New insights into ToF-SIMS imaging in osteoporotic bone research.

The present work focuses on the application of time-of-flight secondary ion mass spectrometry (ToF-SIMS) in osteoporotic bone research. In order to demonstrate the benefit, the authors present concrete application examples of ToF-SIMS in three different areas of bone research. ToF-SIMS as a mass spectrometric imaging technique allows simultaneous visualization of mineralized and nonmineralized bone tissue as well as implanted biomaterials and bone implant interphases. In the first example, the authors show that it is possible to study the incorporation and distribution of different components released from bone filler materials into bone with a single mass spectrometric measurement. This not only enables imaging of nonstained bone cross sections but also provides further insights beyond histologically obtained information. Furthermore, they successfully identified several mass fragments as markers for newly formed cartilage tissue and growth joint in bone. Different modes of ToF-SIMS as well as different SIMS instruments (IONTOF's TOF.SIMS 5 and M6 Hybrid SIMS, Ionoptika's J105) were used to identify these mass signals and highlight the high versatility of this method. In the third part, bone structure of cortical rat bone was investigated from bone sections embedded in technovit (polymethyl methacrylate, PMMA) and compared to cryosections. In cortical bone, they were able to image different morphological features, e.g., concentric arrangement of collagen fibers in so-called osteons as well as Haversian canals and osteocytes. In summary, the study provides examples of application and shows the strength of ToF-SIMS as a promising analytical method in the field of osteoporotic bone research.

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.

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.

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.

Poisson's ratio of individual metal nanowires.

The measurement of Poisson's ratio of nanomaterials is extremely challenging. Here we report a lateral atomic force microscope experimental method to electromechanically measure the Poisson's ratio and gauge factor of individual nanowires. Under elastic loading conditions we monitor the four-point resistance of individual metallic nanowires as a function of strain and different levels of electrical stress. We determine the gauge factor of individual wires and directly measure the Poisson's ratio using a model that is independently validated for macroscopic wires. For macroscopic wires and nickel nanowires we find Poisson's ratios that closely correspond to bulk values, whereas for silver nanowires significant deviations from the bulk silver value are observed. Moreover, repeated measurements on individual silver nanowires at different levels of mechanical and electrical stress yield a small spread in Poisson ratio, with a range of mean values for different wires, all of which are distinct from the bulk value.

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.