Incorporation of Carbon Nanofillers Tunes Mechanical and Electrical Percolation in PHBV:PLA Blends

Biobased fillers, such as bio-derived cellulose, lignin byproducts, and biochar, can be used to modify the thermal, mechanical, and electrical properties of polymer composites. Biochar (BioC), in particular, is of interest for enhancing thermal and electrical conductivities in composites, and can potentially serve as a bio-derived graphitic carbon alternative for certain composite applications. In this work, we investigate a blended biopolymer system: poly(lactic acid) (PLA)/poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV), and addition of carbon black (CB), a commonly used functional filler as a comparison for Kraft lignin-derived BioC. We present calculations and experimental results for phase-separation and nanofiller phase affinity in this system, indicating that the CB localizes in the PHBV phase of the immiscible PHBV:PLA blends. The addition of BioC led to a deleterious reaction with the biopolymers, as indicated by blend morphology, differential scanning calorimetry showing significant melting peak reduction for the PLA phase, and a reduction in melt viscosity. For the CB nanofilled composites, electrical conductivity and dynamic mechanical analysis supported the ability to use phase separation in these blends to tune the percolation of mechanical and electrical properties, with a minimum percolation threshold found for the 80:20 blends of 1.6 wt.% CB. At 2% BioC (approximately the percolation threshold for CB), the 80:20 BioC nanocomposites had a resistance of 3.43 × 10 8 Ω as compared to 2.99 × 10 8 Ω for the CB, indicating that BioC could potentially perform comparably to CB as a conductive nanofiller if the processing challenges can be overcome for higher BioC loadings.

Predictive Model for the Electrical Transport within Nanowire Networks

Thin networks of high aspect ratio conductive nanowires can combine high electrical conductivity with excellent optical transparency, which has led to a widespread use of nanowires in transparent electrodes, transistors, sensors, and flexible and stretchable conductors. Although the material and application aspects of conductive nanowire films have been thoroughly explored, there is still no model which can relate fundamental physical quantities, like wire resistance, contact resistance, and nanowire density, to the sheet resistance of the film. Here, we derive an analytical model for the electrical conduction within nanowire networks based on an analysis of the parallel resistor network. The model captures the transport characteristics and fits a wide range of experimental data, allowing for the determination of physical parameters and performance-limiting factors, in sharp contrast to the commonly employed percolation theory. The model thus constitutes a useful tool with predictive power for the evaluation and optimization of nanowire networks in various applications.

Statistical physics perspective of fracture in brittle and quasi-brittle materials

We discuss the physics of fracture in terms of the statistical physics associated with the failure of elastic media under applied stresses in presence of quenched disorder. We show that the development and the propagation of fracture are largely determined by the strength of the disorder and the stress field around them. Disorder acts as nucleation centres for fracture. We discuss Griffith's law for a single crack-like defect as a source for fracture nucleation and subsequently consider two situations: (i) low disorder concentration of the defects, where the failure is determined by the extreme value statistics of the most vulnerable defect (nucleation regime) and (ii) high disorder concentration of the defects, where the scaling theory near percolation transition is applicable. In this regime, the development of fracture takes place through avalanches of a large number of tiny microfractures with universal statistical features. We discuss the transition from brittle to quasi-brittle behaviour of fracture with the strength of disorder in the mean-field fibre bundle model. We also discuss how the nucleation or percolation mode of growth of fracture depends on the stress distribution range around a defect. We discuss the corresponding numerical simulation results on random resistor and spring networks.This article is part of the theme issue 'Statistical physics of fracture and earthquakes'.

