A method for restraining electromagnetic interference of electricity meter by means of pulse width testing circuit

Peer Reviewe

High-quality mesoporous graphene particles as high-energy and fast-charging anodes for lithium-ion batteries.

The application of graphene for electrochemical energy storage has received tremendous attention; however, challenges remain in synthesis and other aspects. Here we report the synthesis of high-quality, nitrogen-doped, mesoporous graphene particles through chemical vapor deposition with magnesium-oxide particles as the catalyst and template. Such particles possess excellent structural and electrochemical stability, electronic and ionic conductivity, enabling their use as high-performance anodes with high reversible capacity, outstanding rate performance (e.g., 1,138 mA h g-1 at 0.2 C or 440 mA h g-1 at 60 C with a mass loading of 1 mg cm-2), and excellent cycling stability (e.g., >99% capacity retention for 500 cycles at 2 C with a mass loading of 1 mg cm-2). Interestingly, thick electrodes could be fabricated with high areal capacity and current density (e.g., 6.1 mA h cm-2 at 0.9 mA cm-2), providing an intriguing class of materials for lithium-ion batteries with high energy and power performance

A stable nanoporous silicon anode prepared by modified magnesiothermic reactions

Porous silicon prepared by low-cost and scalable magnesiothermic reactions is a promising anode material for Li-ion batteries; yet, retaining good cycling stability for such materials in electrodes of practical loading remains a challenge. Here, we engineered the nanoporous silicon from a modified magnesiothermic reaction by controlled surface oxidization forming a <5 nm oxide layer on the 10–20 nm Si nanocrystallites. High loading electrodes of ~3 mAh/cm² demonstrates stable cycling with ~80% capacity retention over 150 cycles. The specific discharge capacity based on the total electrode weight is ~1000 mAh/g at the lithiation/delithiation current density of 0.5/0.75 mA/cm². This work reveals the importance of the surface treatment on nanostructured Si, which will lead to a well-controlled ratio of silicon and surface oxide layer and provide guidance on further improvement on silicon-based anode materials.This is the publisher’s final pdf. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/nano-energy/Keywords: Silicon anode, Lithium ion batteries, Magnesiothermic reaction, Porous silico

A review of the scaled boundary finite element method for two-dimensional linear elastic fracture mechanics

The development and the application of the scaled boundary finite element method for fracture analysis is reviewed. In this method, polygonal elements (referred to as subdomains) of arbitrary number of edges are constructed, with the only limitation that the whole boundary is directly visible from the scaling centre. The element solution is semi-analytical. When applied to two-dimensional linear fracture mechanics, any kinds of stress singularities are represented analytically without local refinement, special elements and enrichment functions. The flexibility of polygons to represent arbitrary geometric shapes leads to simple yet efficient remeshing algorithms to model crack propagation. Coupling procedures with the extended finite element method, meshless method and boundary element method to handle changes in the crack morphology have been established. These developments result in an efficient framework for fracture modelling. Examples of applications are provided to demonstrate their feasibility. © 2017 Elsevier Lt

Pruning Multilayered ELM Using Cholesky Factorization and Givens Rotation Transformation

Extreme learning machine is originally proposed for the learning of the single hidden layer feedforward neural network to overcome the challenges faced by the backpropagation (BP) learning algorithm and its variants. Recent studies show that ELM can be extended to the multilayered feedforward neural network in which the hidden node could be a subnetwork of nodes or a combination of other hidden nodes. Although the ELM algorithm with multiple hidden layers shows stronger nonlinear expression ability and stability in both theoretical and experimental results than the ELM algorithm with the single hidden layer, with the deepening of the network structure, the problem of parameter optimization is also highlighted, which usually requires more time for model selection and increases the computational complexity. This paper uses Cholesky factorization strategy and Givens rotation transformation to choose the hidden nodes of MELM and obtains the number of nodes more suitable for the network. First, the initial network has a large number of hidden nodes and then uses the idea of ridge regression to prune the nodes. Finally, a complete neural network can be obtained. Therefore, the ELM algorithm eliminates the need to manually set nodes and achieves complete automation. By using information from the previous generation’s connection weight matrix, it can be evitable to re-calculate the weight matrix in the network simplification process. As in the matrix factorization methods, the Cholesky factorization factor is calculated by Givens rotation transform to achieve the fast decreasing update of the current connection weight matrix, thus ensuring the numerical stability and high efficiency of the pruning process. Empirical studies on several commonly used classification benchmark problems and the real datasets collected from coal industry show that compared with the traditional ELM algorithm, the pruning multilayered ELM algorithm proposed in this paper can find the optimal number of hidden nodes automatically and has better generalization performance

