叶黄素简介

叶黄素属类胡萝卜素,近年研究发现其具有保护视觉、抗氧化、抗癌等作用。本文从叶黄素的结构、性质、来源、生理功效、研究现状及应用前景等方面进行了介绍。     

Molecular Weight Distribution Control for Polymerization Processes Based on the Moment-Generating Function

The molecular weight distribution is an important factor that affects the properties of polymers. A control algorithm based on the moment-generating function was proposed to regulate the molecular weight distribution for polymerization processes in this work. The B-spline model was used to approximate the molecular weight distribution, and the weight state space equation of the system was identified by the subspace state space system identification method based on the paired date of B-spline weights and control inputs. Then, a new performance criterion mainly consisting of the moment-generating function was constructed to obtain the optimal control input. The effectiveness of the proposed control method was tested in a styrene polymerization process. The molecular weight distribution of the styrene polymers can be approximated by the B-spline model effectively, and it can also be regulated towards the desired one under the proposed control method.     

Investigations of nuclear dynamics of LaI<Subscript>3</Subscript> molecule. I. Potential functions of normal vibrations in harmonic and anharmonic approximations

The potential energy surface of the LaI₃ molecule is scanned along the normal coordinate by a B3LYP/SDD, SDD method. It is shown that a nonplanar ν₂(A ₂″) vibrational potential function is most anharmonic. The effect of anharmonicity on the root mean square amplitudes of vibrations and the vibrational molecule spectrum is stated.     

取代1-氯蒽醌中分子内卤键的电子密度拓扑分析

运用量子化学密度泛函B3LYP方法,在6-311＋＋G（d,p）基组水平上对邻位和间位取代1-氯葸醌的分子内卤键进行了研究．用电子定域函数和“分子中的原子,,理论对分子内卤键的性质进行了电子密度拓扑分析．通过对计算得到的密度矩阵进行σ-π兀分离,得到了π-键的键径和分子图,并讨论了。电荷密度和兀电荷密度对卤键的影响．结果表明,键鞍点和环鞍点处的电子密度拓扑性质均可作为衡量分子内卤键强度的量度．键鞍点和环鞍点处的电荷密度P越大,键鞍点与环鞍点的距离越大,卤键强度越大．除σ电荷密度外,π电荷密度对分子内卤键的性质也有明显影响．     

甲醇制烯烃催化剂失活模型

在固定床反应器中,研究了甲醇制烯烃反应中当SAPO-34分子筛处于失活期时催化剂活性的变化情况,主要考察催化剂活性随反应温度、空速和运行时间的变化,通过对实验数据的分析拟合,得到了催化剂活性与失活时间、反应温度、空速的经验关联式(失活模型),对失活模型的检验表明,该模型与实验数据较为吻合,表明了该式的准确性。 通过对失活模型的分析,获得了当失活速率达到最大时的失活时间与反应时间、空速的关联式,失活过程应服从不均匀表面失活机理,并且当催化剂处于失活区时,失活时间对活性的影响要大于空速和温度。     

The changes in free energies and entropies from analytical radial distribution functions

The changes in Helmholtz free energies and entropies in dense fluids have been evaluated using three known analytical expressions for radial distribution functions (RDFs) of Lennard–Jones (L-J) fluid. This method provides a simpler and a more expeditious way for the calculation of free energy and entropy in L-J dense fluids through statistical mechanics. Previously, integral equations or perturbation theories were used for this purpose. Such approach not only tests the power of analytical distribution functions in predicting the changes in Helmholtz free energies and entropies, but also specifies better expressions in determining these properties. The results are compared with experimental data and an accurate analytic equation of state for the L-J fluid. It is shown if an expression properly presents RDFs as a function of interparticle distance, density and temperature, it is possible to calculate the changes in Helmholtz free energies and entropies from analytical distribution functions.     

压电材料平面裂纹尖端场的杂交应力有限元分析

基于复势理论和杂交变分原理建立了一种适用于力电耦合分析的杂交应力有限元模 型. 给出了建立刚度矩阵的主要公式和推导过程，单元内的位移场和应力场采用满足平 衡方程的复变函数级数解，假设的复变函数级数解事先精确满足裂纹的无应力和电位移法向 分量为零的条件，单元外边界的位移场假设按抛物线变化, 单元的刚度矩阵采用Gauss积分的方法得出. 通过对力电耦合裂尖场的数值计算验证了程序 的正确性和单元的有效性，同时也用所得结果校验了理论解.     

基于拓扑描述函数的连续体结构拓扑优化方法

提出了一种利用拓扑描述函数(TDF)作为拓扑设计变量求解连续体结构拓扑优化问题 的新方法. 优化问题的目标函数是结构的整体柔顺性，约束条件为对于可利用材料的体积限 制. 这种方法不仅可以消除拓扑优化中经常出现的棋盘格式等数值不稳定现象，而且能够有 效地抑制传统算法处理此类优化问题时所引发的边界扩散效应. 与其它的基于水平集描述函 数的拓扑优化方法相比，所提出的算法不仅无需求解控制水平集函数演化的双曲守恒方 程，而且合理地考虑了目标函数的拓扑导数信息，因而使得算法的计算效率有了显著的提高.     

Machine Learning Approaches for Metalloproteins

Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.