Fire spread and the issue of community-level selection in the evolution of flammability

Whether plants can evolve to promote flammability is controversial. Ecologically, fire only spreads in landscapes when many plants are flammable, but collective behaviours among large groups are difficult to evolve at the individual level. Here, we formulate a model that examines how flammability can spread from rarity, combining individual-level costs and payoffs of flammability with landscape-level fire spread, sufficiently generic to analogize flammability among grasses, Mediterranean systems, and others. We found that fire-prone and fire-suppressing landscapes, composed of flammable and non-flammable plants, respectively, were alternatively stable in some environments, and flammability therefore only increased from rarity in environments when fire-proneness was the only stable state. Thus, fire-vegetation feedbacks alone probably did not drive the evolution and spread of flammability. However, evolution of flammability did promote fire-proneness in temporally and spatially heterogeneous environments: when flammable plants already occupied some substantial fraction of a fire-prone landscape, a positive feedback with fire could maintain flammability in a decreasingly favourable environment, and fire feedbacks could expand the distribution of flammability traits from fire-prone into fire-suppressing areas in a heterogeneous landscape. Thus, fire feedbacks could potentially have promoted the widespread invasion and persistence of flammability traits to their current widespread prominence.

Enhancing High-Frequency Dielectric Properties of Beta-SiC Filled Nanocomposites from Synergy between Percolation and Polarization

Promising comprehensive properties, including high permittivity, low dielectric loss, high breakdown strength, low electrical conductivity, and high thermal conductivity, are very hard to simultaneously obtain in high-frequency applicable polymer nanocomposite dielectrics. Instead of traditional electric percolation, in this work, a novel route based on a synergy between electric percolation and induced polarization has been raised to prepare 0⁻3 type nanocomposites with an enhanced high permittivity (high-k) property and low loss at high frequency. This work aimed at optimizing that synergy to achieve the favorable properties mentioned above in composite dielectrics used at high frequencies such as 1 MHz and 1 GHz. Conductive beta-SiC nanoparticles with a particle size of ~30 nm were employed as filler and both insulating poly(vinyl alcohol) and polyvinyl chloride were employed as polymer matrices to construct two composite systems. Utilizing polyvinyl chloride rather than poly(vinyl alcohol) realizes higher comprehensive electrical properties in composites, ascribed to optimization of that synergy. The optimization was achieved based on a combination of mild induced polarization and polarization-assisted electric percolation. Therefore, this work might open the way for large-scale production of high-frequency applicable composite dielectrics with competitive comprehensive electrical properties.

Signal Percolation within a Bacterial Community

Signal transmission among cells enables long-range coordination in biological systems. However, the scarcity of quantitative measurements hinders the development of theories that relate signal propagation to cellular heterogeneity and spatial organization. We address this problem in a bacterial community that employs electrochemical cell-to-cell communication. We developed a model based on percolation theory, which describes how signals propagate through a heterogeneous medium. Our model predicts that signal transmission becomes possible when the community is organized near a critical phase transition between a disconnected and a fully connected conduit of signaling cells. By measuring population-level signal transmission with single-cell resolution in wild-type and genetically modified communities, we confirm that the spatial distribution of signaling cells is organized at the predicted phase transition. Our findings suggest that at this critical point, the population-level benefit of signal transmission outweighs the single-cell level cost. The bacterial community thus appears to be organized according to a theoretically predicted spatial heterogeneity that promotes efficient signal transmission.

Resilience of networks with community structure behaves as if under an external field

Although detecting and characterizing community structure is key in the study of networked systems, we still do not understand how community structure affects systemic resilience and stability. We use percolation theory to develop a framework for studying the resilience of networks with a community structure. We find both analytically and numerically that interlinks (the connections among communities) affect the percolation phase transition in a way similar to an external field in a ferromagnetic- paramagnetic spin system. We also study universality class by defining the analogous critical exponents δ and γ, and we find that their values in various models and in real-world coauthor networks follow the fundamental scaling relations found in physical phase transitions. The methodology and results presented here facilitate the study of network resilience and also provide a way to understand phase transitions under external fields.

Local structure can identify and quantify influential global spreaders in large scale social networks

Measuring and optimizing the influence of nodes in big-data online social networks are important for many practical applications, such as the viral marketing and the adoption of new products. As the viral spreading on a social network is a global process, it is commonly believed that measuring the influence of nodes inevitably requires the knowledge of the entire network. Using percolation theory, we show that the spreading process displays a nucleation behavior: Once a piece of information spreads from the seeds to more than a small characteristic number of nodes, it reaches a point of no return and will quickly reach the percolation cluster, regardless of the entire network structure; otherwise the spreading will be contained locally. Thus, we find that, without the knowledge of the entire network, any node's global influence can be accurately measured using this characteristic number, which is independent of the network size. This motivates an efficient algorithm with constant time complexity on the long-standing problem of best seed spreaders selection, with performance remarkably close to the true optimum.

