岩盐结构氧化锌物态方程的分子动力学模拟

利用分子动力学方法和有效经验对势模型对ZnO岩盐结构高温高压下的物态方程进行了研究， 发现分子动力学方法得到的ZnO岩盐结构的摩尔体积(300?1273 K，3.2?10.4 GPa)和实验结果吻合；另外，基于经验势模型的可靠性预测了1373?2273 K和0? 50 GPa的ZnO岩盐结构的P-V -T关系，并利用相应的热力学公式拟合得到了ZnO岩盐结构常态下的线性热膨胀系数、等温体模量及其对压力的一阶导数等重要的热力学参量．     

Molecular dynamics simulation of P-V-T relationship of ZnO with rock-salt structure using pair-wise interactions

Molecular dynamics (MD) method is used to investigate the behavior of the pressure-volume-temperature (P-V-T) relationship, lattice constant and thermal expansivity for ZnO with rock-salt structure at high pressures and temperatures. The interionic potential is taken to be the sum of pair-wise additive Coulomb, van der Waals attraction, and repulsive interactions. The isothermal and isobaric properties are discussed from the corresponding P-V-T relationship, and it is shown that the MD simulation is successful in reproducing the measured volumes of ZnO over a wide range of temperature and pressure. Meanwhile, the equations of state parameters including lattice constant, linear thermal expansion coefficient, and isothermal bulk modulus are calculated and compared with the available experimental data and the latest theoretical results. At an extended pressure and temperature range, P-V-T relationship, lattice constant, and linear thermal expansion coefficient have been predicted. The structural and thermodynamic properties of ZnO with rock-salt structure are summarized in the pressure 0-100 GPa ranges and the temperature up to 3100 K.     

A highly Selective Fluorescent Sensor for Monitoring Cu<Superscript>2+</Superscript> Ion: Synthesis,Characterization and Photophysical Properties

A new fluorescent sensor, 4-allylamine-N-(N-salicylidene)-1,8-naphthalimide (1), anchoring a naphthalimide moiety as fluorophore and a Schiff base group as receptor, was synthesized and characterized. The photophysical properties of sensor 1 were conducted in organic solvents of different polarities. Our study revealed that, depending on the solvent polarity, the fluorescence quantum yields varied from 0.59 to 0.89. The fluorescent activity of the sensor was monitored and the sensor was consequently applied for the detection of Cu²⁺ with high selectivity over various metal ions by fluorescence quenching in Tris-HCl (pH = 7.2) buffer/DMF (1:1, v/v) solution. From the binding stoichiometry, it was indicated that a 1:1 complex was formed between Cu²⁺ and the sensor 1. The fluorescence intensity was linear with Cu²⁺ in the concentration range 0.5–5 μM. Moreso, the detection limit was calculated to be 0.32 μM, which is sufficiently low for good sensitivity of Cu²⁺ ion. The binding mode was due to the intramolecular charge transfer (ICT) and the coordination of Cu²⁺ with C = N and hydroxyl oxygen groups of the sensor 1. The sensor proved effective for Cu²⁺ monitoring in real water samples with recovery rates of 95–112.6 % obtained.     

具比率依赖型HollingⅡ功能反应函数的两种群系统的最优税收策略

研究了具比率依赖型功能性反应函数的两种群系统,利用微分方程定性理论得到了系统正平衡点的存在性、局部渐近稳定性及全局渐近稳定性的条件,并且由Pontryagin最大值原理得到了最优税收策略.     

Structure, spin reorientation and M?ssbauer effect studies of Tb0.3Dy0.7(Fe1?xAlx)1.95 alloys

The effect of Al substitution for Fe on crystal structure, magnetostriction and spontaneous magnetostriction, anisotropy and spin reorientation of a series of polycrystalline Tb_0.3Dy_0.7(Fe_1−x Al_x)_1.95 alloys (x = 0, 0.05, 0.1, 0.15, 0.20, 0.25, 0.30, 0.35) at room temperature and 77 K was investigated systematically. It was found that the primary phase of Tb_0.3Dy_0.7(Fe_1−x Al_x)_1.95 is the MgCu₂-type cubic Laves phase structure when x < 0.4 and the lattice constant a of Tb_0.3Dy_0.7(Fe_1−x Al_x)_1.95 increases approximately and monotonically with the increase of x. The substitution of Al leads to the fact that the magnetostriction λ inceases slightly in a low magnetic field (H ⩽ 40 kA/m), but decreases sharply and is easily close to saturation in a high applied field as x increases, showing that a small amount of Al substitution is beneficial to a decrease in the magnetocrystalline anisotropy. It was also found that the spontaneous magnetostriction λ ₁₁₁ decreases greatly with x increasing. The analysis of the M?ssbauer spectra indicated that the easy magnetization direction in the {110} plane deviates slightly from the main axis of symmetry with the changes of composition and temperature, namely spin reorientation. A small amount of non-magnetic phase exists for x = 0.15 in Tb_0.3Dy_0.7(Fe_1−x Al_x)_1.95 alloys and the alloys become paramagnetic for x > 0.15 at room temperature, but at 77 K the alloys still remain magnetic phase even for x = 0.2. At room temperature and 77 K, the hyperfine field decreases and the isomer shifts increase with Al concentration increasing.     

