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.

Manipulating connectivity and electrical conductivity in metallic nanowire networks.

Connectivity in metallic nanowire networks with resistive junctions is manipulated by applying an electric field to create materials with tunable electrical conductivity. In situ electron microscope and electrical measurements visualize the activation and evolution of connectivity within these networks. Modeling nanowire networks, having a distribution of junction breakdown voltages, reveals universal scaling behavior applicable to all network materials. We demonstrate how local connectivity within these networks can be programmed and discuss material and device applications.

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.

Nonlinear ion drift-diffusion memristance description of TiO2 RRAM devices.

The nature and direction of the hysteresis in memristive devices is critical to device operation and performance and the ability to realise their potential in neuromorphic applications. TiO2 is a prototypical memristive device material and is known to show hysteresis loops with both clockwise switching and counter-clockwise switching and in many instances evidence of negative differential resistance (NDR) behaviour. Here we study the electrical response of a device composed of a single nanowire channel Au-Ti/TiO2/Ti-Au both in air and under vacuum and simulate the I-V characteristics in each case using the Schottky barrier and an ohmic-like transport memristive model which capture nonlinear diffusion and migration of ions within the wire. The dynamics of this complex charge conduction phenomenon is obtained by fitting the nonlinear ion-drift equations with the experimental data. Our experimental results support a nonlinear drift of oxygen vacancies acting as shallow donors under vacuum conditions. Simulations show that dopant diffusion under bias creates a depletion region along the channel which results in NDR behaviour, but it is overcome at higher applied bias due to oxygen vacancy generation at the anode. The model allows the motion of the charged dopants to be visualised during device operation in air and under vacuum and predicts the elimination of the NDR under low bias operation, in agreement with experiments.

Time-resolved impurity-invisibility in graphene nanoribbons.

We investigate time-resolved charge transport through graphene nanoribbons supplemented with adsorbed impurity atoms. Depending on the location of the impurities with respect to the hexagonal carbon lattice, the transport properties of the system may become invisible to the impurity due to the symmetry properties of the binding mechanism. This motivates a chemical sensing device since dopants affecting the underlying sublattice symmetry of the pristine graphene nanoribbon introduce scattering. Using the time-dependent Landauer-Büttiker formalism, we extend the stationary current-voltage picture to the transient regime, where we observe how the impurity invisibility takes place at sub-picosecond time scales further motivating ultrafast sensor technology. We further characterize time-dependent local charge and current profiles within the nanoribbons, and we identify rearrangements of the current pathways through the nanoribbons due to the impurities. We finally study the behavior of the transients with ac driving which provides another way of identifying the lattice-symmetry breaking caused by the impurities.

The Electro-Optical Performance of Silver Nanowire Networks.

Networks of metallic nanowires have the potential to meet the needs of next-generation device technologies that require flexible transparent conductors. At present, there does not exist a first principles model capable of predicting the electro-optical performance of a nanowire network. Here we combine an electrical model derived from fundamental material properties and electrical equations with an optical model based on Mie theory scattering of light by small particles. This approach enables the generation of analogues for any nanowire network and then accurately predicts, without the use of fitting factors, the optical transmittance and sheet resistance of the transparent electrode. Predictions are validated using experimental data from the literature of networks comprised of a wide range of aspect ratios (nanowire length/diameter). The separation of the contributions of the material resistance and the junction resistance allows the effectiveness of post-deposition processing methods to be evaluated and provides a benchmark for the minimum attainable sheet resistance. The predictive power of this model enables a material-by-design approach, whereby suitable systems can be prescribed for targeted technology applications.

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.

Electronic density of states of helically-wrapped carbon nanotubes.

Motivated by evidences of helical wrapping of polymeric molecules around nanotubes, we perform a systematic study of how the electronic density of states of nanotubes is affected by the introduction of a coiling polarizing potential. The coiling perturbation, characterized by the wrapping angle, the polarizing width and the polarization strength, introduces an additional geometrical chirality that may not necessarily coincide with the intrinsic chiral angle of the nanotube. Features of the electronic density of states are shown to depend on this perturbation with different degrees of sensitivity. When correlated with the binding energy between the nanotube and the wrapping molecule, we find that noticeable changes in the density of states occur only for minimally bound structures.

The influence of Gaussian strain on sublattice selectivity of impurities in graphene.

Among the different strategies used to induce the opening of a band gap in graphene, one common practice is through chemical doping. While a gap may be opened in this way, disorder-induced scattering is an unwanted side-effect that impacts the electron mobility in the conductive regime of the system. However, this undesirable side effect is known to be minimised if dopants interact asymmetrically with the two sublattices of graphene. In this work we propose that mechanical strain can be used to introduce such a sublattice asymmetry in the doping process of graphene. We argue that a localised out-of-plane deformation applied to a graphene sheet can make one of the graphene sublattices more energetically favourable for impurity adsorption than the other and that this can be controlled by varying the strain parameters. Two complementary modelling schemes are used to describe the electronic structure of the flat and deformed graphene sheets: a tight-binding model and density functional theory. Our results indicate a novel way to select the doping process of graphene through strain engineering.