How Low Nucleation Density of Graphene on CuNi Alloy is Achieved

CuNi alloy foils are demonstrated to be one of the best substrates for synthesizing large area single-crystalline graphene because a very fast growth rate and low nucleation density can be simultaneously achieved. The fast growth rate is understood to be due the abundance of carbon precursor supply, as a result of the high catalytic activity of Ni atoms. However, a theoretical understanding of the low nucleation density remains controversial because it is known that a high carbon precursor concentration on the surface normally leads to a high nucleation density. Here, the graphene nucleation on the CuNi alloy surfaces is systematically explored and it is revealed that: i) carbon atom dissolution into the CuNi alloy passivates the alloy surface, thereby drastically increasing the graphene nucleation barrier; ii) carbon atom diffusion on the CuNi alloy surface is greatly suppressed by the inhomogeneous atomic structure of the surface; and iii) a prominent increase in the rate of carbon diffusion into the bulk occurs when the Ni composition is higher than the percolation threshold. This study reveals the key mechanism for graphene nucleation on CuNi alloy surfaces and provides a guideline for the catalyst design for the synthesis of graphene and other 2D materials.

Electrical Mapping of Silver Nanowire Networks: A Versatile Tool for Imaging Network Homogeneity and Degradation Dynamics during Failure

Electrical stability and homogeneity of silver nanowire (AgNW) networks are critical assets for increasing their robustness and reliability when integrated as transparent electrodes in devices. Our ability to distinguish defects, inhomogeneities, or inactive areas at the scale of the entire network is therefore a critical issue. We propose one-probe electrical mapping (1P-mapping) as a specific simple tool to study the electrical distribution in these discrete structures. 1P-mapping has allowed us to show that the tortuosity of the voltage equipotential lines of AgNW networks under bias decreases with increasing network density, leading to a better electrical homogeneity. The impact of the network fabrication technique on the electrical homogeneity of the resulting electrode has also been investigated. Then, by combining 1P-mapping with electrical resistance measurements and IR thermography, we propose a comprehensive analysis of the evolution of the electrical distribution in AgNW networks when subjected to increasing voltage stresses. We show that AgNW networks experience three distinctive stages: optimization, degradation, and breakdown. We also demonstrate that the failure dynamics of AgNW networks at high voltages occurs through a highly correlated and spatially localized mechanism. In particular the in situ formation of cracks could be clearly visualized. It consists of two steps: creation of a crack followed by propagation nearly parallel to the equipotential lines. Finally, we show that current can dynamically redistribute during failure, by following partially damaged secondary pathways through the crack.

Charge Transfer and Collection in Dilute Organic Donor-Acceptor Heterojunction Blends

Experimental and theoretical approaches are used to understand the role of nanomorphology on exciton dissociation and charge collection at dilute donor-acceptor (D-A) organic heterojunctions (HJs). Specifically, two charge transfer (CT) states in D-A mixed HJs comprising nanocrystalline domains of tetraphenyldibenzoperiflanthene (DBP) as the donor and C₇₀ as the acceptor are unambiguously related to the nanomorphology of the mixed layer. Alternating DBP:C₇₀ multilayer stacks are used to identify and control the optical properties of the CT states, as well as to simulate the dilute mixed heterojunctions. A kinetic Monte Carlo model along with photoluminescence spectroscopy and scanning transmission electron microscopy are used to quantitatively evaluate the layer morphology under various growth conditions. As a result, we are able to understand the counterintuitive observation of high charge extraction efficiency and device performance of DBP:C₇₀ mixed layer photovoltaics at surprisingly low (∼10%) donor concentrations.

