Ag3PO4微晶表面热力学函数的温度及形貌效应

在室温下采用离子交换法制备了四足状、 立方体状和十二面体状Ag₃PO₄微晶及Ag₃PO₄块体, 并进行了表征. 以Ag₃PO₄微/纳米和块体材料热力学性质的区别为基础, 结合化学热力学理论和热动力学基本原理, 导出摩尔表面热力学关系式. 在此基础上, 采用原位微量热技术获取Ag₃PO₄的化学反应动力学信息和表面热力学函数, 讨论了形貌和温度对表面热力学性质变化的影响. 结果表明, 四足状Ag₃PO₄的摩尔表面焓(\begin{array}{c}{H}_m^s\end{array})、 摩尔表面Gibbs自由能(\begin{array}{c}{G}_m^s\end{array})和摩尔表面熵(\begin{array}{c}{S}_m^s\end{array})最大, 立方体状次之, 十二面体状最小; \begin{array}{c}{H}_m^s\end{array}和\begin{array}{c}{S}_m^s\end{array}随温度的升高而增大, \begin{array}{c}{G}_m^s\end{array}则随温度的升高而减小.     

一种简单快速检测汞离子的1,4-二硫苏糖醇膜修饰金平板电极

将1,4-二硫苏糖醇(DTT)自组装在100 nm厚的平整金膜表面, 形成DTT膜修饰金平板电极(GPE), 构建了一种新颖的简单、 快速测定汞离子的选择性电极分析方法. 通过电化学交流阻抗和循环伏安法探讨了该电极的响应原理, 即固定在Au表面的DTT通过另一端的巯基与汞离子发生强配位作用而吸附结合带正电荷的汞离子, 引起电极表面膜电位的变化, 从而选择性地识别汞离子. 实验结果表明, 该电极在pH=6.0的Tris-HCl缓冲溶液中对汞离子有良好的电位响应性能, 其线性范围为1.0×10^-8~1.0×10^-3 mol/L, 能斯特响应斜率为(29.62±0.2) mV/-pc(25 ℃), 检出限为5.1×10^-9 mol/L. 该汞离子检测电极的响应时间仅为20 s, 且有较好的重现性和稳定性. 通过测定各种离子的选择性系数, 发现Cu²⁺, Fe²⁺, Na⁺, K⁺, Mg²⁺, Ba²⁺, Ca²⁺, Zn²⁺, Sn²⁺, Pb²⁺, Ag⁺, Al³⁺, Fe³⁺, Ni²⁺, N\begin{array}{c}{O}_2^{-}\end{array}, I\begin{array}{c}{O}_3^{-}\end{array}, Br\begin{array}{c}{O}_3^{-}\end{array}和Cl\begin{array}{c}{O}_3^{-}\end{array}等离子不干扰该电极对汞离子的检测. 此外, 将该电极用于实际水样中微量汞离子含量的测定, 结果与双硫腙分光光度方法一致, 且回收率为98.20%~101.75%.     

四硅氧烷Gemini咪唑表面活性剂的合成及表面性能

以三甲基氯硅烷、 γ-氯丙基三氯硅烷、 1,4-二氯丁烷和咪唑等为原料合成了一种新型的四硅氧烷Gemini咪唑表面活性剂([Si₄-4-Si₄im]Cl₂), 通过质谱(MS)和核磁共振氢谱(¹H NMR)证明所得产物为目标产物. 通过Wilhelmy板法测得其在25 ℃下的临界胶束浓度(cmc)为0.54 mmol/L, 水溶液的表面张力(γ_cmc)降至18.6 mN/m. 通过电导率法研究了其胶束形成热力学参数(Δ\begin{array}{c}{G}_m^{0—}\end{array},Δ\begin{array}{c}{H}_m^{0—}\end{array}和Δ\begin{array}{c}{S}_m^{0—}\end{array}), 表明在15~35 ℃下其胶束化过程是自发进行的, 且为熵驱动过程.     

