碳纳米管负载钯基催化剂催化氧化2,5-呋喃二甲醇合成2,5-呋喃二甲酸

为积极应对化石能源枯竭和生态环境日益严峻等问题,可再生生物质资源的深度开发并进一步替代传统能源或石化原料被广泛认可.利用高效催化技术将生物质资源转化为高附加值的平台化合物,有望衍生出大量具备新颖结构与功能的绿色化学品.2,5-呋喃二甲酸(FDCA)作为重要的生物质基平台化合物之一,具有巨大的市场应用价值,其中因其与化石基对苯二甲酸(PTA)有着极其相似的化学结构,以FDCA替代PTA作为合成单体制备大宗聚合物备受关注.以5-羟甲基糠醛(HMF)为原料,采用多相催化体系(主要是贵金属催化剂)选择氧化制备FDCA是目前广泛采用的方法.但“HMF路线”面临一些基础性的难题,如HMF熔点较低,需低温存储,增加了实际应用中的运输成本;HMF在碱性溶液中易降解,导致反应过程中碳平衡损失;HMF结构中含有的不对称的羟基和醛基官能团在氧化反应中会发生竞争反应,致使反应副产物较多;此外,碱性反应介质中通常会得到醛基优先氧化的中间体5-羟甲基-2-呋喃甲酸(HMFCA),但由于HMFCA结构中羧基官能团的存在使得羟基进一步氧化较为困难,通常需要增加碱浓度、提升温度或压力,使反应条件变得苛刻.因此,寻求新的原料替代HMF,实现温和条件下高效合成FDCA具有重要意义.本文采用改性后的碳纳米管负载Pd催化剂(Pd/o-CNT),从具有独特对称结构的2,5-二羟甲基呋喃(BHMF)出发,提出一种新颖、高效催化合成FDCA的“BHMF路线”.反应在60°C常压下进行,BHMF在20 min内即可完全转化,60 min后FDCA的产率最高可达93.0%,优于相同条件下HMF为原料时的性能(FDCA产率仅为35.7%).相比于未作处理的碳纳米管负载钯催化剂(Pd/CNT),Pd/o-CNT催化剂具有更高含量的氢化钯(PdH_x)物种,显著促进了FDCA产率的提升.Pd/o-CNT在循环使用10次后,BHMF仍能完全转化,FDCA产率维持在75%.稳定性下降可能与活性物种流失、团聚及价态变化有关.基于对照试验,本文提出了可能的反应路径,即BHMF主要是通过2,5-二甲酰基呋喃和5-甲酰基-2-呋喃甲酸作为过程中间体,有效转化为FDCA,从而规避并减少生成HMF和活性较低的HMFCA.本文通过以新原料BHMF作底物,实现了高效制备生物基平台化合物FDCA,为生物质的产业化应用提供了新的研究思路.     

核酸工具酶辅助的金属稳定同位素标记方法在生物分析中的应用

本文主要概述了近年来核酸工具酶辅助的基于金属稳定同位素标记的电感耦合等离子体质谱(ICP-MS)检测方法在生物分析中的发展和应用,简要介绍了该方法在蛋白质、核酸及一些生物小分子检测中的应用。最后对核酸工具酶辅助的基于金属稳定同位素标记的电感耦合等离子体质谱(ICP-MS)检测方法的发展前景做了展望。     

碳酸钙固硫固体进样测汞仪快速测定硫化铅精矿中汞

在使用固体进样测汞仪直接测定硫化铅精矿中汞时,由于试样中硫含量通常较高(高达30%以上),在测定过程中大量的硫被氧化会污染甚至腐蚀固体进样测汞仪的核心部件催化管,导致催化管寿命严重缩短。为解决该问题,建立碳酸钙固硫-固体进样测汞仪直接测定硫化铅精矿中汞的分析方法;并对Ca/S比,分解温度,分解时间,齐化时间等参数进行优化。在最优条件下,Hg的含量在2~20 ng和20~500ng线性范围内回归系数(R²)分别为0.9996和0.9998;方法检出限(LOD)为0.006 μg/g。用该法对3个典型样品进行测定,相对标准偏差RSD≤9%(n=7),加标回收率为96%~106%。该方法简单、快捷,准确度和精密度高,适合硫化铅精矿中汞的快速测定。     

中性线修正型变截面梁类构件压电控制

传统绝对节点坐标法(absolute nodal coordinate formulation,ANCF)在变截面梁类构件建模过程中常以几何中位线等效构造单元中性线,难以对变截面单元位移场状态进行精确描述.为解决此类问题,本文以中细型变截面梁类构件为研究对象,深入考虑变截面结构几何因素及复合材料属性对变截面梁类构件中性...     