Effects of Carbon Nanotubes/Graphene Nanoplatelets Hybrid Systems on the Structure and Properties of Polyetherimide-Based Foams

Foams based on polyetherimide (PEI) with carbon nanotubes (CNT) and PEI with graphene nanoplatelets (GnP) combined with CNT were prepared by water vapor induced phase separation. Prior to foaming, variable amounts of only CNT (0.1–2.0 wt %) or a combination of GnP (0.0–2.0 wt %) and CNT (0.0–2.0 wt %) for a total amount of CNT-GnP of 2.0 wt %, were dispersed in a solvent using high power sonication, added to the PEI solution, and intensively mixed. While the addition of increasingly higher amounts of only CNT led to foams with more heterogeneous cellular structures, the incorporation of GnP resulted in foams with finer and more homogeneous cellular structures. GnP in combination with CNT effectively enhanced the thermal stability of foams by delaying thermal decomposition and mechanically-reinforced PEI. The addition of 1.0 wt % GnP in combination with 1.0 wt % CNT resulted in foams with extremely high electrical conductivity, which was related to the formation of an optimum conductive network by physical contact between GnP layers and CNT, enabling their use in electrostatic discharge (ESD) and electromagnetic interference (EMI) shielding applications. The experimental electrical conductivity values of foams containing only CNT fitted well to a percolative conduction model, with a percolation threshold of 0.06 vol % (0.1 wt %) CNT.

Graph analysis of cell clusters forming vascular networks

This manuscript describes the experimental observation of vasculogenesis in chick embryos by means of network analysis. The formation of the vascular network was observed in the area opaca of embryos from 40 to 55 h of development. In the area opaca endothelial cell clusters self-organize as a primitive and approximately regular network of capillaries. The process was observed by bright-field microscopy in control embryos and in embryos treated with Bevacizumab (Avastin^®), an antibody that inhibits the signalling of the vascular endothelial growth factor (VEGF). The sequence of images of the vascular growth were thresholded, and used to quantify the forming network in control and Avastin-treated embryos. This characterization is made by measuring vessels density, number of cell clusters and the largest cluster density. From the original images, the topology of the vascular network was extracted and characterized by means of the usual network metrics such as: the degree distribution, average clustering coefficient, average short path length and assortativity, among others. This analysis allows to monitor how the largest connected cluster of the vascular network evolves in time and provides with quantitative evidence of the disruptive effects that Avastin has on the tree structure of vascular networks.

A 3D Nanostructured Hydrogel-Framework-Derived High-Performance Composite Polymer Lithium-Ion Electrolyte

Solid-state electrolytes have emerged as a promising alternative to existing liquid electrolytes for next generation Li-ion batteries for better safety and stability. Of various types of solid electrolytes, composite polymer electrolytes exhibit acceptable Li-ion conductivity due to the interaction between nanofillers and polymer. Nevertheless, the agglomeration of nanofillers at high concentration has been a major obstacle for improving Li-ion conductivity. In this study, we designed a three-dimensional (3D) nanostructured hydrogel-derived Li_0.35 La_0.55 TiO₃ (LLTO) framework, which was used as a 3D nanofiller for high-performance composite polymer Li-ion electrolyte. The systematic percolation study revealed that the pre-percolating structure of LLTO framework improved Li-ion conductivity to 8.8×10^-5  S cm^-1 at room temperature.

Percolative core formation in planetesimals enabled by hysteresis in metal connectivity

The segregation of dense core-forming melts by porous flow is a natural mechanism for core formation in early planetesimals. However, experimental observations show that texturally equilibrated metallic melt does not wet the silicate grain boundaries and tends to reside in isolated pockets that prevent percolation. Here we use pore-scale simulations to determine the minimum melt fraction required to induce porous flow, the percolation threshold. The composition of terrestrial planets suggests that typical planetesimals contain enough metal to overcome this threshold. Nevertheless, it is currently thought that melt segregation is prevented by a pinch-off at melt fractions slightly below the percolation threshold. In contrast to previous work, our simulations on irregular grain geometries reveal that a texturally equilibrated melt network remains connected down to melt fractions of only 1 to 2%. This hysteresis in melt connectivity allows percolative core formation in planetesimals that contain enough metal to exceed the percolation threshold. Evidence for the percolation of metallic melt is provided by X-ray microtomography of primitive achondrite Northwest Africa (NWA) 2993. Microstructural analysis shows that the metal-silicate interface has characteristics expected for a texturally equilibrated pore network with a dihedral angle of ∼85°. The melt network therefore remained close to textural equilibrium despite a complex history. This suggests that the hysteresis in melt connectivity is a viable process for percolative core formation in the parent bodies of primitive achondrites.