SO42-掺杂对Nasicon型Li3Fe2(PO4)3正极材料电化学性能的影响

采用溶胶-凝胶法用S\begin{array}{c}{O}_4^{2-}\end{array}部分代替Li₃Fe₂(PO₄)₃中的P\begin{array}{c}{O}_4^{3-}\end{array}阴离子制得Li_3-xFe₂(PO₄)_3-x(SO₄)_x(x=00.90)正极材料, 通过X射线衍射、 充放电技术、 循环伏安特性测试及电化学阻抗谱表征了掺杂材料的相组成及电化学性能. 结果表明, S\begin{array}{c}{O}_4^{2-}\end{array}主要以固溶形式存在于Li₃Fe₂(PO₄)₃中, 产物中还伴有少量Fe₂O₃第二相析出. S\begin{array}{c}{O}_4^{2-}\end{array}掺杂使Li₃Fe₂(PO₄)₃的放电容量呈抛物线形规律变化, 并在掺杂浓度x=0.60时达到最佳值, 该样品在0.5C倍率下的首次放电容量为111.59 mA·h/g, 比未掺杂的样品提高了18.4%; 60次循环充放电后的容量保持率为96%; 将该样品的放电倍率由0.5C逐渐提高至5C, 再降至0.5C, 并在每个倍率下循环10次, 材料的最终放电容量仍能达到首次放电容量的97%. 导致这些变化的原因是S\begin{array}{c}{O}_4^{2-}\end{array}掺杂使材料的氧化还原性能增强, 电池内阻减小, 极化程度降低及Li⁺扩散系数增大.     

解草酯的合成及动力学

针对原有生产上存在过程繁琐,收率不高等缺点,以5-氯-8羟基喹啉和氯乙酸-1-甲基己基酯为起始原料,研究了解草酯的合成工艺条件,确定了反应所用的碱、反应时间以及加入的5-氯-8-羟基喹啉与氯乙酸-1-甲基己基酯和碳酸钾与氯乙酸-1-甲基己基酯的摩尔比。在解草酯合成工艺条件的基础上,进一步探讨了解草酯合成的反应动力学,确定该反应符合二级反应条件,得到了323、328、333和339 K不同温度下的反应动力学方程式-r₂=-dc2dt=1.46×10⁸e(-49.82×103/RT)c₁c₂,依据所得的不同温度下的反应速率常数,计算了该反应的活化能与频率因子,所得的反应的活化能和频率因子分别为49.82 kJ/mol和1.46×10⁸,为合成解草酯的工业放大提供了理论指导。     

一种新的二帽Keggin多金属氧酸盐的合成、表征及磁性

在水热条件下合成了一种新的二帽二支撑的Keggin型钼钒多金属氧酸盐[Cu(2,2'-bpy)₃]{[Cu(2,2'-bpy)₂]₂HP^ⅤMo8VIV6IVO₄₂]}·2H₂O(1,bpy=联吡啶),利用元素分析、红外光谱、X射线光电子能谱,粉末和X射线单晶衍射等分析技术对化合物的晶体结构进行了表征。 结果表明,该化合物属于单斜晶系,C2/c空间群,a=2.8734(3) nm,b=1.47333(12) nm,c=2.31023(16) nm,β=112.509(5)°,V=9.0351(12) nm³,Z=1,R₁=0.0489,wR₂=0.1286。 化合物的阴离子是二帽α-Keggin结构P/Mo/V多金属氧簇,通过簇阴离子两个钒帽上的端氧分别支撑两个铜过渡金属配合物,化合物簇阴离子,水分子和2,2'-联吡啶分子之间通过π-π和氢键作用形成了三维超分子结构。     