A new approach to determine wedge-indented interfacial toughness in soft-film hard-substrate systems with application to low-k films on Si substrate

10.1557/jmr.2012.319Journal of Materials Research27222872-2883JMRE

Study of the Magnetic Properties of Haematite Based on Spectroscopy and the IPSO-ELM Neural Network

The detection of the magnetic properties of haematite plays an important role in the adjustment of the beneficiation process of haematite and the improvement of metal recovery. The existing methods for measuring the magnetic properties of iron ore either have large errors or take a long time. Therefore, it is very necessary to find a method that can quickly and accurately detect the magnetic properties of haematite. This paper presents a method to detect the magnetic properties of haematite based on the extreme learning machine based on the improved particle swarm optimization (IPSO-ELM) algorithm and spectroscopy. The improved particle swarm optimization algorithm is used to optimize the input weights, hidden layer deviations, and hidden layer nodes of the ELM network. Introducing the linear decreasing inertia weight for the particle swarm algorithm, taking into account the norm of the output weight in the particle update process and using the variation idea to change the length of the particle give the IPSO-ELM better stability and generalization ability. The experimental results show that the IPSO-ELM prediction model has a good prediction performance and has better generalization ability than that of the ELM and PSO-ELM prediction models. Compared with traditional chemical analysis methods and manual methods, this method has great advantages in terms of economy, speed, and accuracy

Fluorination Effect on Lithium- and Manganese-Rich Layered Oxide Cathodes.

Lithium- and manganese-rich (LMR) layered oxides are promising high-energy cathodes for next-generation lithium-ion batteries, yet their commercialization has been hindered by a number of performance issues. While fluorination has been explored as a mitigating approach, results from polycrystalline-particle-based studies are inconsistent and the mechanism for improvement in some reports remains unclear. In the present study, we develop an in situ fluorination method that leads to fluorinated LMR with no apparent impurities. Using well-defined single-crystal Li1.2Ni0.2Mn0.6O2 (LNMO) as a platform, we show that a high fluorination level leads to decreased oxygen activities, reduced side reactions at high voltages, and a broadly improved cathode performance. Detailed characterization reveals a particle-level Mn3+ concentration gradient from the surface to the bulk of fluorinated-LNMO crystals, ascribed to the formation of a Ni-rich LizNixMn2-xO4-yFy (x &gt; 0.5) spinel phase on the surface and a spinel-layered coherent structure in the bulk where domains of a LiNi0.5Mn1.5O4 high-voltage spinel phase are integrated into the native layered framework. This work provides fundamental understanding of the fluorination effect on LMR and key insights for future development of high-energy Mn-based cathodes with an intergrown/composite crystal structure

Selective Adsorption of CR (VI) onto Amine-Modified Passion Fruit Peel Biosorbent

This study aimed to prepare surface amino-riched passion fruit peel (DAPFP) by amination reaction with low-cost biomaterials and use it as a biosorbent to adsorb Cr (VI). The specific physicochemical and structural properties of DAPFP were characterized by SEM, EDS, XRD, TG, Zeta, XPS, and FT-IR. The effects of pH value, initial concentration, adsorption time, coexisting ions, and temperature on the adsorption of Cr (VI) were systematically investigated. The results showed that within 90 min, DAPFP could reduce the concentration of Cr (VI) solution (1 mg/L−1) to an allowable safe level of drinking water (0.05 mg/L−1) specified by the World Health Organization. The adsorption process complies with pseudo-second-order kinetics and the Langmuir isotherm model. The adsorption capacity of the prepared biosorbent could reach 675.65 mg/g−1. The results of thermodynamic studies confirmed that the adsorption process was a self-discharging heat process. DAPFP also showed good reusability; even after being used repeatedly five times, it still showed excellent adsorption performance. FT-IR and XPS analyses showed that electrostatic attraction and reduction were the main reasons for the adsorption. By virtue of its low cost and excellent adsorption performance, DAPFP has a potential practical application as an adsorbent in treating Cr (VI) containing wastewater