Fabrication of Graphene-Polyimide Nanocomposites with Superior Electrical Conductivity

We report on the fabrication of a novel class of lightweight materials, polyimide-graphene nanocomposites (0.01-5 vol %), with tunable electrical conductivity. The graphene-polyimide nanocomposites exhibit an ultra-low graphene percolation threshold of 0.03 vol % and maximum dc conductivity of 0.94 S/cm, which we attribute to excellent dispersion, extraordinary electron transport in the well-dispersed graphene, high number density of graphene nanosheets, and the π-π interactions between the aromatic moieties of the polyimide and the carbon rings in graphene. The dc conductivity data are shown to follow the power-law dependence on the graphene volume fraction near the percolation threshold. The ac conductivity of the nanocomposites is accurately represented by the extended pair-approximation model. The exponent s of the approximation is estimated to be 0.45-0.61, indicating anomalous diffusion of charge particles and a fractal structure for the conducting phase, lending support to the percolation model. Low-temperature dc conductivity of the nanocomposites is well-approximated by the thermal fluctuation-induced tunneling. Wide-angle X-ray scattering and transmission electron microscopy were utilized to correlate the morphology with the electrical conductivity. The lack of maxima in X-ray indicates the loss of structural registry and short-range ordering.

Ultralow Percolation Threshold in Nanoconfined Domains

Self-assembled percolated networks play an important role in many advanced electronic materials and devices. In nanocarbon composites, decreasing the percolation threshold ϕ_c is of paramount importance to reduce nanotube bundling, minimize material resources and costs, and enhance charge transport. Here we demonstrate that three-dimensional nanoconfinement in single-wall carbon nanotube/polymer nanocomposites produces a strong reduction in ϕ_c, reaching the lowest value ever reported in this system of ϕ_c ≈ 1.8 × 10^-5 wt % and 4-5 orders of magnitude lower than the theoretical statistical percolation threshold ϕ_stat. Moreover, a change in network resistivity and electrical conduction was observed with increased confinement, and a simple resistive model is used to accurately estimate the difference in ϕ_c in the confined networks. These results are explained in terms of networks' size, confinement, and tube orientation as determined by atomic force microscopy, electrical conductivity measurements, and polarized Raman spectroscopy. Our findings provide important insight into nanoscale percolated networks and should find application in electronic nanocomposites and devices.

Electric-Field-Tunable Conductivity in Graphene/Water and Graphene/Ice Systems

This study demonstrates that the application of an external electrical potential to a phenyl-sulfonic functionalized graphene (SG)/water suspension distinctly enhances its electrical conductivity via the structural transition from isolated clusters to a 3D SG network. Microstructural and alternating current impedance spectroscopy studies indicate that the surface charge plays an important role in the state of dispersion and connectivity of the SG in the suspension due to the potential-dependent interactions with functional groups on the SG surface in the presence of an external electrical potential. In addition, the conductive SG/ice can be produced via liquid-solid phase transition of the SG/water suspension in the presence of an external electrical potential, which shows a one-order magnitude improvement in electrical conductivity compared with pure ice. The electric-field-tunable property advances the understanding of nanofluid systems and has many potential applications.

