Prediction of Complexation Properties of Crown Ethers Using Computational Neural Networks

A computational neural network method was used for the prediction of stability constants of simple crown ether complexes. The essence of the method lies in the ability of a computer neural network to recognize the structure-property relationships in these host-guest systems. Testing of the computational method has demonstrated that stability constants of alkali metal cation (Na+, K+, Cs+)-crown ether complexes in methanol at 25 °C can be predicted with an average error of ±0.3 log K units based on the chemical structure of the crown ethers alone. The computer model was then used for the preliminary analysis of trends in the stabilities of the above complexes.     

基于支持向量机的羰基化合物红外光谱研究

设计了一个4层分等级分类系统，通过支持向量机技术对羰基化合物的红外光谱成功地进行了信息分类和提取，并与人工神经网络进行比较，结果表明：支持向量机对羰基类化合物红外光谱分类效果优于人工神经网络；另外详细探讨了表征酰胺类化合物的特征光谱峰片段对识圳伯仲叔酰胺的影响，证明了酰胺的N-H伸缩振动峰对识别伯仲叔酰胺贡献最大，为构建红外光谱智能解析系统提供了定量依据。     

人工神经元网络辅助丙烷氨氧化催化剂设计

报道了利用人工神经元网络辅助丙烷氨氧化制丙烯腈催化剂设计的研究进展结果．通过筛选得到了可以拟合该反应体系的神经元网络模型，从而使丙烷转化率和丙烯腈选择性可以表示为催化剂组成的函数，利用培训后的神经元网络模型所得到的权值文件，结合作者自编的优化程序，该网络可用于预测最优的催化剂组成．实验得到丙烯腈收率和选择性可分别达到４３．０％和５６．２４％．     

神经网络法用于多元混酸同时测定

利用多层神经网络误差反向传播算法处理酸碱电位滴定数据，求得出多元混合酸各组分的浓度，优化了神经网络的结构和参数，测定了三组分有机酸混合样品，结果良好，平均相对偏差ＲＳＤ≤４％。     

Interpenetrating polymer networks of poly(carbonate-urethane) and polyvinyl pyridine

Simultaneous interpenetrating polymer networks (IPN's), pseudo IPN's, and liner blends of aliphatic poly(carbonate-urethane) (PCU) and polyvinyl pyridine (PVP) have been prepared and characterized by DSC, DMA, and TEM. The full IPN's of PCU and PVP had a single phase morphology only above 50 wt % PCU, as determined by both DSC and DMA and confirmed by transmission electron microscopy (TEM). However, in both pseudo IPN's of PCU and PVP and in their linear blends there exist multiple glass transitions and melting points seen by DSC and DMA indicating phase incompatibility. The full IPN's exhibited superior ultimate mechanical properties and solvent resistance as compared to the pseudo IPN's, liner blends, and the pure crosslinked PCU and PVP networks.     

Modeling of the relationship between electroosmotic flow and separation parameters in capillary zone electrophoresis using artificial neural networks and experimental design

The prediction of migration time of electroosmotic flow (EOF) marker was achieved by applying artificial neural networks (ANN) model based on principal component analysis (PCA) and standard normal distribution simulation to the input variables. The voltage of performance, the temperature in the capillary, the pH and the ionic strength of background electrolytes (BGE) were applied as the input variables to ANN. The range of the performance voltage studied was from 15 to 27 kV, and that of the temperature in the capillary was from 20 to 30 °C. For the pH values studied, the range was from 5.15 to 8.04. The range of the ionic strength investigated in this paper was from 0.040 to 0.097. The prediction abilities of ANN with different pre-processing procedure to the input variables were compared. Under the same performance conditions, the average prediction error of the migration time of the EOF marker was 5.46% with RSD = 1.76% according to 10 parallel runs of the optimized ANN structure by the proposed approach, and that of the 10 parallel predictions of the optimal ANN structure for the different performance conditions was 12.95% with RSD = 2.29% according to the proposed approach. The study showed that the proposed method could give better predicted results than other approaches discussed.     

Anion dependence of crown ether selectivities for alkali picrates and sulfonates in dioxane and toluene. A synergistic effect

