对-叔丁基杯[8]芳烃键合硅胶制备及其毛细管电色谱性能研究

以γ-(环氧丙氧)丙基键合硅胶为前体,于硅胶表面键合环氧基,在催化剂存在下以杯芳烃钠盐开环制备杯芳烃键合硅胶固定相.该方法反应条件温和,适用性强.将这个新方法首次用于制备对-叔丁基杯[8]芳烃电色谱键合固定相(C8BS),采用加压电色谱初步评价其电色谱性能.研究结果表明,C8BS电渗流(Electrosmoticflow,EOF)较小,但通过控制键合反应及使用压力辅助电色谱可部分弥补上述不足.该固定相的EOF受流动相pH影响小(pH=3-8),同时大环配体屏蔽效应能有效地克服硅羟基引起的碱性化合物拖尾现象,这对电色谱分离具有重要意义.通过分步封尾研究EOF的来源发现,杯芳烃酚羟基对EOF有弱的贡献,这与报道的杯芳烃涂层具有径向电渗流调控能力相一致.     

蒙药新-Ⅱ中微量硒的测定

采用硒 -硫氰酸钾 -罗丹明 B-明胶 - OP体系分光光度法测定蒙药新 - 中微量硒。其含量为 6 .6—7.3μg· g-1 ,回收率为 94 %— 95 %。     

天然磷脂的色谱法分离、纯化研究进展

天然磷脂是一类含磷酸根的类脂化合物的总称,在医药学、营养学、化妆品和生命科学领域等有重要的应用价值。本文综述了不同原料样品中磷脂种类和其各分子的色谱法分离、纯化。着重讨论高效液相色谱法分离技术及其影响因素。最后概述了有助于提高分离选择性、检测灵敏度的化学衍生化方法。     

Accurate redox potentials of mononuclear iron, manganese, and nickel model complexes*

Density functional theory (DFT) was combined with solution of the Poisson equation for continuum dielectric media to compute accurate redox potentials for several mononuclear transition metal complexes (TMCs) involving iron, manganese, and nickel. Progress was achieved by altering the B3LYP DFT functional (B4(XQ3)LYP-approach) and supplementing it with an empirical correction term G(X) having three additional adjustable parameters, which is applied after the quantum-chemical DFT computations. This method was used to compute 58 redox potentials of 48 different TMCs involving different pairs of redox states solvated in both protic and aprotic solvents. For the 58 redox potentials the root mean square deviation (RMSD) from experimental values is 65 mV. The reliability of the present approach is also supported by the observation that the energetic order of the spin multiplicities of the electronic ground states is fulfilled for all studied TMCs, if the influence from the solvent is considered as well.     

A tris(pyrazolyl) eta6-arene ligand that selects Cu(I) over Cu(II)

1,3,5-Tris{2'-[(pyrazol-1-yl)methyl]phenyl}benzene, 4, and its complexes with Cu(I) and Ag(I) have been prepared and characterized. Both CuI4 and AgI4 triflate crystallize in the rhombohedral space group R3, with the cations and anions each exhibiting crystallographically imposed 3-fold (C3) symmetry. In both complexes, 4 behaves as a tris(pyrazolyl) eta6-arene ligand whose arms act as three-pronged tweezers to form chiral, propeller-like cations with pyramidal MN(pyrazole)3 coordination geometries. Centers of symmetry in the space group ensure that the crystals are racemates, with equal numbers of P,P,P and M,M,M enantiomers. In broad outline, each cation is shaped like a three-legged stool, with the metal ion centered at the top and pointed downward from a triangular N(pyrazole) plane toward the center of gravity (Cg) of the central benzene ring (a metal-endo conformation), which constitutes the bottom shelf of the stool. The Cu(I)...Cg and Ag(I)...Cg distances, 3.195(2) and 3.165(2) A, respectively, support the existence of an eta6 bonding interaction with Ag(I) and, to a lesser extent, with Cu(I). NMR data for AgI4 suggest rapid interconversion of this cation in solution between P,P,P and M,M,M enantiomers. Our inability to prepare any Cu(II) complexes with 4 is consistent with cyclovoltammetric results, which suggest that the ligand is more easily oxidized than Cu(I).     