Transparent, Flexible, and Conductive 2D Titanium Carbide (MXene) Films with High Volumetric Capacitance

2D transition-metal carbides and nitrides, known as MXenes, have displayed promising properties in numerous applications, such as energy storage, electromagnetic interference shielding, and catalysis. Titanium carbide MXene (Ti₃ C₂ T_x ), in particular, has shown significant energy-storage capability. However, previously, only micrometer-thick, nontransparent films were studied. Here, highly transparent and conductive Ti₃ C₂ T_x films and their application as transparent, solid-state supercapacitors are reported. Transparent films are fabricated via spin-casting of Ti₃ C₂ T_x nanosheet colloidal solutions, followed by vacuum annealing at 200 °C. Films with transmittance of 93% (≈4 nm) and 29% (≈88 nm) demonstrate DC conductivity of ≈5736 and ≈9880 S cm^-1 , respectively. Such highly transparent, conductive Ti₃ C₂ T_x films display impressive volumetric capacitance (676 F cm^-3 ) combined with fast response. Transparent solid-state, asymmetric supercapacitors (72% transmittance) based on Ti₃ C₂ T_x and single-walled carbon nanotube (SWCNT) films are also fabricated. These electrodes exhibit high capacitance (1.6 mF cm^-2 ) and energy density (0.05 µW h cm^-2 ), and long lifetime (no capacitance decay over 20 000 cycles), exceeding that of graphene or SWCNT-based transparent supercapacitor devices. Collectively, the Ti₃ C₂ T_x films are among the state-of-the-art for future transparent, conductive, capacitive electrodes, and translate into technologically viable devices for next-generation wearable, portable electronics.

Asymmetric percolation drives a double transition in sexual contact networks

Zika virus (ZIKV) exhibits unique transmission dynamics in that it is concurrently spread by a mosquito vector and through sexual contact. Due to the highly asymmetric durations of infectiousness between males and females-it is estimated that males are infectious for periods up to 10 times longer than females-we show that this sexual component of ZIKV transmission behaves akin to an asymmetric percolation process on the network of sexual contacts. We exactly solve the properties of this asymmetric percolation on random sexual contact networks and show that this process exhibits two epidemic transitions corresponding to a core-periphery structure. This structure is not present in the underlying contact networks, which are not distinguishable from random networks, and emerges because of the asymmetric percolation. We provide an exact analytical description of this double transition and discuss the implications of our results in the context of ZIKV epidemics. Most importantly, our study suggests a bias in our current ZIKV surveillance, because the community most at risk is also one of the least likely to get tested.

Graph Analysis and Modularity of Brain Functional Connectivity Networks: Searching for the Optimal Threshold

Neuroimaging data can be represented as networks of nodes and edges that capture the topological organization of the brain connectivity. Graph theory provides a general and powerful framework to study these networks and their structure at various scales. By way of example, community detection methods have been widely applied to investigate the modular structure of many natural networks, including brain functional connectivity networks. Sparsification procedures are often applied to remove the weakest edges, which are the most affected by experimental noise, and to reduce the density of the graph, thus making it theoretically and computationally more tractable. However, weak links may also contain significant structural information, and procedures to identify the optimal tradeoff are the subject of active research. Here, we explore the use of percolation analysis, a method grounded in statistical physics, to identify the optimal sparsification threshold for community detection in brain connectivity networks. By using synthetic networks endowed with a ground-truth modular structure and realistic topological features typical of human brain functional connectivity networks, we show that percolation analysis can be applied to identify the optimal sparsification threshold that maximizes information on the networks' community structure. We validate this approach using three different community detection methods widely applied to the analysis of brain connectivity networks: Newman's modularity, InfoMap and Asymptotical Surprise. Importantly, we test the effects of noise and data variability, which are critical factors to determine the optimal threshold. This data-driven method should prove particularly useful in the analysis of the community structure of brain networks in populations characterized by different connectivity strengths, such as patients and controls.