The binding constants,K _N, of sodium and potassium 8-anilinonaphthalene-1-sulfonate (ANS) and of sodium 5-dimethylamino-1-naphthalenesulfonate (DNS) to benzo-18-crown-6 bound to a 2% cross-linked polystyrene network (RN18C6) were measured spectrophotometrically in dioxane and the results compared with those obtained for picrate salts. The network RN18C6 was then used to measure in dioxane and toluene by a competition method the equilibrium constant,K, of the reaction A^–M⁺N+CrA^–M⁺Cr+N.A^–M⁺N denotes the ionic solute (ANS, DNS, methyl orange or picrate salt) bound to the network RN18C6 (N) and A^–M⁺Cr is the solute bound to a soluble ligand Cr, where Cr represents a series of 18-crown-6 and 15-crown-5 compounds. Combining theK _N andK values the formation constants,K _L, of the crown ether complexes of the respective salts were obtained in dioxane. The data show a reversal in the complexation strength of the 18-crown-6 compounds in dioxane when sodium picrate is replaced by sodium ANS. The results were rationalized in terms of a synergistic effect exerted by dioxane, with dioxane forming a 1:1 dioxanate with the crown ion pair complex. This effect is especially strong with ANS and with a rigid planar crown ether like dibenzo-18-crown-6. The binding constants,K _N, of NaANS and NaDNS to RN18C6 in dioxane are nearly three times larger than for sodium picrate, and the same holds for the potassium salts. Differences in anion interactions with the network appear to be a plausible cause for the anion dependence ofK _N.     

Self-assembled,wormlike-cylinder network of polystyrene-block-poly(ethylene oxide) micelles in solution

We report the formation of a highly entangled and interconnected, self-assembled, wormlike-cylinder network of polystyrene-block-poly(ethylene oxide) in N, N-dimethylformamide/water. In this system, N,N-dimethylformamide was a common solvent and water was a selective solvent for the poly(ethylene oxide) blocks. The degrees of polymerization of the polystyrene and poly(ethylene oxide) blocks were 962 and 227, respectively. The network was formed at copolymer concentrations higher than 0.4 wt % and consisted of self-assembled, wormlike cylinders that were interconnected by Y-shaped, T-shaped, and multiple junctions. The network morphology was visualized with transmission electron microscopy. Capillary viscometry measurements revealed an order-of-magnitude increase in the inherent viscosity of the colloidal system upon the formation of the network. A similar effort to obtain a wormlike-cylinder network in an N,N-dimethylformamide/acetonitrile system, in which acetonitrile was a selective solvent for the poly(ethylene oxide) blocks, was unsuccessful even at high copolymer concentrations; instead, the wormlike cylinders showed a tendency to align. The viscosity measurements also did not show a substantial increase in the inherent viscosity. Thus, the solvent played a critical role in determining the formation of the self-assembled, wormlike-cylinder network. This formation of the network resulted from an interplay between the end-capping energy, bending energy (curvature), and configurational entropy of the self-assembled, wormlike-cylinder micelles that minimized the free energy. © 2006 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 44: 3605–3611, 2006     

压力和温度对4-甲基二苯并噻吩和二苯并噻吩加氢脱硫反应的影响

研究了4-甲基二苯并噻吩(4-MDBT)和二苯并噻吩(DBT)在CoMo/γ-Al2O3上的加氢脱硫反应产物分布及其可能的反应网络,通过反应压力和温度对产物分布影响的研究,揭示了加氢脱硫反应的可能机理。研究发现4-MDBT在CoMo/γ-Al2O3上的加氢脱硫反应主要通过直接氢解路径和加氢路径进行,且两者反应速率相当；DBT在 CoMo/γ-Al2O3上的加氢脱硫反应主要通过直接氢解路径进行。4-MDBT分子位于4位的甲基阻碍其在催化剂表面通过硫原子的端连吸附,从而降低了其直接氢解脱硫路径的反应速率,因而也降低了其总的加氢脱硫转化率。反应压力降低,DBT和4-MDBT加氢脱硫反应中加氢路径反应速率明显下降,而其对氢解路径影响较小,但效果却与加氢路径相反,反应压力对4-MDBT转化率的影响大于DBT。反应温度对DBT和4-MDBT加氢脱硫反应中加氢路径和氢解路径都有明显影响,但是对DBT加氢脱硫反应中氢解路径的影响小于加氢路径,而对4-MDBT加氢脱硫反应中氢解路径的影响稍高于加氢路径,4-MDBT分子中甲基的供电子作用有利于相连苯环的加氢反应。     

Optimization of the Microwave-Assisted Saponification and Extraction of Organic Pollutants from Marine Biota Using Experimental Design and Artificial Neural Networks

Several improvements in sample pretreatment for the determination of organic pollutants (i.e. n-alkanes, polycyclic aromatic hydrocarbons, PAHs) in marine biota (mussels) are presented. The use of liquid nitrogen and homogenization of the samples are shown to be an alternative to the time consuming liophilization step required for the analysis of biota samples. Microwave-assisted hydrolysis and extraction are combined to isolate organic pollutants (19 n-alkanes and 27 PAHs) from biota matrices. Experimental design (ED) and artificial neural networks (ANNs) were used to optimize the experimental conditions. NIST-CRM 2978 was used to test the validity of the developed method which shows a good agreement with certified values.