Sensitive electromechanical sensors using viscoelastic graphene-polymer nanocomposites.

Despite its widespread use in nanocomposites, the effect of embedding graphene in highly viscoelastic polymer matrices is not well understood. We added graphene to a lightly cross-linked polysilicone, often encountered as Silly Putty, changing its electromechanical properties substantially. The resulting nanocomposites display unusual electromechanical behavior, such as postdeformation temporal relaxation of electrical resistance and nonmonotonic changes in resistivity with strain. These phenomena are associated with the mobility of the nanosheets in the low-viscosity polymer matrix. By considering both the connectivity and mobility of the nanosheets, we developed a quantitative model that completely describes the electromechanical properties. These nanocomposites are sensitive electromechanical sensors with gauge factors >500 that can measure pulse, blood pressure, and even the impact associated with the footsteps of a small spider.

Resistance of Single Ag Nanowire Junctions and Their Role in the Conductivity of Nanowire Networks.

Networks of silver nanowires appear set to replace expensive indium tin oxide as the transparent conducting electrode material in next generation devices. The success of this approach depends on optimizing the material conductivity, which until now has largely focused on minimizing the junction resistance between wires. However, there have been no detailed reports on what the junction resistance is, nor is there a known benchmark for the minimum attainable sheet resistance of an optimized network. In this paper, we present junction resistance measurements of individual silver nanowire junctions, producing for the first time a distribution of junction resistance values and conclusively demonstrating that the junction contribution to the overall resistance can be reduced beyond that of the wires through standard processing techniques. We find that this distribution shows the presence of a small percentage (6%) of high-resistance junctions, and we show how these may impact the performance of network-based materials. Finally, through combining experiment with a rigorous model, we demonstrate the important role played by the network skeleton and the specific connectivity of the network in determining network performance.

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.

Amplitude coda of classical waves in disordered media.

The propagation of classical waves in the presence of a disordered medium is studied. We consider wave pulses containing a broad range of frequencies in terms of the configurationally averaged Green function of the system. Damped oscillations in the time-dependent response trailing behind the direct arrival of the pulse (coda) are predicted, the periods of which are governed by the density of scatterers.

Sublattice asymmetry of impurity doping in graphene: A review.

In this review we highlight recent theoretical and experimental work on sublattice asymmetric doping of impurities in graphene, with a focus on substitutional nitrogen dopants. It is well known that one current limitation of graphene in regards to its use in electronics is that in its ordinary state it exhibits no band gap. By doping one of its two sublattices preferentially it is possible to not only open such a gap, which can furthermore be tuned through control of the dopant concentration, but in theory produce quasi-ballistic transport of electrons in the undoped sublattice, both important qualities for any graphene device to be used competetively in future technology. We outline current experimental techniques for synthesis of such graphene monolayers and detail theoretical efforts to explain the mechanisms responsible for the effect, before suggesting future research directions in this nascent field.

Effective electrode length enhances electrical activation of nanowire networks: experiment and simulation.

Networks comprised of randomly oriented overlapping nanowires offer the possibility of simple fabrication on a variety of substrates, in contrast with the precise placement required for devices with single or aligned nanowires. Metal nanowires typically have a coating of surfactant or oxide that prevents aggregation, but also prevents electrical connection. Prohibitively high voltages can be required to electrically activate nanowire networks, and even after activation many nanowire junctions remain nonconducting. Nonelectrical activation methods can enhance conductivity but destroy the memristive behavior of the junctions that comprise the network. We show through both simulation and experiment that electrical stimulation, microstructured electrode geometry, and feature scaling can all be used to manipulate the connectivity and thus electrical conductivity of networks of silver nanowires with a nonconducting polymer coating. More generally, these results describe a strategy to integrate nanomaterials into controllable, adaptive macroscale materials.

Indirect magnetic coupling in light-element-doped single-walled carbon nanotubes.

Single-walled carbon nanotubes substitutionally doped with the light-element phosphorus are synthesized and are investigated by electrical and nuclear magnetic resonance measurements. Decreased spin-lattice relaxation times compared to undoped tubes point toward enhanced spin-sensitive scattering. Temperature dependence of the zero-bias conductance shows step-like features, a signature of scattering from a very low density (few sites per nanotube) of localized spin moments at oxidized phosphorus sites, consistent with density functional calculations. This supports recent predictions that localized magnetic moments must be indirectly magnetically coupled through the nanotube conduction electrons.

Graphene-metal interface: two-terminal resistance of low-mobility graphene in high magnetic fields.

The two-terminal magnetotransport of a single graphene layer was investigated up to a field of 55 T. The dependence of the electron transmission probability at the organo-metallic interface between the graphene and the metal electrodes was studied as a function of filling factor and electron density. A resistance-plateau spanning several tens of tesla width was observed. We argue that this plateau originates from an augmented sublattice spin-splitting due to the high surface-impurity concentration of the graphene layer. At electron densities close to the Dirac point, fingerprints of a thermally activated energy gap were observed.