新型HPWs@MIL-101催化剂的制备、表征及其催化性能

采用直接合成法将磷钨杂多酸(HPWs)固载到金属-有机骨架材料MIL-101的介孔笼中,制得新型HPWs@MIL-101多相催化剂。利用XRD、N2吸附、扫描电镜(SEM)、XPS、UV-Vis DRS、Raman和FT-IR等手段对该催化剂进行了表征,并研究了其在环戊烯选择氧化制备戊二醛反应中的催化性能。结果表明,磷钨杂多酸高度分散在金属-有机骨架MIL-101的介孔笼中,将磷钨杂多酸固载到MIL-101上后,能够很好地保持磷钨杂多酸的Keggin结构和载体MIL-101的晶体骨架结构。HPWs@MIL-101催化剂的催化性能远高于传统浸渍法制备的催化剂的催化性能,当磷钨杂多酸的负载量为12.5%(w)时,HPWs@MIL-101表现出最优的催化性能,环戊烯转化率高达100%,戊二醛的得率达到78.7%。重复实验表明该催化剂具有较高的稳定性,使用三次后,GA的得率仍然达到74.1%。     

2个具有Sn4O4梯状结构二丁基锡羧酸酯的微波溶剂热合成、结构和体外抗癌活性

在甲醇微波溶剂热中,二丁基氧化锡分别与4-甲基苯甲酸、4-二甲氨基苯甲酸反应,合成了2个具有梯形结构的二丁基锡羧酸酯[（μ₃-O）（ μ₂-OMe）（n-Bu₂Sn）₂（O₂CR）]₂（R=4-Me-C₆H₄（1）,4-Me2N-C₆H₄（2））,并对其进行了元素分析、IR和（¹H,¹³C和¹¹⁹Sn）NMR光谱表征。X射线晶体衍射分析表明,化合物1、2具有Sn₄O₄构筑的3个四元环梯形骨架平面结构,μ₃-O桥联2个环内梯锡和1个环梯端锡原子,此外,甲醇氧还以μ₂-O桥联1个环内梯锡和1个环梯端锡原子,锡与配基原子构成畸形三角双锥六面体。化合物对人结肠癌（HT-29）、肝癌细胞（HepG2）、乳腺癌（MCF-7）、鼻咽癌（KB）和肺癌细胞（A549）均显示出比临床使用的顺铂还强的抗癌活性。     

Ni-Co@C-N催化剂的制备及其催化硝基芳烃还原偶联反应

以ZIF-67为前驱体,通过高温热解和乙二醇还原的方法制备了低成本的负载镍纳米粒子催化剂Ni-Co@C-N。研究了催化剂对硝基芳烃化合物合成相应氧化偶氮苯类化合物的催化性能,并讨论了碱及分散剂种类对催化剂结构与性能的影响。结果表明,碱性增强可以加快纳米粒子的成核速率,分散剂分子量增加则会限制纳米粒子的粒径增长。其中,由弱碱与低分子量分散剂制备的Ni-Co@C-N-4 对硝基芳烃还原具有良好的催化活性,原料的转化率和产率在反应30 min 后分别达到92.8%和89.3%,且循环实验表明Ni-Co@C-N-4具有良好的稳定性。     

一种水中稳定的锌(Ⅱ)金属有机骨架用于检测四环素

采用溶剂热法合成1个锌(Ⅱ)金属有机骨架（Zn-MOF）：[Zn (H₂L)(4,4''-bpy)]_n (1),其中H₄L=1,1''-乙烷基联苯-3,3'',5,5''-四羧酸,4,4''-bpy=4,4''-联吡啶。通过单晶X射线衍射、元素分析和热重分析等方法对其结构进行表征。单晶结构分析表明,1属于单斜晶系C2/c空间群,H₂L^2-配体采取单齿配位模式连接Zn (Ⅱ)形成一维链,4,4''-bpy连接一维链构筑成二维波浪状网。该化合物在水中表现出良好的稳定性,并且可作为高灵敏度、高选择性荧光探针检测四环素(TET),其检出限为0.17 μmol·L^-1。1可成功用于延河水中TET的测定。此外,还研究了1对TET的荧光猝灭机理。     

3D Supramolecular Network Based on Hydrogen Bonds Between 1D [ Ni(μ2 -C2O4 ) (Him) 2 ] Zigzag Chains

Introduction In recent years, considerable attention has been paid to supramolecular networks based on metal organic building blocks because of their potential applications in diverse fields, such as, catalysis, optics, sensors,magnetism, and molecular recognition[1-3]. On the basis of the principles of crystal engineering and special synthesis strategies, several novel supramolecular frameworks have been assembled from various organic,inorganic and metal-organic moieties, which largely enriches the structure chemistry of solid state materials[4-9].     