旋涡与行进表面水波相互作用的实验研究

采用振动板式造波器在二维水槽中生成近似单色的行进表面水波，采用夹板式涡发生器生成稳定上浮的涡对，在Ｆｒｏｕｄｅ数约为０．５的条件下，得到了水下涡对与不同波长和振幅的行进表面水波相互作用时的干扰图象，以实验方法验证了理论分析和数值计算的结果，并为进一步研究旋涡与行进表面水波的相互作用提供了一种实验研究方法     

计算重根特征向量导数的消元法

本文提出了重根特征向量导数计算的消无法，它分为若干直接算法和一种迭代算法。这种方法概念简单、实施容易、精度良好。该方法可经简单移值用于非重根情况。     

Production and Digestibility Studies of β-Galactosyl Xylitol Derivatives Using Heterogeneous Catalysts of LacA β-Galactosidase from Lactobacillus Plantarum WCFS1

The synthesis of β-galactosyl xylitol derivatives using immobilized LacA β-galactosidase from Lactobacillus plantarum WCFS1 is presented. These compounds have the potential to replace traditional sugars by their properties as sweetener and taking the advantages of a low digestibility. The enzyme was immobilized on different supports, obtaining immobilized preparations with different activity and stability. The immobilization on agarose-IDA-Zn-CHO in the presence of galactose allowed for the conserving of 78% of the offered activity. This preparation was 3.8 times more stable than soluble. Since the enzyme has polyhistidine tags, this support allowed the immobilization, purification and stabilization in one step. The immobilized preparation was used in synthesis obtaining two main products and a total of around 68 g/L of β-galactosyl xylitol derivatives and improving the synthesis/hydrolysis ratio by around 30% compared to that of the soluble enzyme. The catalyst was recycled 10 times, preserving an activity higher than 50%. The in vitro intestinal digestibility of the main β-galactosyl xylitol derivatives was lower than that of lactose, being around 6 and 15% for the galacto-xylitol derivatives compared to 55% of lactose after 120 min of digestion. The optimal amount immobilized constitutes a very useful tool to synthetize β-galactosyl xylitol derivatives since it can be used as a catalyst with high yield and being recycled for at least 10 more cycles.     

On the Nature of Functional Differentiation: The Role of Self-Organization with Constraints

The focus of this article is the self-organization of neural systems under constraints. In 2016, we proposed a theory for self-organization with constraints to clarify the neural mechanism of functional differentiation. As a typical application of the theory, we developed evolutionary reservoir computers that exhibit functional differentiation of neurons. Regarding the self-organized structure of neural systems, Warren McCulloch described the neural networks of the brain as being “heterarchical”, rather than hierarchical, in structure. Unlike the fixed boundary conditions in conventional self-organization theory, where stationary phenomena are the target for study, the neural networks of the brain change their functional structure via synaptic learning and neural differentiation to exhibit specific functions, thereby adapting to nonstationary environmental changes. Thus, the neural network structure is altered dynamically among possible network structures. We refer to such changes as a dynamic heterarchy. Through the dynamic changes of the network structure under constraints, such as physical, chemical, and informational factors, which act on the whole system, neural systems realize functional differentiation or functional parcellation. Based on the computation results of our model for functional differentiation, we propose hypotheses on the neuronal mechanism of functional differentiation. Finally, using the Kolmogorov–Arnold–Sprecher superposition theorem, which can be realized by a layered deep neural network, we propose a possible scenario of functional (including cell) differentiation.     

遗传算法求解约束非线性规划及Matlab实现

对于约束非线性规划问题,传统的方法:可行方向法、惩罚函数法计算烦琐且精度不高.用新兴的遗传算法来解决约束非线性规划,核心是惩罚函数的构造.以前的惩罚函数遗传算法有的精度较低,有的过于复杂.本文在两个定义的基础上构造了新的惩罚函数,并在新的惩罚函数的基础上,提出了一种解决约束非线性最优化问题的方法.通过两个例子应用Matlab说明了这个算法的可行性.     

A TRIANGULAR FINITE VOLUME ELEMENT METHOD FOR A SEMILINEAR ELLIPTIC EQUATION

In this paper we extend the idea of interpolated coefficients for a semilinear problem to the triangular finite volume element method. We first introduce triangular finite volume element method with interpolated coefficients for a boundary value problem of semilinear elliptic equation. We then derive convergence estimate in Hi-norm, L2-norm and L∞-norm, respectively. Finally an example is given to illustrate the effectiveness of the proposed method.