Effective medium theory for the conductivity of disordered metallic nanowire networks.

Motivated by numerous technological applications, there is current interest in the study of the conductive properties of networks made of randomly dispersed nanowires. The sheet resistance of such networks is normally calculated by numerically evaluating the conductance of a system of resistors but due to disorder and with so many variables to account for, calculations of this type are computationally demanding and may lack mathematical transparency. Here we establish the equivalence between the sheet resistance of disordered networks and that of a regular ordered network. Rather than through a fitting scheme, we provide a recipe to find the effective medium network that captures how the resistance of a nanowire network depends on several different parameters such as wire density, electrode size and electrode separation. Furthermore, the effective medium approach provides a simple way to distinguish the sheet resistance contribution of the junctions from that of the nanowires themselves. The contrast between these two contributions determines the potential to optimize the network performance for a particular application.

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.

Ultimate conductivity performance in metallic nanowire networks.

In this work, we introduce a combined experimental and computational approach to describe the conductivity of metallic nanowire networks. Due to their highly disordered nature, these materials are typically described by simplified models in which network junctions control the overall conductivity. Here, we introduce a combined experimental and simulation approach that involves a wire-by-wire junction-by-junction simulation of an actual network. Rather than dealing with computer-generated networks, we use a computational approach that captures the precise spatial distribution of wires from an SEM analysis of a real network. In this way, we fully account for all geometric aspects of the network, i.e. for the properties of the junctions and wire segments. Our model predicts characteristic junction resistances that are smaller than those found by earlier simplified models. The model outputs characteristic values that depend on the detailed connectivity of the network, which can be used to compare the performance of different networks and to predict the optimum performance of any network and its scope for improvement.