SmPO4-coated Li1.2Mn0.54Ni0.13Co0.13O2 as a cathode material with enhanced cycling stability for lithium ion batteries

SmPO₄ coated Li_1.2Mn_0.54Ni_0.13Co_0.13O₂ cathode materials were prepared by the precipitation method and calcined at 450 °C. The crystal structures and electrochemical properties of the pristine and coated samples are studied by X-ray diffraction, scanning electron microscopy, high resolution transmission electron microscopy, electron diffraction spectroscopy, galvanostatic cycling, cyclic voltammetry, and electrochemical impedance spectroscopy (EIS). It has been found that the electrochemical performances of the Li-rich cathode material have been substantially improved by SmPO₄ surface coating. Especially, the 2 wt% SmPO₄-coated sample demonstrates the best cycling performance, with capacity retention of 88.4% at 1 C rate after 100 cycles, which is much better than that of 72.3% in the pristine sample. The improved electrochemical properties have been ascribed to the SmPO₄ coating layer, which not only stabilizes the cathode structure by decreasing the loss of oxygen, but also protects the Li-rich cathode material from side reaction with the electrolyte and increases the Li⁺ migration rate at the cathode interface.     

Piezoelectric effects on carrier capturing time in a hybrid p–n junction system: a numerical study using finite element method

The piezoelectric influence on carrier (electrons and holes) trapping time in a hybrid p–n junction system is investigated in this paper. The hybrid nature of the junction is the conceptual combination of p–n junction solar cell and piezoelectricity. The mechanism is that the piezoelectric field induced on the p–n interface can affect the transport of free carriers such as electrons and holes present inside the junction system. These free carriers included are dissociated from excitons which are generated by the light or by the piezoelectric field on the interface via Langevin recombination. The numerical analysis focuses on carrier density (n, p), carrier velocity \((v_{n},v_{p})\), carrier capture cross section \((\sigma _{n},\sigma _{p})\), and carrier capturing time \((\tau _{n},\tau _{p})\) using the finite element method. These parameters are affected by the piezoelectric potential induced by different vertical stresses \((T_{z})\) on the p–n interface. Based on these features, the simulation of the distribution of the carrier (electrons or holes) capturing time over the junction system can be used to analyze the piezophototronic devices where traps are present.     

Channel Magnifying Limited Feedback Technique for FDD Massive MIMO Systems

A limited feedback system, so-called, channel magnifying (CM) is proposed for a downlink (DL) frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) system. Although massive MIMO system has received significant research interest as a key technology for beyond 4G wireless communications systems, it has a number of issues that needs to be technically addressed. Among such issues is the difficulty of acquiring channel state information at transmitter for an FDD massive MIMO system which cannot exploit channel reciprocity as in a time-division duplex system. The proposed CM technique makes it possible to support a few user equipments in DL FDD massive MIMO system by finding a balance between spatial resources and channel quantization error (CQE). By choosing a subchannel with low CQE, CM can secure multiplexing gain at high SNR based on a fixed size codebook. Two types of subchannel indicator alignment (SIA) schemes are introduced for efficient interference nulling for the proposed CM technique. Specifically, we discuss how to maximize the sumrate of CM through genie added SIA and minimum CQE based SIA. Simulation results show that the sum rate of the proposed CM has a higher multiplexing gain than that of random vector quantization, especially when the number of transmit antennas is sufficiently large.     

Acceleration of cellular automata algorithms using genetic algorithms

The following problem is solved: Given a Cellular Automaton with continuous state space which simulates a physical system or process, use a Genetic Algorithm in order to find a Cellular Automaton with discrete state space, having the smallest possible lattice size and the smallest possible number of discrete states, the results of which are as close as possible to the results of the Cellular Automaton with continuous state space. The Cellular Automaton with discrete state space evolves much faster than the Cellular Automaton with continuous state space. The state spaces of two Cellular Automata have been discretized using a Genetic Algorithm. The first Cellular Automaton simulates the two-dimensional photoresist etching process in integrated circuit fabrication and the second is used to predict forest fire spreading. A general method for the discretization of the state space of Cellular Automata using a Genetic Algorithm is also presented. The aim of this work is to provide a method for accelerating the execution of algorithms based on Cellular Automata (Cellular Automata algorithms) and to build a bridge between Cellular Automata as models for physical systems and processes and Cellular Automata as a VLSI architecture.     

AI-based decision support tool for strategic decision making in the factory of the future

The process of strategic decision making is highly complex. Top executives make decisions based on uncertain information, much of which is difficult to quantify, often using intuitive feelings and experience as guiding factors. The demands placed upon high-level managers will increase in the factory of the future, as organizations become less hierarchical, cycle times decrease and customized production becomes a reality. Advances in process technologu will provide high levels of flexibility but knowledge technology, allied to strong and purposeful leadership, will provide the key to competitive success in many industries in future. This paper describes a prototype decision support system and learning tool which uses qualitative frame-based knowledge representation to encapsulate the intuitive knowledge of high-level decision makers. The system allows a user to construct a ‘mind set’ of a particular strategic problem domain, using qualitative relationships to describe the causal effects and interactions between concepts. The resulting knowledge base may then be used for experimentation by altering individual relationships and observing the effects of the change in the structure of the knowledge base.     

Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches

Cardiovascular diseases (CDs) are a major concern in the human race and one of the leading causes of death worldwide. β-Adrenergic receptors (β1-AR and β2-AR) play a crucial role in the overall regulation of cardiac function. In the present study, structure-based virtual screening, machine learning (ML), and a ligand-based similarity search were conducted for the PubChem database against both β1- and β2-AR. Initially, all docked molecules were screened using the threshold binding energy value. Molecules with a better binding affinity were further used for segregation as active and inactive through ML. The pharmacokinetic assessment was carried out on molecules retained in the above step. Further, similarity searching of the ChEMBL and DrugBank databases was performed. From detailed analysis of the above data, four compounds for each of β1- and β2-AR were found to be promising in nature. A number of critical ligand-binding amino acids formed potential hydrogen bonds and hydrophobic interactions. Finally, a molecular dynamics (MD) simulation study of each molecule bound with the respective target was performed. A number of parameters obtained from the MD simulation trajectories were calculated and substantiated the stability between the protein-ligand complex. Hence, it can be postulated that the final molecules might be crucial for CDs subjected to experimental validation.     

Formation and oxidation behavior of Ni2Al3 coating on heat-resistant Ti-alloy

A nickel aluminide coating process, developed on heat resistant Ti alloy by electroplating a Ni film followed by high Al activity pack cementation, forms a duplex layer structure, an outer Ni₂Al₃ layer and an inner TiAl₃/TiAl₂/TiNiAl₂ layer. The coated Ti-alloy was oxidized in air for up to 3,600 ks (1,000 h) under thermal cycling between room temperature and 1023 K. A protective Al₂O₃ scale formed with little oxide exfoliation and the oxidation amount was 6 g/m² after the 3,600 ks oxidation. It was found that after 3,600 ks oxidation at 1023 K the coating layer structure was essentially unchanged, similar to the as-formed, except that the TiAl₃ layer had changed to TiAl₂.