local electrical properties of nanowire networks at the macroscale still represents a challenge. In this work, we report on the investigation of local electrical properties and their evolution over time of Ag NW networks by means of electrical resistance tomography (ERT). Spatial correlation of local conductivity properties and optical transparency revealed that the nonscanning and rapid ERT technique allows to probe local electrical inhomogeneities in the NW network, differently from conventional measurement techniques such as van der Pauw and the four-point probe. In addition, ERT mapping over time was employed for in situ monitoring the evolution of Ag NW networks conductivity, elucidating the dependence of the degradation of local electrical properties under ambient exposure on the initial conductivity. Our results shed light on the importance of the characterization of local electrical properties of NW networks where uniformity and stability represent the main challenges to overcome for their use as transparent conductive materials.openGianluca Milano; Alessandro Cultrera; Katarzyna Bejtka; Natascia De Leo; Luca Callegaro; Carlo Ricciardi; Luca BoarinoMilano, Gianluca; Cultrera, Alessandro; Bejtka, Katarzyna; DE LEO, Maria; Callegaro, Luca; Ricciardi, Carlo; Boarino, Luc

Memristive Devices for Quantum Metrology

As a consequence of the redefinition of the International System of Units (SI), where units are defined in terms of fundamental physical constants, memristive devices represent a promising platform for quantum metrology. Coupling ionics with electronics, memristive devices can exhibit conductance levels quantized in multiples of the fundamental quantum of conductance G(0) = 2e(2)/h. Since the fundamental quantum of conductance G(0) is related only to physical constants that assume fixed value in the revised SI, memristive devices can be exploited for the practical realization of a quantum-based resistance standard that, differently from quantum-Hall based devices conventionally adopted as resistance standards, can operate in different ambient conditions (air, vacuum, harsh environment), in a wide range of temperatures and without the need of an applied magnetic field In this work, the possibility of using memristive devices for quantum metrology is critically discussed, based on recent experimental and theoretical advances on quantum conductance phenomena reported in literature. Thanks to the high operational speed, high scalability down to the nanometer scale, and CMOS compatibility, memristive devices allow on-chip implementation of a resistance standard required for the realization of self-calibrating electrical systems and equipment with zero-chain traceability in accordance with the revised SI

Hyperbolic Metamaterials via Hierarchical Block Copolymer Nanostructures

Hyperbolic metamaterials (HMMs) offer unconventional properties in the field of optics, enabling opportunities for confinement and propagation of light at the nanoscale. In‐plane orientation of the optical axis, in the direction coinciding with the anisotropy of the HMMs, is desirable for a variety of novel applications in nanophotonics and imaging. Here, a method for creating localized HMMs with in‐plane optical axis, based on block copolymer (BCP) blend instability, is introduced. The dewetting of BCP thin film over topographically defined substrates generates droplets composed of highly ordered lamellar nanostructures in hierarchical configuration. The hierarchical nanostructures represent a valuable platform for the subsequent pattern transfer into a Au/air HMM, exhibiting hyperbolic behavior in a broad wavelength range in the visible spectrum. A computed Purcell factor as high as 32 at 580 nm supports the strong reduction in the fluorescence lifetime of defects in nanodiamonds placed on top of the HMM

Brain‐Inspired Structural Plasticity through Reweighting and Rewiring in Multi‐Terminal Self‐Organizing Memristive Nanowire Networks

open8sìActing as artificial synapses, two‐terminal memristive devices are considered fundamental building blocks for the realization of artificial neural networks. Current memristive crossbar architectures demonstrate the implementation of neuromorphic computing paradigms, although they are unable to emulate typical features of biological neural networks such as high connectivity, adaptability through reconnection and rewiring, and long‐range spatio‐temporal correlation. Herein, self‐organizing memristive random nanowire (NW) networks with functional connectivity able to display homo‐ and heterosynaptic plasticity is reported thanks to the mutual electrochemical interaction among memristive NWs and NW junctions. In particular, it is shown that rewiring and reweighting effects observed in single NWs and single NW junctions, respectively, are responsible for structural plasticity of the network under electrical stimulation. Such biologically inspired systems allow a low‐cost realization of neural networks that can learn and adapt when subjected to multiple external stimuli, emulating the experience‐dependent synaptic plasticity that shape the connectivity and functionalities of the nervous system that can be exploited for hardware implementation of unconventional computing paradigms.openGianluca Milano; Giacomo Pedretti; Matteo Fretto; Luca Boarino; Fabio Benfenati; Daniele Ielmini; Ilia Valov; Carlo RicciardiMilano, Gianluca; Pedretti, Giacomo; Fretto, Matteo; Boarino, Luca; Benfenati, Fabio; Ielmini, Daniele; Valov, Ilia; Ricciardi, Carl

Brain‐Inspired Structural Plasticity through Reweighting and Rewiring in Multi‐Terminal Self‐Organizing Memristive Nanowire Networks

Acting as artificial synapses, two‐terminal memristive devices are considered fundamental building blocks for the realization of artificial neural networks. Current memristive crossbar architectures demonstrate the implementation of neuromorphic computing paradigms, although they are unable to emulate typical features of biological neural networks such as high connectivity, adaptability through reconnection and rewiring, and long‐range spatio‐temporal correlation. Herein, self‐organizing memristive random nanowire (NW) networks with functional connectivity able to display homo‐ and heterosynaptic plasticity is reported thanks to the mutual electrochemical interaction among memristive NWs and NW junctions. In particular, it is shown that rewiring and reweighting effects observed in single NWs and single NW junctions, respectively, are responsible for structural plasticity of the network under electrical stimulation. Such biologically inspired systems allow a low‐cost realization of neural networks that can learn and adapt when subjected to multiple external stimuli, emulating the experience‐dependent synaptic plasticity that shape the connectivity and functionalities of the nervous system that can be exploited for hardware implementation of unconventional computing paradigms

Monolithic Cells for Solar Fuels.

A tutorial review explaining the many processes occurring in photoelectrochemical cells for solar fuel production, and prospects for future developments