Room-Temperature-Processable Highly Reliable Resistive Switching Memory with Reconfigurability for Neuromorphic Computing and Ultrasonic Tissue Classification

Reversible metal-filamentary mechanism has been widely investigated to design an analog resistive switching memory (RSM) for neuromorphic hardware-implementation. However, uncontrollable filament-formation, inducing its reliability issues, has been a fundamental challenge. Here, an analog RSM with 3D ion transport channels that can provide unprecedentedly high reliability and robustness is demonstrated. This architecture is realized by a laser-assisted photo-thermochemical process, compatible with the back-end-of-line process and even applicable to a flexible format. These superior characteristics also lead to the proposal of a practical adaptive learning rule for hardware neural networks that can significantly simplify the voltage pulse application methodology even with high computing accuracy. A neural network, which can perform the biological tissue classification task using the ultrasound signals, is designed, and the simulation results confirm that this practical adaptive learning rule is efficient enough to classify these weak and complicated signals with high accuracy (97%). Furthermore, the proposed RSM can work as a diffusive-memristor at the opposite voltage polarity, exhibiting extremely stable threshold switching characteristics. In this mode, several crucial operations in biological nervous systems, such as Ca²⁺ dynamics and nonlinear integrate-and-fire functions of neurons, are successfully emulated. This reconfigurability is also exceedingly beneficial for decreasing the complexity of systems—requiring both drift- and diffusive-memristors.     

Metal Single-Site Molecular Complex–MXene Heteroelectrocatalysts Interspersed Graphene Nanonetwork for Efficient Dual-Task of Water Splitting and Metal–Air Batteries

Development of multifunctional electrocatalysts with high efficiency and stability is of great interest in recent energy conversion technologies. Herein, a novel heteroelectrocatalyst of molecular iron complex (Fe_MC)-carbide MXene (Mo₂TiC₂T_x) uniformly embedded in a 3D graphene-based hierarchical network (GrH) is rationally designed. The coexistence of Fe_MC and MXene with their unique interactions triggers optimum electronic properties, rich multiple active sites, and favorite free adsorption energy for excellent trifunctional catalytic activities. Meanwhile, the highly porous GrH effectively promotes a multichannel architecture for charge transfer and gas/ion diffusion to improve stability. Therefore, the Fe_MC–MXene/GrH results in superb performances towards oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) in alkaline medium. The practical tests indicate that Zn/Al–air batteries derived from Fe_MC–MXene/GrH cathodic electrodes produce high power densities of 165.6 and 172.7 mW cm⁻², respectively. Impressively, the liquid-state Zn–air battery delivers excellent cycling stability of over 1100 h. In addition, the alkaline water electrolyzer induces a low cell voltage of 1.55 V at 10 mA cm⁻² and 1.86 V at 0.4 A cm⁻² in 30 wt.% KOH at 80 °C, surpassing recent reports. The achievements suggest an exciting multifunctional electrocatalyst for electrochemical energy applications.     

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code (ECEMAC) has been used to aggregate the parameters generated from the wearable sensor devices of the patient. The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO. Aggregation scheme will reduce the number of transmissions over the network. The aggregated data are preprocessed at edge node to remove the noise for better diagnosis. Edge node will reduce the overhead of cloud server. The aggregated data are forward to cloud server for central storage and diagnosis. This proposed smart diagnosis will reduce the transmission cost through aggregation scheme which will reduce the energy of the system. Energy cost for proposed system for 300 nodes is 0.34μJ. Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme (SPPDA), concealed data aggregation scheme for multiple application (CDAMA) and secure aggregation scheme (ASAS) are 1.3 μJ, 0.81 μJ and 0.51 μJ respectively. The optimization approaches and encryption method will ensure the data privacy.     

Mechanical property enhancement of aligned multi-walled carbon nanotube sheets and composites through press-drawing process

A solid-state drawing and winding process was done to create thin aligned carbon nanotube (CNT) sheets from CNT arrays. However, waviness and poor packing of CNTs in the sheets are two main weaknesses restricting their reinforcing efficiency in composites. This report proposes a simple press-drawing technique to reduce wavy CNTs and to enhance dense packing of CNTs in the sheets. Non-pressed and pressed CNT/epoxy composites were developed using prepreg processing with a vacuum-assisted system. Effects of pressing on the mechanical properties of the aligned CNT sheets and CNT/epoxy composites were examined. Pressing with distributed loads of 147, 221, and 294 N/m showed a substantial increase in the tensile strength and the elastic modulus of the aligned CNT sheets and their composites. The CNT sheets under a press load of 221 N/m exhibited the best mechanical properties found in this study. With a press load of 221 N/m, the pressed CNT sheet and its composite, respectively, enhanced the tensile strength by 139.1 and 141.9%, and the elastic modulus by 489 and 77.6% when compared with non-pressed ones. The pressed CNT/epoxy composites achieved high tensile strength (526.2 MPa) and elastic modulus (100.2 GPa). Results show that press-drawing is an important step to produce superior CNT sheets for development of high-performance CNT composites.     

