Co-percolation to tune conductive behaviour in dynamical metallic nanowire networks

Tuning the electro-optical properties of nanowire networks

Conductive and transparent metallic nanowire networks are regarded as promising alternatives to Indium-Tin-Oxides (ITOs) in emerging flexible next-generation technologies due to their prominent optoelectronic properties and low-cost fabrication. The performance of such systems closely relies on many geometrical, physical, and intrinsic properties of the nanowire materials as well as the device-layout. A comprehensive computational study is essential to model and quantify the device's optical and electrical responses prior to fabrication. Here, we present a computational toolkit that exploits the electro-optical specifications of distinct device-layouts, namely standard random nanowire network and transparent mesh pattern structures. The target materials for transparent conducting electrodes of this study are aluminium, gold, copper, and silver nanowires. We have examined a variety of tunable parameters including network area fraction, length to diameter aspect ratio, and nanowires angular orientations under different device designs. Moreover, the optical extinction efficiency factors of each material are estimated by two approaches: Mie light scattering theory and finite element method (FEM) algorithm implemented in COMSOL®Multiphysics software. We studied various nanowire network structures and calculated their respective figures of merit (optical transmittance versus sheet resistance) from which insights on the design of next-generation transparent conductor devices can be inferred.

Emergence of winner-takes-all connectivity paths in random nanowire networks

Nanowire networks are promising memristive architectures for neuromorphic applications due to their connectivity and neurosynaptic-like behaviours. Here, we demonstrate a self-similar scaling of the conductance of networks and the junctions that comprise them. We show this behavior is an emergent property of any junction-dominated network. A particular class of junctions naturally leads to the emergence of conductance plateaus and a "winner-takes-all" conducting path that spans the entire network, and which we show corresponds to the lowest-energy connectivity path. The memory stored in the conductance state is distributed across the network but encoded in specific connectivity pathways, similar to that found in biological systems. These results are expected to have important implications for development of neuromorphic devices based on reservoir computing.

Oxide-mediated recovery of field-effect mobility in plasma-treated MoS2

Precise tunability of electronic properties of two-dimensional (2D) nanomaterials is a key goal of current research in this field of materials science. Chemical modification of layered transition metal dichalcogenides leads to the creation of heterostructures of low-dimensional variants of these materials. In particular, the effect of oxygen-containing plasma treatment on molybdenum disulfide (MoS₂) has long been thought to be detrimental to the electrical performance of the material. We show that the mobility and conductivity of MoS₂ can be precisely controlled and improved by systematic exposure to oxygen/argon plasma and characterize the material using advanced spectroscopy and microscopy. Through complementary theoretical modeling, which confirms conductivity enhancement, we infer the role of a transient 2D substoichiometric phase of molybdenum trioxide (2D-MoO _x ) in modulating the electronic behavior of the material. Deduction of the beneficial role of MoO _x will serve to open the field to new approaches with regard to the tunability of 2D semiconductors by their low-dimensional oxides in nano-modified heterostructures.