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.

Stretching the Equilibrium Limit of Sn in Ge1-x Sn x Nanowires: Implications for Field Effect Transistors.

Ge1-x Sn x nanowires incorporating a large amount of Sn would be useful for mobility enhancement in nanoelectronic devices, a definitive transition to a direct bandgap for application in optoelectronic devices and to increase the efficiency of the GeSn-based photonic devices. Here we report the catalytic bottom-up fabrication of Ge1-x Sn x nanowires with very high Sn incorporation (x > 0.3). These nanowires are grown in supercritical toluene under high pressure (21 MPa). The introduction of high pressure in the vapor-liquid-solid (VLS) like growth regime resulted in a substantial increase of Sn incorporation in the nanowires, with a Sn content ranging between 10 and 35 atom %. The incorporation of Sn in the nanowires was found to be inversely related to nanowire diameter; a high Sn content of 35 atom % was achieved in very thin Ge1-x Sn x nanowires with diameters close to 20 nm. Sn was found to be homogeneously distributed throughout the body of the nanowires, without apparent clustering or segregation. The large inclusion of Sn in the nanowires could be attributed to the nanowire growth kinetics and small nanowire diameters, resulting in increased solubility of Sn in Ge at the metastable liquid-solid interface under high pressure. Electrical investigation of the Ge1-x Sn x (x = 0.10) nanowires synthesized by the supercritical fluid approach revealed their potential in nanoelectronics and sensor-based applications.

Antimicrobial TiO2 nanocomposite coatings for surfaces, dental and orthopaedic implants.

Engineering of self-disinfecting surfaces to constrain the spread of SARS-CoV-2 is a challenging task for the scientific community because the human coronavirus spreads through respiratory droplets. Titania (TiO2) nanocomposite antimicrobial coatings is one of the ideal remedies to disinfect pathogens (virus, bacteria, fungi) from common surfaces under light illumination. The photocatalytic disinfection efficiency of recent TiO2 nanocomposite antimicrobial coatings for surfaces, dental and orthopaedic implants are emphasized in this review. Mostly, inorganic metals (e.g. copper (Cu), silver (Ag), manganese (Mn), etc), non-metals (e.g. fluorine (F), calcium (Ca), phosphorus (P)) and two-dimensional materials (e.g. MXenes, MOF, graphdiyne) were incorporated with TiO2 to regulate the charge transfer mechanism, surface porosity, crystallinity, and the microbial disinfection efficiency. The antimicrobial activity of TiO2 coatings was evaluated against the most crucial pathogenic microbes such as Escherichia coli, methicillin-resistant Staphylococcus aureus, Pseudomonas aeruginosa, Bacillus subtilis, Legionella pneumophila, Staphylococcus aureus, Streptococcus mutans, T2 bacteriophage, H1N1, HCoV-NL63, vesicular stomatitis virus, bovine coronavirus. Silane functionalizing agents and polymers were used to coat the titanium (Ti) metal implants to introduce superhydrophobic features to avoid microbial adhesion. TiO2 nanocomposite coatings in dental and orthopaedic metal implants disclosed exceptional bio-corrosion resistance, durability, biocompatibility, bone-formation capability, and long-term antimicrobial efficiency. Moreover, the commercial trend, techno-economics, challenges, and prospects of antimicrobial nanocomposite coatings are also discussed briefly.

Emulating synaptic response in n- and p-channel MoS2 transistors by utilizing charge trapping dynamics.

Brain-inspired, neuromorphic computing aims to address the growing computational complexity and power consumption in modern von-Neumann architectures. Progress in this area has been hindered due to the lack of hardware elements that can mimic neuronal/synaptic behavior which form the fundamental building blocks for spiking neural networks (SNNs). In this work, we leverage the short/long term memory effects due to the electron trapping events in an atomically thin channel transistor that mimic the exchange of neurotransmitters and emulate a synaptic response. Re-doped (n-type) and Nb-doped (p-type) molybdenum di-sulfide (MoS2) field-effect transistors are examined using pulsed-gate measurements, which identify the time scales of electron trapping/de-trapping. The devices demonstrate promising trends for short/long term plasticity in the order of ms/minutes, respectively. Interestingly, pulse paired facilitation (PPF), which quantifies the short-term plasticity, reveal time constants (τ1 = 27.4 ms, τ2 = 725 ms) that closely match those from a biological synapse. Potentiation and depression measurements describe the ability of the synaptic device to traverse several analog states, where at least 50 conductance values are accessed using consecutive pulses of equal height and width. Finally, we demonstrate devices, which can emulate a well-known learning rule, spike time-dependent plasticity (STDP) which codifies the temporal sequence of pre- and post-synaptic neuronal firing into corresponding synaptic weights. These synaptic devices present significant advantages over iontronic counterparts and are envisioned to create new directions in the development of hardware for neuromorphic computing.

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.

Synthesis of centimeter-size free-standing perovskite nanosheets from single-crystal lead bromide for optoelectronic devices.

Considerable attention has been drawn to the lead halide perovskites (LHPs) because of their outstanding optoelectronic characteristics. LHP nanosheets (NSs) grown from single crystalline lead halide possess advantages in device applications as they provide the possibility for control over morphology, composition, and crystallinity. Here, free-standing lead bromide (PbBr2) single-crystalline NSs with sizes up to one centimeter are synthesized from solution. These NSs can be converted to LHP while maintaining the NS morphology. We demonstrate that these perovskite NSs can be processed directly for fabrication of photodetector and laser arrays on a large scale. This strategy will allow high-yield synthesis of large-size perovskite NSs for functional devices in an integrated photonics platform.

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.

Nonpolar Resistive Switching in Ag@TiO2 Core-Shell Nanowires.

Nonpolar resistive switching (RS), a combination of bipolar and unipolar RS, is demonstrated for the first time in a single nanowire (NW) system. Exploiting Ag@TiO2 core-shell (CS) NWs synthesized by postgrowth shell formation, the switching mode is controlled by adjusting the current compliance effectively, tailoring the electrical polarity response. We demonstrate ON/OFF ratios of 105 and 107 for bipolar and unipolar modes, respectively. In the bipolar regime, retention times could be controlled up to 103 s, and in the unipolar mode, >106 s was recorded. We show how the unique dual-mode switching behavior is enabled by the defect-rich polycrystalline material structure of the TiO2 shell and the interaction between the Ag core and the Ag electrodes. These results provide a foundation for engineering nonpolar RS behaviors for memory storage and neuromorphic applications in CSNW structures.

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.