Study on the solidification cracking behaviour of high strength aluminum alloy welds: Effects of alloying elements and solidification behaviours

The solidification cracking susceptibility of the 7000 series Al-Zn-Mg high strength aluminum alloy has been studied. The cracking behaviour of the specimens were evaluated by a Tig-a-Ma-Jig Varestraint test process under various augmented strain conditions. It has been experimentally observed that the addition of copper decreased the solidification cracking resistivity of the high strength aluminum alloy weld metal by increasing the total crack length (TCL). The effect of the addition of manganese on the solidification cracking behaviour is found to be beneficial by markedly decreasing the solidification cracking susceptibility as the manganese content increases from 0.3 to 0.7%. This enhancement by manganese is understood to be attributed to the reduction of the mushy zone size during the solidification process. The effects of chromium and zirconium additions are also investigated. The weld metal containing zirconium is less sensitive to the solidification cracking than the weld metal containing chromium. In addition, the solidification behaviours of the tested alloys are also investigated and it is found that as the solidification temperature range (T) becomes narrow, the solidified structure becomes more dendritic in its features which is believed to create higher solidification cracking resistance.     

Size-dependent microparticles separation through standing surface acoustic waves

This study presents a method that uses a standing surface acoustic wave (SSAW) to continuously separate particles in a size-gradient manner in a microchannel flow. The proposed method was applied to a colloidal suspension containing poly dispersed particles with three different sizes (1, 5, and 10 μm) but the same density and compressibility. Particle suspension was focused hydrodynamically at an entrance region, and particles were forced actively toward the side wall where SSAW-pressure nodes were generated by two interdigital transducers (IDTs) across the channel. The particles placed in the middle stream, in which the shear rate was minimized, were separated successfully in a size-gradient manner by acoustic force. In addition, this study further developed an analytical model to predict the displacement of particles in microchannel flow by considering viscous, acoustic, and diffusive forces. The predicted values of particle displacement showed excellent agreement with the experimental results, and diffusion was found to be important and not negligible. The advantage of this method is to minimize the shear rate on particles, which would be useful for potential applications of shear-dependent cells such as platelets.     

Development of a framework for truss-braced wing conceptual MDO

The paper describes the development of a multidisciplinary design optimization framework for conceptual design of truss-braced wing configurations. This unconventional configuration requires specialized analysis tools supported by a modular and flexible framework to accommodate different configurations. While the previous framework developed at Virginia Tech was a monolithic Fortran-77 code, the need for more flexibility for complex truss-braced wing configurations was addressed by the development of this new framework, which is based on Phoenix Integration ModelCenter^TM environment. The framework uses updated structural and aerodynamic design modules that enable a more general geometry definition. The new framework, thus, provides a foundation for future design concepts, especially multi-member truss-braced wing configurations. The fuel saving potential of these truss-braced wing configurations is presented by comparing different truss designs with gradually increased level of complexity.     

A new vertical‐alignment LC mode with high‐transmittance and fast‐response‐time features

Abstract— A novel pixel design for vertical‐alignment LCDs with superior transmittance has been developed. The new liquid‐crystal mode, refered to as the hole‐induced vertical‐alignment mode (Hi‐VA), uses a via hole of an organic layer on a TFT substrate to achieve multi‐domain alignment. Compared to the conventional design, the Hi‐VA mode has a transmittance of up to 135% with a contrast ratio of 2000:1. Moreover, the new structure is free from ITO patterning or protrusion on the color‐filter side, which makes the fabrication process simple and low cost.     

Gas sensing properties of p-type hollow NiO hemispheres prepared by polymeric colloidal templating method

This work presents a simple and versatile route to produce macroporous p-type metal oxide thin films. Two-dimensional arrays of p-type NiO films with a hollow hemisphere structure were fabricated by colloidal templating and RF-sputtering followed by a subsequent heat treatment. The diameter and shell thickness of the NiO hemisphere were 800 nm and 20 nm, respectively. X-ray diffraction and high-resolution transmission electron microscopy analysis indicate that the pure NiO phase with grain size of 10 nm was obtained at calcination temperatures that exceeded 450 °C. Close-packed arrays of hollow NiO hemispheres were found to exhibit p-type gas sensing properties against (CO, H₂, C₃H₈, CH₄, NO₂, and C₂H₅OH), leading to significantly enhanced responses to C₂H₅OH (R_gas/R_air = 5.0 at 200 ppm).     

Autonomous topological modeling of a home environment and topological localization using a sonar grid map

This paper presents a method of autonomous topological modeling and localization in a home environment using only low-cost sonar sensors. The topological model is extracted from a grid map using cell decomposition and normalized graph cut. The autonomous topological modeling involves the incremental extraction of a subregion without predefining the number of subregions. A method of topological localization based on this topological model is proposed wherein a current local grid map is compared with the original grid map. The localization is accomplished by obtaining a node probability from a relative motion model and rotational invariant grid-map matching. The proposed method extracts a well-structured topological model of the environment, and the localization provides reliable node probability even when presented with sparse and uncertain sonar data. Experimental results demonstrate the performance of the proposed topological modeling and localization in a real home environment.