Electrochemical performance of in situ LiFePO4 modified by N-doped graphene for Li-ion batteries

LiFePO₄ modified by N-doped graphene (NG) with a three-dimensional conductive network structure was synthesized via a one-step in situ hydrothermal method. The effects of N amount of NG on the phase structure, morphology, and electrochemical properties of LiFePO₄ are investigated in this study. X-ray diffraction (XRD) results show that doping suitable N amounts in NG do not alter the crystal structure of LiFePO_4, and scanning electron microscopy (SEM) images show that NG can slightly reduce the particle size of LiFePO₄. The high-resolution transmission electron microscopy (HRTEM) results show that the LiFePO₄ particles are well covered and connected by NG. The electrochemical performance confirms that LiFePO₄ modified by 20% N-doped graphene (named LFP/NG-4) displays a perfect specific capacity of 166.6 mAh·g^?1 at a rate of 0.2C and can reach 125 mAh·g^?1 at a rate of 5 C. Electrochemical impedance spectroscopy (EIS) results illustrate that the charge transfer resistance value of the LFP/NG-4 composite is only 58.6 Ω, which is very low compared with LiFePO₄. Cyclic voltammetry (CV) tests indicate that the addition of 20% N-doped graphene can effectively reduce electrode polarization and improve reversibility. The LFP/NG-4 composite with a three-dimensional conductive network structure can be regarded as a promising cathode material for Li-ion batteries.     

Synthesis of Hollow Three-Dimensional Channels LiNi_(0.5)Mn_(1.5)O_4 Microsphere by PEO Soft Template Assisted with Solvothermal Method

A appropriate size with three-dimension(3 D) channels for lithium diffusion plays an important role in constructing highperforming LiNi_(0.5)Mn_(1.5)O_4(LNMO) cathode materials, as it can not only reduce the transport path of lithium ions and electrons, but also reduce the side effects and withstand the structural strain in the process of repetitive Li~+ intercalation/deintercalation. In this work, an e fficient method for designing the hollow LNMO microsphere with 3 D channels structure by using polyethylene oxide(PEO) as soft template agent assisted solvothermal method is proposed. Experimental results indicate that PEO can make the reagents mingle evenly and nucleate slowly in the solvothermal process, thus obtaining a homogeneous distribution of carbonate precursors. In the final LNMO products, the hollow 3 D channels structure obtained by the decomposition of PEO and carbonate precursor in the calcination can provide abundant electroactive zones and electron/ion transport paths during the charge/discharge process, which benefits to improve the cycling performance and rate capability. The LNMO prepared by adding 1 g PEO possesses the most outstanding electrochemical performance, which presented an excellent discharge capacity of 143.1 mAh g~(-1) at 0.1 C and with a capacity retention of 92.2% after 100 cycles at 1 C. The superior performance attributed to the 3 D channels structure of hollow microspheres, which provide uninterrupted conductive systems and therefore achieve the stable transfer for electron/ion.     

Adaptive Discretization for Computerized Tomography

Two adaptive discretization frameworks are tested for computerized tomography (CT) data reconstruction. Removal of inactive pixels is primary motivation. Efficient and user independent entropy optimized masking is employed for spatial filtering purposes. Density of nodes at high gradient of reconstructed physical property is used as adaptation criterion. An alternative option, independent from noisy projection data and nature of the physical properties, is also discussed. Sensitivity analysis between the uniform and nonuniform (evolved via adaptive route) reconstruction grid reveals the utility of nonuniform grids. Iterative and transform based reconstruction techniques are used. Outcomes are tested successfully on three real world projection data from two different compact CT setups and one commercial high-resolution micro-CT scanner.     

自动化立体仓库消防给水设施设计探讨

立体仓库采用智能化出入库管理,集自动入库、智能化储存和自动化出库于一体,物品高度集中,火灾负荷集中的建筑。立体仓库的消防给水设施是其中重要的消防设施之一,本文主要分析立体仓库消防水系统的设计及参数的选择。     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.     

Convolutional neural network with adaptive inferential framework for skeleton-based action recognition

In the task of skeleton-based action recognition, CNN-based methods represent the skeleton data as a pseudo image for processing. However, it still remains as a critical issue of how to construct the pseudo image to model the spatial dependencies of the skeletal data. To address this issue, we propose a novel convolutional neural network with adaptive inferential framework (AIF-CNN) to exploit the dependencies among the skeleton joints. We particularly investigate several initialization strategies to make the AIF effective with each strategy introducing the different prior knowledge. Extensive experiments on the dataset of NTU RGB+D and Kinetics-Skeleton demonstrate that the performance is improved significantly by integrating the different prior information. The source code is available at: https://github.com/hhe-distance/AIF-CNN.     