Engineering hollow covalent organic framework particle through self-templated crystallization

Nano/microparticles with hollow structures have many applications in various fields. However, engineering hollow covalent organic framework (COF) particles in a simple and efficient manner remains a significant challenge. In this study, we propose a self-template crystallization method to prepare imine-based COF microparticles with tailored shell thickness and high crystallinity. This method involves the post-transformation of amorphous microparticles in a highly reversible reaction system containing monofunctional competitors. The amorphous precursors with high-Gibbs free energy decompose and reconstruct to form highly crystalline COFs in situ on the particle surface. This process allows for the preparation of various Schiff-base COF particles with good crystallinity and tailored hollow structures, with diameters ranging from 120 to 680 nm and shell thicknesses of 27–96 nm. These COF particles have well-defined micropore sizes centered between 1.0 and 4.0 nm and a high-specific surface area of up to 543 m² g⁻¹. The resulted COF particles could be used to support ethylene oligomerization catalysts for improving their activity and selectivity, that is, the mass fraction of α-olefins above C₁₀ increased from almost zero to 52.7% after COF supporting. Our method thus provides an efficient way to precisely tailor the microstructure and morphology of COF materials for advanced applications.     

Portfolio selection with probabilistic utility

Inspired by statistical physics, we present a probabilistic approach to portfolio selection. Instead of seeking the global extremum of some chosen utility function, we reinterpret the latter as a probability distribution of ‘optimal’ portfolios, and select the portfolio that is given by the mean value with respect to that distribution. Compared to the standard maximization of expected utility, this approach has several attractive features. First, it significantly reduces the excessive sensitivity to external parameters that often plague optimization procedures. Second, it mitigates the commonly observed concentration on too few assets; and third, it provides a natural and consistent way to account for the incompleteness of information and the aversion to uncertainty. Supportive empirical evidence is derived by using artificial data to simulate finite-sample behavior and out-of-sample performance.     

Uncertainty due to volumetric operations is often underestimated

Fifteen international titration standards were evaluated to determine minimum and maximum combined standard uncertainties. Assuming most thorough performance of the analyses revealed minimum values, whereas maximum values of uncertainty were obtained assuming that the analyses had been done under high time pressure. Minimum and maximum uncertainties were compared with the corresponding reproducibility standard deviations. Since the combined standard uncertainty is expected to lie between the reproducibility standard deviation and the maximum combined standard uncertainty, realistic standard uncertainties of individual influence quantities of volumetric and weighing procedures could be calculated. This top-down approach revealed up to 4 times higher uncertainties for volumetric operations compared to the bottom-up approach according to the current literature. Hence, uncertainty due to volumetric operations is obviously strongly underestimated. The present study additionally contains a ranking of the contributions to the uncertainty of titrimetric results.     

Case Studies for a First-Order Robust Nonlinear Programming Formulation

In this paper, we conduct three case studies to assess the effectiveness of a recently proposed first-order method for robust nonlinear programming [Zhang, Y.: J. Optim. Theory Appl. 132, 111–124 (2007)]. Three robust nonlinear programming problems were chosen from the literature using the criteria that results calculated using other methods must be available and the problems should be realistic, but fairly simple. Our studies show that the first-order method produced reasonable solutions when the level of uncertainty was small to moderate. In addition, we demonstrate a method for leveraging a theoretical result to eliminate constraint violations. Since the first-order method is relatively inexpensive in comparison to other robust optimization techniques, our studies indicate that, under moderate uncertainty, the first-order approach may be more suitable than other methods for large problems. The authors recognize funding from NSF Grants DMS-0405831 and DMS-0240058.