Approximate probabilistic optimization using exact-capacity-approximate-response-distribution (ECARD)

Probabilistic structural design deals with uncertainties in response (e.g. stresses) and capacity (e.g. failure stresses). The calculation of the structural response is typically expensive (e.g., finite element simulations), while the capacity is usually available from tests. Furthermore, the random variables that influence response and capacity are often disjoint. In previous work we have shown that this disjoint property can be used to reduce the cost of obtaining the probability of failure via Monte Carlo simulations. In this paper we propose to use this property for an approximate probabilistic optimization based on exact capacity and approximate response distributions (ECARD). In Approximate Probabilistic Optimization Using ECARD, the change in response distribution is approximated as the structure is re-designed while the capacity distribution is kept exact, thus significantly reducing the number of expensive response simulations. ECARD may be viewed as an extension of SORA (Sequential Optimization and Reliability Assessment), which proceeds with deterministic optimization iterations. In contrast, ECARD has probabilistic optimization iterations, but in each iteration, the response distribution is approximated so as not to require additional response calculations. The use of inexpensive probabilistic optimization allows easy incorporation of system reliability constraints and optimal allocation of risk between failure modes. The method is demonstrated using a beam problem and a ten-bar truss problem. The former allocates risk between two different failure modes, while the latter allocates risk between members. It is shown that ECARD provides most of the improvement from risk re-allocation that can be obtained from full probabilistic optimization.     

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.     

Chain Vacancies in 2D Crystals

Defects in bulk crystals can be classified into vacancies, interstitials, grain boundaries, stacking faults, dislocations, and so forth. In particular, the vacancy in semiconductors is a primary defect that governs electrical transport. Concentration of vacancies depends mainly on the growth conditions. Individual vacancies instead of aggregated vacancies are usually energetically more favorable at room temperature because of the entropy contribution. This phenomenon is not guaranteed in van der Waals 2D materials due to the reduced dimensionality (reduced entropy). Here, it is reported that the 1D connected/aggregated vacancies are energetically stable at room temperature. Transmission electron microscopy observations demonstrate the preferential alignment direction of the vacancy chains varies in different 2D crystals: MoS₂ and WS₂ prefer direction, while MoTe₂ prefers direction. This difference is mainly caused by the different strain effect near the chalcogen vacancies. Black phosphorous also exhibits directional double‐chain vacancies along 〈01〉 direction. Density functional theory calculations predict that the chain vacancies act as extended gap (conductive) states. The observation of the chain vacancies in 2D crystals directly explains the origin of n‐type behavior in MoTe₂ devices in recent experiments and offers new opportunities for electronic structure engineering with various 2D materials.     

Mesoporous Germanium Anode Materials for Lithium‐Ion Battery with Exceptional Cycling Stability in Wide Temperature Range

Porous structured materials have unique architectures and are promising for lithium‐ion batteries to enhance performances. In particular, mesoporous materials have many advantages including a high surface area and large void spaces which can increase reactivity and accessibility of lithium ions. This study reports a synthesis of newly developed mesoporous germanium (Ge) particles prepared by a zincothermic reduction at a mild temperature for high performance lithium‐ion batteries which can operate in a wide temperature range. The optimized Ge battery anodes with the mesoporous structure exhibit outstanding electrochemical properties in a wide temperature ranging from ?20 to 60 °C. Ge anodes exhibit a stable cycling retention at various temperatures (capacity retention of 99% after 100 cycles at 25 °C, 84% after 300 cycles at 60 °C, and 50% after 50 cycles at ?20 °C). Furthermore, full cells consisting of the mesoporous Ge anode and an LiFePO₄ cathode show an excellent cyclability at ?20 and 25 °C. Mesoporous Ge materials synthesized by the zincothermic reduction can be potentially applied as high performance anode materials for practical lithium‐ion batteries.     

Plasmon Enhanced Fluorescence Based on Porphyrin–Peptoid Hybridized Gold Nanoparticle Platform

A porphyrin–peptoid‐hybridized silica‐coated gold nanoparticle is developed, which is inspired by the protein–chlorophyll ensemble found in photosynthetic antenna. In the natural antenna, chlorophylls are integrated into dense assemblies that are supported by frameworks of proteins, which ensure optimal pigment arrangement for effective light harvesting. In the subject platform, porphyrins are conjugated to the peptoid helix scaffold in a structurally well‐defined alignments and subsequently immobilized on the surface of nanoparticles. This prevents intermolecular aggregation among porphyrins and allows high resolution analysis of the effect of porphyrin configuration on the optical properties of the system. Interestingly, under the influence of plasmon from the gold nanoparticle core, the fluorescence of porphyrin is enhanced up to 24‐fold at the wavelength where the plasmon resonance matches the porphyrin excitation wavelength. In addition, differences in porphyrin configuration result in spectral modification of their fluorescence emissions. Particularly, the peptoid bearing two porphyrins at a distance of 6 Å shows the most significant alteration in fluorescence. The platform can facilitate extensive studies on the relationship between porphyrin arrangement design and their photophysical interaction in antenna complexes.     

Hierarchical 3D Cobalt‐Doped Fe3O4 Nanospheres@NG Hybrid as an Advanced Anode Material for High‐Performance Asymmetric Supercapacitors

Hierarchical nanostructure, high electrical conductivity, extraordinary specific surface area, and unique porous architecture are essential properties in energy storage and conversion studies. A new type of hierarchical 3D cobalt encapsulated Fe₃O₄ nanosphere is successfully developed on N‐graphene sheet (Co?Fe₃O₄ NS@NG) hybrid with unique nanostructure by simple, scalable, and efficient solvothermal technique. When applied as an electrode material for supercapacitors, hierarchical Co?Fe₃O₄ NS@NG hybrid shows an ultrahigh specific capacitance (775 F g^?1 at a current density of 1 A g^?1) with exceptional rate capability (475 F g^?1 at current density of 50 A g^?1), and admirable cycling performance (97.1% capacitance retention after 10 000 cycles). Furthermore, the fabricated Co?Fe₃O₄ NS@NG//CoMnO₃@NG asymmetric supercapacitor (ASC) device exhibits a high energy density of 89.1 Wh kg^?1 at power density of 0.901 kW kg^?1, and outstanding cycling performance (89.3% capacitance retention after 10 000 cycles). Such eminent electrochemical properties of the Co?Fe₃O₄ NS@NG are due to the high electrical conductivity, ultrahigh surface area, and unique porous architecture. This research first proposes hierarchical Co?Fe₃O₄ NS@NG hybrid as an ultrafast charge?discharge anode material for the ASC device, that holds great potential for the development of high‐performance energy storage devices.     

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.