Social network fragmentation and community health

Community health interventions often seek to intentionally destroy paths between individuals to prevent the spread of infectious diseases. Immunizing individuals through direct vaccination or the provision of health education prevents pathogen transmission and the propagation of misinformation concerning medical treatments. However, it remains an open question whether network-based strategies should be used in place of conventional field approaches to target individuals for medical treatment in low-income countries. We collected complete friendship and health advice networks in 17 rural villages of Mayuge District, Uganda. Here we show that acquaintance algorithms, i.e., selecting neighbors of randomly selected nodes, were systematically more efficient in fragmenting all networks than targeting well-established community roles, i.e., health workers, village government members, and schoolteachers. Additionally, community roles were not good proxy indicators of physical proximity to other households or connections to many sick people. We also show that acquaintance algorithms were effective in offsetting potential noncompliance with deworming treatments for 16,357 individuals during mass drug administration (MDA). Health advice networks were destroyed more easily than friendship networks. Only an average of 32% of nodes were removed from health advice networks to reduce the percentage of nodes at risk for refusing treatment in MDA to below 25%. Treatment compliance of at least 75% is needed in MDA to control human morbidity attributable to parasitic worms and progress toward elimination. Our findings point toward the potential use of network-based approaches as an alternative to role-based strategies for targeting individuals in rural health interventions.

Influences of Thermal Treatment on the Dielectric Performances of Polystyrene Composites Reinforced by Graphene Nanoplatelets

Dielectric properties of composites near percolation threshold (f_c) are often sensitive to thermal treatments, and the annealing temperature is usually associated with a polymer’s rheological properties. In this study, the influences of the thermal treatment on dielectric properties are investigated for the polystyrene (PS) matrix composite reinforced by graphene nanoplatelets (GNP) fillers near f_c. It can be found that the thermal treatment can not only increase the dielectric constant, but also decrease the dielectric loss for the PS/GNP composite. This interesting phenomenon possibly happens in the interfacial region of PS/GNP with the thickness about 4–6 nm according to the electron energy-loss spectroscopy (EELS) results. The free volumes around the interface can be easily altered by the movement of polymeric segments after annealing at the glass transition temperature.

Welding of 3D-printed carbon nanotube-polymer composites by locally induced microwave heating

Additive manufacturing through material extrusion, often termed three-dimensional (3D) printing, is a burgeoning method for manufacturing thermoplastic components. However, a key obstacle facing 3D-printed plastic parts in engineering applications is the weak weld between successive filament traces, which often leads to delamination and mechanical failure. This is the chief obstacle to the use of thermoplastic additive manufacturing. We report a novel concept for welding 3D-printed thermoplastic interfaces using intense localized heating of carbon nanotubes (CNTs) by microwave irradiation. The microwave heating of the CNT-polymer composites is a function of CNT percolation, as shown through in situ infrared imaging and simulation. We apply CNT-loaded coatings to a 3D printer filament; after printing, microwave irradiation is shown to improve the weld fracture strength by 275%. These remarkable results open up entirely new design spaces for additive manufacturing and also yield new insight into the coupling between dielectric properties and radio frequency field response for nanomaterial networks.

Transparent Electrodes Based on Silver Nanowire Networks: From Physical Considerations towards Device Integration

The past few years have seen a considerable amount of research devoted to nanostructured transparent conducting materials (TCM), which play a pivotal role in many modern devices such as solar cells, flexible light-emitting devices, touch screens, electromagnetic devices, and flexible transparent thin film heaters. Currently, the most commonly used TCM for such applications (ITO: Indium Tin oxide) suffers from two major drawbacks: brittleness and indium scarcity. Among emerging transparent electrodes, silver nanowire (AgNW) networks appear to be a promising substitute to ITO since such electrically percolating networks exhibit excellent properties with sheet resistance lower than 10 Ω/sq and optical transparency of 90%, fulfilling the requirements of most applications. In addition, AgNW networks also exhibit very good mechanical flexibility. The fabrication of these electrodes involves low-temperature processing steps and scalable methods, thus making them appropriate for future use as low-cost transparent electrodes in flexible electronic devices. This contribution aims to briefly present the main properties of AgNW based transparent electrodes as well as some considerations relating to their efficient integration in devices. The influence of network density, nanowire sizes, and post treatments on the properties of AgNW networks will also be evaluated. In addition to a general overview of AgNW networks, we focus on two important aspects: (i) network instabilities as well as an efficient Atomic Layer Deposition (ALD) coating which clearly enhances AgNW network stability and (ii) modelling to better understand the physical properties of these networks.