基于集合卡尔曼变换的东中国海声学 敏感区判定方法*

为改善海洋与水声环境预报质量,针对常规观测成本高、资料利用率低等问题,将适应性观测方法应用于海洋声学领域。结合海洋-声学耦合模式与集合卡尔曼转换敏感区诊断方法,以东中国海宫古海峡北部区域为验证区,计算并分析不同条件下海洋环境敏感区与声学敏感区分布,通过观测系统模拟试验验证适应性观测对验证区预报的提升效果。结果表明,两种敏感区位置随时间间隔增加均向验证区上游移动,海洋环境敏感区相比于声学敏感区分布更为集中,且平移特征更明显;对海洋环境敏感区和声学敏感区添加适应性观测均能提升海洋与水声环境的预报质量,提升效果随时间间隔增加而减小,在某种类型敏感区的适应性观测对相对应参数的预报质量提升效果优于对其他类型敏感区进行观测的效果。     

Protein secondary structure prediction with SPARROW

A first step toward predicting the structure of a protein is to determine its secondary structure. The secondary structure information is generally used as starting point to solve protein crystal structures. In the present study, a machine learning approach based on a complete set of two-class scoring functions was used. Such functions discriminate between two specific structural classes or between a single specific class and the rest. The approach uses a hierarchical scheme of scoring functions and a neural network. The parameters are determined by optimizing the recall of learning data. Quality control is performed by predicting separate independent test data. A first set of scoring functions is trained to correlate the secondary structures of residues with profiles of sequence windows of width 15, centered at these residues. The sequence profiles are obtained by multiple sequence alignment with PSI-BLAST. A second set of scoring functions is trained to correlate the secondary structures of the center residues with the secondary structures of all other residues in the sequence windows used in the first step. Finally, a neural network is trained using the results from the second set of scoring functions as input to make a decision on the secondary structure class of the residue in the center of the sequence window. Here, we consider the three-class problem of helix, strand, and other secondary structures. The corresponding prediction scheme "SPARROW" was trained with the ASTRAL40 database, which contains protein domain structures with less than 40% sequence identity. The secondary structures were determined with DSSP. In a loose assignment, the helix class contains all DSSP helix types (α, 3-10, π), the strand class contains β-strand and β-bridge, and the third class contains the other structures. In a tight assignment, the helix and strand classes contain only α-helix and β-strand classes, respectively. A 10-fold cross validation showed less than 0.8% deviation in the fraction of correct structure assignments between true prediction and recall of data used for training. Using sequences of 140,000 residues as a test data set, 80.46% ± 0.35% of secondary structures are predicted correctly in the loose assignment, a prediction performance, which is very close to the best results in the field. Most applications are done with the loose assignment. However, the tight assignment yields 2.25% better prediction performance. With each individual prediction, we also provide a confidence measure providing the probability that the prediction is correct. The SPARROW software can be used and downloaded on the Web page http://agknapp.chemie.fu-berlin.de/sparrow/ .     

The role of group 14 element hydrides in the activation of C-h bonds in cyclic olefins

Formally, triple-bonded dimetallynes ArEEAr [E = Ge (1), Sn (2); Ar = C(6)H(3)-2,6-(C(6)H(3)-2,6-(i)Pr(2))(2)] have been previously shown to activate aliphatic, allylic C-H bonds in cyclic olefins, cyclopentadiene (CpH), cyclopentene (c-C(5)H(8)) and 1,4-cyclohexadiene, with intriguing selectivity. In the case of the five-membered carbocycles, cyclopentadienyl species ArECp [E = Ge (3), Sn (4)] are formed. In this study, we examine the mechanisms for activation of CpH and c-C(5)H(8) using experimental methods and describe a new product found from the reaction between 1 and c-C(5)H(8), an asymmetrically substituted digermene ArGe(H)Ge(c-C(5)H(9))Ar (5), crystallized in 46% yield. This compound contains a hydrogenated cyclopentyl moiety and is found to be produced in a 3:2 ratio with 3, explaining the fate of the liberated H atoms following triple C-H activation. We show that when these C-H activation reactions are carried out in the presence of tert-butyl ethylene (excess), compounds {ArE(CH(2)CH(2)tBu)}(2) [E = Ge(8), Sn(9)] are obtained in addition to ArECp; in the case of CpH, the neohexyl complexes replace the production of H(2) gas, and for c-C(5)H(8) they displace cyclopentyl product 5 and account for all the hydrogen removed in the dehydroaromatization reactions. To confirm the source of 8 and 9, it was demonstrated that these molecules are formed cleanly between the reaction of (ArEH)(2) [E = Ge(6), Sn(7)] and tert-butyl ethylene, new examples of noncatalyzed hydro-germylation and -stannylation. Therefore, the presence of transient hydrides of the type 6 and 7 can be surmised to be reactive intermediates in the production of 3 and 4, along with H(2), from 1 and 2 and CpH (respectively), or the formation of 3 and 5 from 1. The reaction of 6 or 7 with CpH gave 3 or 4, respectively, with concomitant H(2) evolution, demonstrating the basic nature of these low-valent group 14 element hydrides and their key role in the 'cascade' of C-H activation steps. Additionally, during the course of these studies a new polycyclic compound (ArGe)(2)(C(7)H(12)) (10) was obtained in 60% yield from the reaction of 1,6-heptadiene and 1 via double [2 + 2] cycloaddition and gives evidence for a nonradical mechanism for these types of reactions.