Design of an adaptive super-twisting decoupled terminal sliding mode control scheme for a class of fourth-order systems

This paper proposes an adaptive super-twisting decoupled terminal sliding mode control technique for a class of fourth-order systems. The adaptive-tuning law eliminates the requirement of the knowledge about the upper bounds of external perturbations. Using the proposed control procedure, the state variables of cart-pole system are converged to decoupled terminal sliding surfaces and their equilibrium points in the finite time. Moreover, via the super-twisting algorithm, the chattering phenomenon is avoided without affecting the control performance. The numerical results demonstrate the high stabilization accuracy and lower performance indices values of the suggested method over the other ones. The simulation results on the cart-pole system as well as experimental validations demonstrate that the proposed control technique exhibits a reasonable performance in comparison with the other methods.     

Pumped-hydro in Bendigo: Room for wider reform?

As the world adjusts to a rapidly changing climate, interest in renewable energy technologies has increased. One disadvantage of renewables is that they’re only intermittently reliable; when the sun doesn’t shine, or the wind stops blowing the energy generation capacity of these infrastructures tapers off. In light of these changes there has been renewed interest in the creation of Pumped Hydro-Electricity Storage (‘PHES’), which acts as a ‘battery’ storing excess electricity generated when consumption of energy is at its lowest. In addition new technologies allow for legacy infrastructures, that would otherwise be a liability for the community, to now be used in a productive way for projects like PHES. Although there are many exciting opportunities the Bendigo PHES project presents, there is a dearth of regulatory issues for a small-medium scale project like this. This paper will explore the way in which such a project would fit within the national electricity market, as well as the limitations and barriers to implementation it might face before making some brief recommendations on changes that would benefit other such schemes in the future.     

An enhancement algorithm based on adaptive updating template with Gaussian model for Si3N4 ceramic bearing roller surface defects detection

The method based on machine vision image processing is used to detect the surface defects of Si₃N₄ bearing roller. Owing to the variety of defects, small area and low contrast, it is easy to miss or error detection. In this paper, an adaptive update template defect enhancement algorithm based on Gaussian model is proposed. First, a large number of surface images of Si₃N₄ bearing roller are collected to obtain the non-defect background statistical feature, and the background characteristic curve is fitted by Gaussian model. Further, the initial background template is gained according to the Gaussian curve. Then, combined with the gray distribute of defect images and initial background template, unique adaptive update template can be established. Finally, subtraction operation and nonlinear enhancement are used to improve the comparison of defect information and background. Through inverse sorting, adaptive threshold segmentation and Canny operation, the precise positioning of defects is realized. The enhancement algorithm can effectively enhance the contrast and eliminate the influence of noise. The average detection time is 0.84s, and the detection accuracy is 96.2%.     

The effects of fuel variability on the electrical performance and durability of a solid oxide fuel cell operating on biohythane

Biohythane is typically composed of 60/30/10 vol% CH₄/CO₂/H₂ and can be produced via two-stage anaerobic digestion of renewable and low carbon biomass with much greater efficiency compared with CH₄/CO₂ biogas. This work investigates the effects of fuel variability on the electrical performance and fuel processing of a commercially available anode supported solid oxide fuel cell (SOFC) operating on biohythane mixtures at 750 °C. Cell electrical performance was characterised using current-voltage curves and electrochemical impedance spectroscopy. Fuel processing was characterised using quadrupole mass spectroscopy. It is shown that when H₂/CO₂ is blended with CH₄ to make biohythane, the SOFC efficiency is significantly increased, high SOFC durability is achieved, and there are considerable savings in CH₄ consumption. Enhanced electrical performance was due to the additional presence of H₂ and promotion of CH₄ dry reforming, the reverse Boudouard and reverse water-gas shift reactions. These processes alleviated carbon deposition and promoted electrochemical oxidation of H₂ as the primary power production pathway. Substituting 50 vol% CH₄ with 25/75 vol% H₂/CO₂ was shown to increase cell power output by 81.6% at 0.8 V compared with pure CH₄. This corresponded to a 3.4-fold increase in the overall energy conversion efficiency and a 72% decrease in CH₄ consumption. A 260 h durability test demonstrated very high cell durability when operating on a typical 60/30/10 vol% CH₄/CO₂/H₂ biohythane mixture under high fuel utilisation due to inhibition of carbon deposition. Overall, this work suggests that decarbonising gas grids by substituting natural gas with renewably produced H₂/CO₂ mixtures (rather than pure H₂ derived from fossil fuels), and utilising in SOFC technology, gives considerable gains in energy conversion efficiency and carbon emissions savings.