RESUMEN
Networks of nanowires, nanotubes, and nanosheets are important for many applications in printed electronics. However, the network conductivity and mobility are usually limited by the resistance between the particles, often referred to as the junction resistance. Minimising the junction resistance has proven to be challenging, partly because it is difficult to measure. Here, we develop a simple model for electrical conduction in networks of 1D or 2D nanomaterials that allows us to extract junction and nanoparticle resistances from particle-size-dependent DC network resistivity data. We find junction resistances in porous networks to scale with nanoparticle resistivity and vary from 5 Ω for silver nanosheets to 24 GΩ for WS2 nanosheets. Moreover, our model allows junction and nanoparticle resistances to be obtained simultaneously from AC impedance spectra of semiconducting nanosheet networks. Through our model, we use the impedance data to directly link the high mobility of aligned networks of electrochemically exfoliated MoS2 nanosheets (≈ 7 cm2 V-1 s-1) to low junction resistances of â¼2.3 MΩ. Temperature-dependent impedance measurements also allow us to comprehensively investigate transport mechanisms within the network and quantitatively differentiate intra-nanosheet phonon-limited bandlike transport from inter-nanosheet hopping.
RESUMEN
The investigation of high-mobility two-dimensional (2D) flakes beyond molybdenum disulfide (MoS2) will be necessary to create a library of high-mobility solution-processed networks that conform to substrates and remain functional over thousands of bending cycles. Here we report electrochemical exfoliation of large-aspect-ratio (>100) semiconducting flakes of tungsten diselenide (WSe2) and tungsten disulfide (WS2) as well as MoS2 as a comparison. We use Langmuir-Schaefer coating to achieve highly aligned and conformal flake networks, with minimal mesoporosity (â¼2-5%), at low processing temperatures (120 °C) and without acid treatments. This allows us to fabricate electrochemical transistors in ambient air, achieving average mobilities of µMoS2 ≈ 11 cm2 V-1 s-1, µWS2 ≈ 9 cm2 V-1 s-1, and µWSe2 ≈ 2 cm2 V-1 s-1 with a current on/off ratios of Ion/Ioff ≈ 2.6 × 103, 3.4 × 103, and 4.2 × 104 for MoS2, WS2, and WSe2, respectively. Moreover, our transistors display threshold voltages near â¼0.4 V with subthreshold slopes as low as 182 mV/dec, which are essential factors in maintaining power efficiency and represent a 1 order of magnitude improvement in the state of the art. Furthermore, the performance of our WSe2 transistors is maintained on polyethylene terephthalate (PET) even after 1000 bending cycles at 1% strain.
RESUMEN
Although printed networks of semiconducting nanosheets have found success in a range of applications, conductive nanosheet networks are limited by low conductivities (<106 S m-1 ). Here, dispersions of silver nanosheets (AgNS) that can be printed into highly conductive networks are described. Using a commercial thermal inkjet printer, AgNS patterns with unannealed conductivities of up to (6.0 ± 1.1) × 106 S m-1 are printed. These networks can form electromagnetic interference shields with record shielding effectiveness of >60 dB in the microwave region at thicknesses <200 nm. High resolution patterns with line widths down to 10 µm are also printed using an aerosol-jet printer which, when annealed at 200 °C, display conductivity >107 S m-1 . Unlike conventional Ag-nanoparticle inks, the 2D geometry of AgNS yields smooth, short-free interfaces between electrode and active layer when used as the top electrode in vertical nanosheet heterostructures. This shows that all-printed vertical heterostructures of AgNS/WS2 /AgNS, where the top electrode is a mesh grid, function as photodetectors demonstrating that such structures can be used in optoelectronic applications that usually require transparent conductors.
