ATRP法合成两亲的含氟柱状刷PBIEM-g-(PAA-b-PFA)

以聚甲基丙烯酸[2-(2-溴异丁酰氧)]乙酯(PBIEM)为大分子引发剂,采用接出(grafting from)原子转移自由基聚合(ATRP)技术合成了以聚丙烯酸叔丁酯-b-聚含氟丙烯酸酯为侧链的柱状分子刷PBIEM-g-(PtBA-b-PFA).通过GPC,1H-NMR和FTIR对PBIEM-g-(PtBA-b-PFA)组成和结构进行了表征,证实ATRP过程中没有发生分子间或分子内偶合反应,制备得到可控性好的含氟嵌段共聚物刷.利用大分子链中叔丁酯基团的水解反应生成两亲的含氟柱状刷PBIEM-g-(PAA-b-PFA),原子力显微镜可直接观察到PBIEM-g-(PAA-b-PFA)特征的核壳型柱状结构,得到聚合物刷的整体长度为ln=54~72 nm.     

基于同一天然手性源(4R)-羟基脯氨酸的(2S,4S)/(2S,4R)-4-氟谷氨酸的立体专一性合成

基于同一天然手性源(4R)-羟基脯氨酸,通过Mitsunobo反应、全氟丁基磺酰氟作用下的氟化反应以及NaIO4/RuO2作用下的氧化反应,立体专一性地合成了(2S,4S)/(2S,4R)-4-氟谷氨酸.     

微乳法制备Au／Fe2O3水煤气变换反应催化剂 Ⅰ．制备参数对催化剂活性的影响

 分别在TritonX－100／正己醇／环己烷／水和十六烷基三甲基溴化铵（CTAB）／正己醇／水的W／O型微乳液体系中合成了Au／Fe2O3催化剂，考察了主要制备参数对催化剂水煤气变换活性的影响．结果表明，催化剂的焙烧温度、水与表面活性剂的质量比（rw）、表面活性剂浓度（W）、表面活性剂种类及催化剂活性组分金的负载量均对催化剂活性有显著的影响．催化剂的最佳焙烧温度为250℃，催化活性随着rw和W的增加而降低，由TritonX－100制得的催化剂的活性高于由CTAB制得的催化剂．当金负载量为3％，水煤气变换反应温度为200℃时，CO的转化率可达99．5％．     

Separation and simultaneous determination of four artificial sweeteners in food and beverages by ion chromatography

In this paper, the separation and determination of four artificial sweeteners (aspartame, sodium cyclamate, acesulfame-K and sodium saccharin) by ion chromatography coupled with suppressed conductivity detector is reported. The four artificial sweeteners were separated using KOH eluent generator. Due to the use of eluent generator, very low conductance background conductivity can be obtained and sensitivity of sweeteners has been greatly improved. Under the experimental condition, several inorganic anions, such as F-, Cl-, NO3-, NO2-, Br-, SO4(2)-, PO4(3)- and some organic acid such as formate, acetate, benzoate, and citrate did not interfere with the determination. With this method, good linear relationship, sensitivity and reproducibility were obtained. Detection limits of aspartame, sodium cyclamate, acesulfame-K, sodium saccharin were 0.87, 0.032, 0.019, 0.045 mg/L, respectively. Rate of recovery were between 98.23 and 105.42%, 99.48 and 103.57%, 97.96 and 103.23%, 98.46 and 102.40%, respectively. The method has successfully applied to the determination of the four sweeteners in drinks and preserved fruits.     

Does deprotonated benzoic acid lose carbon monoxide in collision-induced dissociation?

Decarboxylation is known to be the major fragmentation pathway for the deprotonated carboxylic acids in collision-induced dissociation (CID). However, in the CID mass spectrum of deprotonated benzoic acid (m/z 121) recorded on a Q-orbitrap mass spectrometer, the dominant peak was found to be m/z 93 instead of the anticipated m/z 77. Based on theoretical calculations, ¹⁸O-isotope labeling and MS³ experiments, we demonstrated that the fragmentation of benzoate anion begins with decarboxylation, but the initial phenide anion (m/z 77) can react with trace O₂ in the mass analyzer to produce phenolate anion (m/z 93) and other oxygen-containing ions. Thus oxygen adducts should be considered when annotating the MS/MS spectra of benzoic acids.     

Complete synchronization on multi-layer center dynamical networks

In this paper, complete synchronization of three-layer center networks is studied. By using linear stability analysis approach, several different coupling schemes of three-layer center networks with the Logistic map local dynamics are discussed, and the stability conditions for synchronization are illustrated via some examples.     

Intramolecular transacylation: fragmentation of protonated molecules via ion‐neutral complexes in mass spectrometry

An intramolecular transacylation reaction was observed in the mass spectrometry of molecules containing both benzoyl and carboxymethyl groups on an aromatic heterocyclic core. The reaction is triggered by a dissociative protonation on the heterocyclic ring at the atom (carbon or nitrogen) that bonds to the benzoyl group, leading to an intermediate ion‐neutral complex. The incipient benzoyl cation in the complex migrates to attack the carboxyl group of the neutral partner at the carbonyl or hydroxyl oxygen under thermodynamic or kinetic control, respectively. Elimination of benzoic acid followed by loss of carbon monoxide takes place as a result of the transacylation. Copyright © 2009 John Wiley & Sons, Ltd.     

含金属高聚物材料

综述含金属高聚物及其复合材料的制备方法，并简介这些材料所具有的导电，液晶，非线性光学等特殊性能及其应用前景。     

Atomic force microscopy study of self-organization of chiral azobenzene derived from amino acid

A kind of chiral azobenzene amphiphile, N-[4-(4-dodecyloxyphenylazo)benzoyl]- -glutamic acid (C₁₂-Azo- -Glu; as shown in Fig. 1), was synthesized and the self-organization properties on solid substrates were investigated. C₁₂-Azo- -Glu underwent a reversible trans–cis photoisomerization in dilute solution. While the photoisomerization was suppressed on solid substrate because of the H-aggregation, indicating the formation of compact film. When C₁₂-Azo- -Glu was cast from ethanol solution onto the hydrophilic surface of mica, a stable flat-layered structure formed spontaneously in large scale. High-resolution images allowed the identification of the relative orientation of molecular rows in the ordered thin film and the crystal lattice of mica. A molecular packing model of the layered structure was proposed. There was a template effect of mica to the self-organization process. Hydrogen bonding, π–π interaction and the chiral center in the molecule played the important roles in the self-organization process. The cooperative competitive effect between them led to the highly ordered structure.     

A New Expectation Identity and Its Application in the Calculations of Predictive Powers Assuming Normality

For calculating the predictive powers, we suggest an elegant expectation identity to directly calculate the expectations. We calculate the predictive powers of the hypotheses with a nonzero threshold for five different categories, which are non-sequential trials with classical power and Bayesian power, and sequential trials with hybrid predictions, Bayesian predictions, and classical predictions. Moreover, the calculations of the various predictive powers are illustrated through three examples. Finally, when calculating the average success probability in \ncite{9}, it is tricky to find the predictive distribution for the predictive power, whereas, it is straightforward to utilize the expectation identity for the calculation.