一种新型的稀土Tb配合物的光致和电致发光性能的研究

用一种新型的含酚羟基的Schiff碱三足配体H₃L3稀土Tb配合物作发光层制备了三层结构电致发光器件,器件在正向直流偏压下发出明亮的绿光。器件的电致发光(EL)光谱和Tb配合物薄膜的光致发光(PL)光谱与典型的Tb配合物的发光光谱相同。研究了器件发光及电学特性,讨论了该种新型的稀土Tc配合物的发光机理。     

Metal-Free,Visible-Light-Induced Selective C−C Bond Cleavage of Cycloalkanones with Molecular Oxygen

A metal-free, visible-light-induced oxidative C−C bond cleavage of cycloketones with molecular oxygen is described. Cooperative Brønsted-acid catalysis and photocatalysis enabled selective C−C bond cleavage of cycloketones to generate an array of γ-, δ- and ϵ-keto esters under very mild conditions. Mechanistic studies indicate that singlet molecular oxygen (¹O₂) is responsible for this transformation.     

Point and interval forecasting for carbon price based on an improved analysis-forecast system

Effective analysis and forecasting of carbon prices, which is an essential endeavor for the carbon trading market, is still considered a difficult task because of the nonlinearity and nonstationarity inherent in carbon prices. Previous studies have failed at the analysis and interval prediction of carbon prices and are limited to point forecasts. Therefore, an improved carbon price analysis and forecasting system that consists of an analysis module and a forecasting module is established in this study; more importantly, the forecasting module includes point forecasting and interval forecasting as well. Aimed at investigating the characteristics of the carbon price series, a chaotic analysis based on the maximum Lyapunov exponent is performed, the determination of appropriate distribution functions based on our newly proposed hybrid optimization algorithm is conducted, and different distribution functions are effectively designed in the analysis module. Furthermore, in the point forecasting model, the phase space reconstruction technique is applied to reconstruct the sequences decomposed by variational mode decomposition due to the chaotic characteristics of the carbon price series, and the reconstructed sequences are considered as the optimal input–output variables of the forecasting model. Then, an adaptive neuro-fuzzy inference system model is trained by the newly proposed hybrid optimization algorithm, which is developed for the first time in the domain of carbon price point forecasting. Moreover, based on the results of point forecasting and the distribution function of the carbon price series determined by the analysis module, the interval forecasting results can be obtained and implemented to provide more reliable information for decision making. Empirical results based on the carbon price data of the European Union Emissions Trading System and Shenzhen of China demonstrate that the proposed system achieves better results than other benchmark models in point forecasting as well as interval forecasting.     

Novel biogeography-based optimization algorithm with hybrid migration and global-best Gaussian mutation

The Biogeography-Based Optimization algorithm and its variants have been used widely for optimization problems. To get better performance, a novel Biogeography-Based Optimization algorithm with Hybrid migration and global-best Gaussian mutation is proposed in this paper. Firstly, a linearly dynamic random heuristic crossover strategy and an exponentially dynamic random differential mutation one are presented to form a hybrid migration operator, and the former is used to get stronger local search ability and the latter strengthen the global search ability. Secondly, a new global-best Gaussian mutation operator is put forward to balance exploration and exploitation better. Finally, a random opposition learning strategy is merged to avoid getting stuck in local optima. The experiments on the classical benchmark functions and the complexity functions from CEC-2013 and CEC-2017 test sets, and the Wilcoxon, Bonferroni-Holm and Friedman statistical tests are used to evaluate our algorithm. The results show that our algorithm obtains better performance and faster running speed compared with quite a few state-of-the-art competitive algorithms. In addition, experimental results on Minimum Spanning Tree and K-means clustering optimization show that our algorithm can cope with these two problems better than the comparison algorithms.