3-丁基-5,5-二甲基海因咪唑季铵盐对HCl溶液中Q235钢的缓蚀性能

利用失重法、 电化学阻抗谱法、 环境扫描电镜观测和接触角测试等研究了3-丁基-5,5-二甲基海因咪唑季铵盐(BDMHI)对HCl溶液中Q235钢的缓蚀性能和其在Q235钢表面的吸附行为. 结果显示, 缓蚀效率随BDMHI浓度的增加而增加, 随温度的升高而降低, 最高缓蚀效率为91.62%; 在25~35 ℃温度范围内, BDMHI质量浓度为1.0 g/L时, 缓蚀效率达80%以上. 测定了BDMHI在Q235钢表面吸附的吸附吉布斯自由能(\begin{array}{c}\Delta G_{\mathit{ads}}^0\end{array})和吸附热(\begin{array}{c}\Delta H_{\mathit{ads}}^0\end{array}). 结果表明, BDMHI在Q235钢表面的吸附为放热反应, 符合Langmuir等温式, 是包含物理吸附和化学吸附的混合吸附. 运用量子化学方法探究了BDMHI的缓蚀机理.     

开放骨架磷酸铁的合成与性质

通过使用二丙烯基三胺为结构导向剂, 在水热体系中合成出一例具有新型三维开放骨架结构的磷酸铁化合物JU94(\begin{array}{c}\left|2H_{3}O\right|\end{array}[Fe₂P₂O₈(OH)₂]). 单晶X射线衍射分析结果显示, 该化合物结晶在单斜晶系P2₁/c空间群, 晶胞参数a=0.97566(5) nm, b=0.98560(5) nm, c=1.24514(5) nm, β=129.651(3)°, V=0.92189(8) nm³. 该化合物的骨架结构是由FeO₆八面体和PO₄四面体连接构成, 以四核铁簇作为结构构筑单元. JU94沿[101], [\begin{array}{c}\bar{1}\end{array}01], [010]和[111]方向含有扭曲八元环孔道, 水分子分布于孔道中. 穆斯堡尔谱研究结果表明, 该结构具有2个晶体学独立的正三价铁离子. 磁性研究结果表明, 该物质具有反铁磁性.     

腐植酸作用下γ-六氯环己烷在冰相中的光转化

研究了腐植酸(HA)存在下冰相体系中γ-六氯环己烷(γ-HCH)的光转化规律. 结果表明, HA浓度对γ-HCH的光转化率呈现低浓度促进而高浓度抑制的现象; 盐离子浓度、 N\begin{array}{c}{O}_2^{-}\end{array}及N\begin{array}{c}{O}_3^{-}\end{array}对γ-HCH的光转化率均有促进作用; 低浓度Fe³⁺对γ-HCH的光转化率有促进作用, 当Fe³⁺的浓度增大到50 μmol/L时, 呈现抑制效应; γ-HCH在不同pH值条件下光转化速率的大小顺序为碱性>中性>酸性. 冰相中HA通过产生单线态氧(¹O₂)、 羟基自由基(·OH)及三重激发态(HA^*)加速γ-HCH的光转化. HA存在下γ-HCH的光转化产物主要是五氯环己烯、 邻二氯苯和对二氯苯、 一氯苯, 光转化过程中¹O₂通过消耗中间产物间接加速了γ-HCH的光转化过程.     

玫瑰红作用下γ-六氯环己烷在冰相中的光敏化降解

考察了在玫瑰红(RB)存在下γ-六氯环己烷(γ-HCH)在冰中的光降解. 结果表明, 光敏剂RB通过其激发态[RB]^*及其产生的¹\begin{array}{c}{O_2}^{*}\end{array}加速了γ-HCH的光降解, RB浓度是影响光降解率最显著的因素; γ-HCH在较低初始浓度下的光敏化降解更快; 无机盐离子的种类和浓度可以改变冰表面上类液层(LLL)的比例从而影响γ-HCH的光解. 通过分析γ-HCH光降解产物提出了RB存在时冰中γ-HCH的光降解作用机理.     

An <Emphasis Type="BoldItalic">in silico</Emphasis> approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

Summary P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure–activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 ± 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.     