RESUMEN
Printed strain sensors will be important in applications such as wearable devices, which monitor breathing and heart function. Such sensors need to combine high sensitivity and low resistance with other factors such as cyclability, low hysteresis, and minimal frequency/strain-rate dependence. Although nanocomposite sensors can display a high gauge factor (G), they often perform poorly in the other areas. Recently, evidence has been growing that printed, polymer-free networks of nanoparticles, such as graphene nanosheets, display very good all-round sensing performance, although the details of the sensing mechanism are poorly understood. Here, we perform a detailed characterization of the thickness dependence of piezoresistive sensors based on printed networks of graphene nanosheets. We find both conductivity and gauge factor to display percolative behavior at low network thickness but bulk-like behavior for networks above â¼100 nm thick. We use percolation theory to derive an equation for gauge factor as a function of network thickness, which well-describes the observed thickness dependence, including the divergence in gauge factor as the percolation threshold is approached. Our analysis shows that the dominant contributor to the sensor performance is not the effect of strain on internanosheet junctions but the strain-induced modification of the network structure. Finally, we find these networks display excellent cyclability, hysteresis, and frequency/strain-rate dependence as well as gauge factors as high as 350.
RESUMEN
Conductive nanocomposites are often piezoresistive, displaying significant changes in resistance upon deformation, making them ideal for use as strain and pressure sensors. Such composites typically consist of ductile polymers filled with conductive nanomaterials, such as graphene nanosheets or carbon nanotubes, and can display sensitivities, or gauge factors, which are much higher than those of traditional metal strain gauges. However, their development has been hampered by the absence of physical models that could be used to fit data or to optimize sensor performance. Here we develop a simple model which results in equations for nanocomposite gauge factors as a function of both filler volume fraction and composite conductivity. These equations can be used to fit experimental data, outputting figures of merit, or predict experimental data once certain physical parameters are known. We have found these equations to match experimental data, both measured here and extracted from the literature, extremely well. Importantly, the model shows the response of composite strain sensors to be more complex than previously thought and shows factors other than the effect of strain on the interparticle resistance to be performance limiting.
RESUMEN
Nanocomposite strain sensors, particularly those consisting of polymer-graphene composites, are increasingly common and are of great interest in the area of wearable sensors. In such sensors, application of strain yields an increase in resistance due to the effect of deformation on interparticle junctions. Typically, widening of interparticle separation is thought to increase the junction resistance by reducing the probability of tunnelling between conducting particles. However, an alternative approach would be to use piezoresistive fillers, where an applied strain modifies the intrinsic filler resistance and so the overall composite resistance. Such an approach would broaden sensing capabilities, as using negative piezoresistive fillers could yield strain-induced resistance reductions rather than the usual resistance increases. Here, we introduce nanocomposites based on polyethylene oxide (PEO) filled with MoS2 nanosheets. Doping of the MoS2 by the PEO yields nanocomposites which are conductive enough to act as sensors, while efficient stress transfer leads to nanosheet deformation in response to an external strain. The intrinsic negative piezoresistance of the MoS2 leads to a reduction of the composite resistance on the application of small tensile strains. However, at higher strain the resistance grows due to increases in junction resistance. MoS2-PEO composite gauge factors are approximately -25 but fall to -12 for WS2-PEO composites and roughly -2 for PEO filled with MoSe2 or WSe2. We develop a simple model, which describes all these observations. Finally, we show that these composites can be used as dynamic strain sensors.
RESUMEN
Mixed networks of conducting and nonconducting nanoparticles show promise in a range of applications where fast charge transport is important. While the dependence of network conductivity on the loading level of conductive additive is well understood, little is known about the loading dependence of mobility and carrier density. This is particularly important as the addition of graphene might lead to increases in the mobility of semiconducting nanosheet network transistors. Here, we use electrolytic gating to investigate the transport properties of spray-coated composite networks of graphene and WS2 nanosheets. As the graphene loading is increased, we find that both conductivity and carrier density increase in line with the percolation theory with percolation thresholds (â¼8 vol %) and exponents (â¼2.5) consistent with previous reporting. Perhaps surprisingly, we find the mobility increases modestly from â¼0.1 cm2/V s (for a WS2 network) to â¼0.3 cm2/V s (for a graphene network) which we attribute to the similarity between WS2-WS2 and graphene-graphene junction resistances. In addition, we find both the transistor on- and off-currents to scale with loading according to the percolation theory, changing sharply at the percolation threshold. Through fitting, we show that only the current in the WS2 network changes significantly upon gating. As a result, the on-off ratio falls sharply at the percolation threshold from â¼104 to â¼2 at higher loading. Reflecting on these results, we conclude that the addition of graphene to a semiconducting network is not a viable strategy to improve transistor performance as it reduces the on:off ratio far more than it improves the mobility.