Hydrogel nanonetworks with functional core-shell structure

Nanohydrogel particles of poly(acrylonitrile-co-N-isopropylacrylamide (p(AN-c-NIPAM)) were synthesized using a microemulsion polymerization technique. Highly monodisperse nanohydrogel particles e.g. 50-150 nm, and various morphology such as core-shell and connected beads were obtained. It was shown that the shell thickness and the size of particles can be tuned by the monomer concentrations and their ratios as well as by the utilization of different crosslinkers. The hydrophobic core monomer, AN was converted to amidoxime groups to increase the hydrophilicity of the nanogels which provide more hydrophilic character and impart new functionality to the nanonetwork. Transmission electron microscopy (TEM), and dynamic light scattering (DLS) techniques were employed for the particle size characterizations. The amidoximation reaction was confirmed by FT-IR spectroscopy.     

卤代化合物的碳—13化学位移模拟

用人工神经网络和多元回归方法对含２个碳的２１个卤代化合物的３５个化学位移进行计算机图像模拟，结果表明，人工神经网络方法优于多元回归方法，同时此种方法处理这类问题有明显的优势，波谱模拟技术在有机化合物结构解析中是非常有用的方法。     

人工神经网络方法预测气相色谱保留值

本文运用一典型的人工神经网络模型－“反向传播“模型的改进形式，研究了诱导效应指数Ｉ，摩尔折射度Ｒｏ，疏水亲脂参数ＩｇＰ，以及分子联通性指数与气象色谱保留行为的关系，实现了对色谱保留植的预测。神经网络预测模型的最大相对误差不超过８．７％。结果表明，该方法性能良好，可望成为色谱保留值预测的有效手段。     

PAA/PVA SIPN高吸水性树脂的制备及性能研究

采用水溶液法合成了聚丙烯酸-聚乙烯醇(PAA-PVA)半互传网络型高吸水性树脂,其工艺条件的优化结果为:丙烯酸(AA)与PVA质量比为10∶0.5,AA中和度为80%,加入AA单体质量0.14%的引发剂和0.025%的交联剂,产物吸水率为530g/g,吸盐水率达63g/g。并采用红外光谱法表征了半互穿网络结构的形成。     

{Cu[H_2NC(CH_2OH)_3]_2}{Pic}_2·H_2O的合成与晶体结构

三羟甲基氨基甲烷与苦味酸(Pic)铜在乙醇水混合液中反应,制得少见的不对称氢键连接的超分子化合物{Cu[H2NC(CH2OH)3]2}{Pic}2?H2O。晶体属三斜晶系,空间群为P ,晶胞参数为a = 6.894(1),b = 8.219(1),c = 13.543(3) ? ?= 79.290(1),?= 8 3.36(2), ?= 84.590(1)埃轿狢20H30CuN8O22,V = 746.9(2) 3,Z = 1,Dc = 1.774 g/cm3,F(000) = 411,?= 0.844 mm-1,R = 0.0334,Rw = 0.0863。在配合物的结构单元中, Cu2+位于对称中心,分别与2个四齿配体(三羟甲基氨基甲烷)中的2个OH,1个NH2,三齿配位,呈笼状螯合。而另一个OH,因配体和中心离子构型的限制,不参与配位。     

用人工神经网络处理谷物成分分析

本文用人工神经网络处理谷物的付立叶变换近红外漫反射光谱,对谷物中含量在10~(-1)～10~(-3)的蛋白质、脂肪和6种人体必需氨基酸定量分析数据进行了解析,分析结果与经典化学方法没有系统偏差,且优于逐步回归分析法的结果。     

人工神经网络及其在分析化学中的应用

人工神经网络是一种新兴的计算方法，有着广阔的发展前途，目前在分析化学领域已经有了多方面的应用。本文简要介绍了人工神经网络的原理及其在分析化学中的应用。     

新颖的二维超分子化合物[CoⅢ(aea)(2,2''''-bipy)]Cl·H2O的水热合成及结构表征

0IntroductionInrecentyearsgreatattentionhasbeenpaidtosupramolecularchemistryandawidevarietyofnet鄄workshavealreadybeenconstructedbasedupontheprincipleofcrystalengineering[1~3].Usually,variousmultifunctionalorganicligands,bothrigidandflexiblemulticarboxyli…     

Networks and chain coverings in partial orders and their products

For an arbitrary poset P, subposets {P _i : 1ik} form a transitive basis of P if P is the transitive closure of their union. Let u be the minimum size of a covering of P by chains within posets of the basis, s the maximum size of a family of elements with no pair comparable in any basis poset, and a the maximum size of an antichain in P. Define a dense covering to be a collection D of chains within basis posets such that each element belongs to a chain in D within each basis poset and is the top of at least k-1 chains and the bottom of at least k-1 chains in D. Dense coverings generalize ordinary chain coverings of poset. Let d=min {|D|–(k–1)|P|}. For an arbitrary poset and transitive basis, a convenient network model for dense coverings yields the following: Theorem 1: da, with equality iff P has a minimum chain decomposition in which every pair of consecutive elements on each chain are comparable in some basis poset. Theorem 2: usda. Theorem 3: s=d iff s=a. The most interesting special case is where the transitive basis expresses P as the product of two posets, in which case u and s measure the minimum and maximum sizes of unichain coverings and semiantichains.     

A neuro-fuzzy system for X-ray spectra interpretation

A decision scheme for the interpretation of spectra from wavelength dispersive X-ray fluorescence spectrometry is described that encompasses elements from three areas of artificial intelligence: fuzzy logic, rule based expert systems and neural net technology.After transforming the recorded spectra to line spectra by appropriate background correction a reasoning scheme is applied that takes into account not only the observed spectra, but also the recording conditions and prior spectroscopic information regarding the relative emission probabilities and the usefulness of the different lines for the purpose of element identification. The latter is done on the basis of a previously described scheme to compute conditional a posteriori Bayes probabilities for a mean matrix. These different pieces of information are then assembled into a battery of fuzzy rules. The importance of the rules as well as the importance of the X-ray lines is determined in a training process, similar to the one in a feedforward back-propagation network.To further stabilize the results this network is pruned in a second training cycle. This, however, had little effect on the quality of interpretation.The advantages of this approach to the interpretation of X-ray spectra over older ones are numerous: the system adapts itself to better interpret spectra that are of greater importance to a laboratory as these are better represented in the training set; the fuzzy logic is capable of working with incomplete and uncertain knowledge, and the neural network results based on these fuzzy rules is readily interpretable by the X-ray spectroscopist as every rule can be expressed also in natural language as in any classical rule based system.On leave from Silesian University, Katowice, Poland