Quantification of isoorientin and total flavonoids in Passiflora edulis fruit pulp by HPLC-UV/DAD

A method is reported for the quantification of isoorientin (using a standard addition method) and total flavonoids (expressed as rutin, using the external standard method) in passion fruit pulp (Passiflora edulis Sims f. flavicarpa Degener, Passifloraceae). Extraction of flavonoids was optimized by experimental design methodology, and quantitative analysis was performed by high-performance liquid chromatography with photo-diode array detection (HPLC-UV/DAD). The method was developed and validated according to ICH requirements for specificity, linearity, accuracy, precision (repeatability and intermediate precision), LOD and LOQ. Rutin was chosen as standard for the quantification of total flavonoids in order to propose a HPLC method feasible for routine analysis of the flavonoids in the passion fruit pulp. The passion fruit pulp contained 16.226 ± 0.050 mg L^− 1 of isoorientin and 158.037 ± 0.602 mg L^− 1 of total flavonoid, suggesting that P. edulis fruits may be comparable with other flavonoid food sources such as orange juice or sugarcane juice.     

Differentiation of organic and non-organic ewe's cheeses using main mineral composition or near infrared spectroscopy coupled to chemometric tools: a comparative study

Two independent methodologies were investigated to achieve the differentiation of ewes’ cheeses from different systems of production (organic and non-organic). Eighty cheeses (40 organic and 40 non-organic) from two systems of production, two different breeds of ewe, different sizes, seasons (summer and winter) and ripening times up to 9 months were elaborated. Their mineral composition or the information provided by their spectra in the near infrared zone (NIR) coupled to chemometric tools were used in order to differentiate between organic and non-organic cheeses. Main mineral composition (Ca, K, Mg, Na and P) of cheeses and stepwise lineal discriminant analysis were used to develop a discriminant model. The results from canonical standardised coefficients indicated that the most important mineral was Mg (1.725) followed by P (0.764) and K (0.742). The percentage of correctly classified samples was 88% in internal validation and 90% in external validation, selecting Mg, K and P as variables.Spectral information in the NIR zone was used coupled to a discriminant analysis based on a regression by partial least squares in order to obtain a model which allowed a rate of samples correctly classified of 97% in internal validation and 85% in external validation.     

Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis

Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%. Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA.     

Comparative studies on some metrics for external validation of QSPR models

Quantitative structure-property relationship (QSPR) models used for prediction of property of untested chemicals can be utilized for prioritization plan of synthesis and experimental testing of new compounds. Validation of QSPR models plays a crucial role for judgment of the reliability of predictions of such models. In the QSPR literature, serious attention is now given to external validation for checking reliability of QSPR models, and predictive quality is in the most cases judged based on the quality of predictions of property of a single test set as reflected in one or more external validation metrics. Here, we have shown that a single QSPR model may show a variable degree of prediction quality as reflected in some variants of external validation metrics like Q2(F1), Q2(F2), Q2(F3), CCC, and r2(m) (all of which are differently modified forms of predicted variance, which theoretically may attain a maximum value of 1), depending on the test set composition and test set size. Thus, this report questions the appropriateness of the common practice of the "classic" approach of external validation based on a single test set and thereby derives a conclusion about predictive quality of a model on the basis of a particular validation metric. The present work further demonstrates that among the considered external validation metrics, r2(m) shows statistically significantly different numerical values from others among which CCC is the most optimistic or less stringent. Furthermore, at a given level of threshold value of acceptance for external validation metrics, r2(m) provides the most stringent criterion (especially with Δr2(m) at highest tolerated value of 0.2) of external validation, which may be adopted in the case of regulatory decision support processes.     

三苯胺类染料敏化太阳能电池能量转化效率的全息定量构效关系研究

研究构建了三苯胺类化合物分子结构与相应染料敏化太阳能电池能量转化效率(PCE)之间的全息定量构效关系(HQSAR)模型。当fragment distinction、fragment size、hologram length和principal components分别为“C、DA”、“4-7”、“199”和“6”时,可以获得最优HQSAR模型。采用外部测试集验证和留一交叉验证对所建立模型进行检验,外部测试集验证中CCC和 Q2F3分别为0.933和0.892,留一交叉验证中其q2cv和r2分别为0.791和0.902,表明所建立模型具有较好的拟合效果和预测能力。通过所建立HQSAR模型的分子贡献图可知,环戊并二噻吩基团的存在有利于提高PCE值,长烷基链的存在可能降低PCE值。