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.

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.

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.

Curvature in graphene nanoribbons generates temporally and spatially focused electric currents.

Today graphene nanoribbons and other graphene-based nanostructures can be synthesized with atomic precision. But while investigations have concentrated on straight graphene ribbons of fixed crystal orientation, ribbons with intrinsic curvature have remained mainly unexplored. Here, we investigate electronic transport in intrinsically curved graphene nanoribbons coupled to straight leads using two computational approaches. Stationary approach shows how transport gaps are affected both by the straight leads and by the degree of edge asymmetry in the curved ribbons. An advanced time-dependent approach shows that behind the façade of calm stationary transport the currents run violently: curvature triggers temporally and spatially focused electric currents, to the extent that for short durations single carbon-carbon bonds carry currents far exceeding the steady-state currents in the entire ribbons. Recognizing this focusing is pivotal for a robust design of graphene sensors and circuitries.

Optical and electronic properties of graphene nanoribbons upon adsorption of ligand-protected aluminum clusters.

We have carried out first-principles calculations to investigate how the electronic and optical features of graphene nanoribbons are affected by the presence of atomic clusters. Aluminum clusters of different sizes and stabilized by organic ligands were deposited on graphene nanoribbons from which the energetic features of the adsorption plus electronic structure were treated within density-functional theory. Our results point out that, depending on their size and structure shape, the clusters perturb distinctively the electronic properties of the ribbons. We suggest that such selective response can be measured through optical means revealing that graphene nanoribbons can work as an efficient characterization medium of atomic clusters. In addition, we demonstrate that atomic clusters can fine-tune the electronic and spin-polarized states of graphene ribbons from which novel spin-filter devices could be designed.

Dynamic and electronic transport properties of DNA translocation through graphene nanopores.

Graphene layers have been targeted in the last years as excellent host materials for sensing a remarkable variety of gases and molecules. Such sensing abilities can also benefit other important scientific fields such as medicine and biology. This has automatically led scientists to probe graphene as a potential platform for sequencing DNA strands. In this work, we use robust numerical tools to model the dynamic and electronic properties of molecular sensor devices composed of a graphene nanopore through which DNA molecules are driven by external electric fields. We performed molecular dynamic simulations to determine the relation between the intensity of the electric field and the translocation time spent by the DNA to pass through the pore. Our results reveal that one can have extra control on the DNA passage when four additional graphene layers are deposited on the top of the main graphene platform containing the pore in a 2 × 2 grid arrangement. In addition to the dynamic analysis, we carried electronic transport calculations on realistic pore structures with diameters reaching nanometer scales. The transmission obtained along the graphene sensor at the Fermi level is affected by the presence of the DNA. However, it is rather hard to distinguish the respective nucleobases. This scenario can be significantly altered when the transport is conducted away from the Fermi level of the graphene platform. Under an energy shift, we observed that the graphene pore manifests selectiveness toward DNA nucleobases.

Nanoscale ear drum: graphene based nanoscale sensors.

The difficulty in determining the mass of a sample increases as its size diminishes. At the nanoscale, there are no direct methods for resolving the mass of single molecules or nanoparticles and so more sophisticated approaches based on electromechanical phenomena are required. More importantly, one demands that such nanoelectromechanical techniques could provide not only information about the mass of the target molecules but also about their geometrical properties. In this sense, we report a theoretical study that illustrates in detail how graphene membranes can operate as nanoelectromechanical mass-sensor devices. Wide graphene sheets were exposed to different types and amounts of molecules and molecular dynamic simulations were employed to treat these doping processes statistically. We demonstrate that the mass variation effect and information about the graphene-molecule interactions can be inferred through dynamical response functions. Our results confirm the potential use of graphene as a mass detector device with remarkable precision in estimating variations in mass at the molecular scale and other physical properties of the dopants.