Application of an artificial neural network (ANN) and piezoelectric chemical sensor array for identification of volatile organic compounds

A piezoelectric chemical sensor array was developed using four quartz crystals. Gas chromatographic stationary phases were used as sensing materials and the array was connected to an artificial neural network (ANN). The application of the ANN method proved to be particularly advantageous if the measured property (mass, concentration, etc.) should not be connected exactly to the signal of the transducers of the piezoelectric sensor. The optimum structure of neural network was determined by a trial and error method. Different structures were tried with several neurons in the hidden layer and the total error was calculated. The optimum values of primary weight factors, learning rate (η=0.15), momentum term (μ=0.9), and the sigmoid parameter (β=1) were determined. Finally, three hidden neurons and 900 training cycles were applied. After the teaching process the network was used for identification of taught analytes (acetone, benzene, chloroform, pentane). Mixtures of organic compounds were also analysed and the ANN method proved to be a reliable way of differentiating the sensing materials and identifying the volatile compounds.     

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.     

气相[（SiO2）nX]^—团簇产生机理

利用ＸｅＣｌ准分子激光烧蚀多种硅氧多孔结构材料，在负离子通道测得丰富的「（ＳｉＯ２）ｎＸ」＾－负离子团簇，并讨论了多孔网结构和表面活性基团的分布对激光能量的吸收，传输及团簇产生的重要作用。     

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。     

含LiClO4的接枝聚合物网络的结构,形态与离子导电性能研究

聚合物网络快离子导体成膜后有良好的机械强度、尺寸稳定性,但室温电导率稍偏低。本文通过大单体技术合成兼具优良室温电导率(σ_(298K)=4.8×10~(-5)S·cm~(-1))与机械强度的接枝聚合物网络快离子导体,并研究了网络结构、形态与离子导电性之间的关系。 1 实验部分 1.1 试剂及精制 试剂均为化学纯。分子量为800与2000的聚氧化乙烯(PEO800,     

吡啶和喹啉类化合物的碳-13核磁共振波谱模拟

运用多元回归分析和人工神经网法对１８个吡啶和喹啉类化合物的１１８个化学位移进行计算机模拟,并应用碳原子的空间和电子结构特征来表征其所处的化学环境,从而获得了比较好的相关模型。波谱模拟技术对辅助有机化合物结构解析和化学位移的确认是十分有用的工具。     

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     

Open spaces and molecular motions of polyether-based network polymers probed by positron annihilation

The lifetimes of positrons have been measured for network polymers based on polyethers. From the temperature dependence of the lifetime of ortho-positronium (o-Ps), τ₃, for the network polymer of poly(ethylene oxide-co-propylene oxide) [P(EO/PO)], an onset temperature for limited local motions of molecules, T_γ, and the glass transition temperature, T_g, were determined to be 57 and 201 K, respectively. For the network polymer of poly[EO-co-2-(2-methoxyethoxy)ethyl glycidyl ether] [P(EO/MEEGE)], T_γ and T_g were determined to be 57 and 185 K, respectively. For both specimens, above 270 K, the observed linear temperature dependence of τ₃ was attributed to the thermal expansion of open spaces in a liquid state. In the temperature range between T_γ and 270 K, for the P(EO/MEEGE) network, τ₃ was longer and its intensity was smaller than those for the P(EO/PO) network. These results were attributed to the increase in the size of open spaces for the P(EO/MEEGE) network polymer and the blocking of these regions by motions of side chains and chain ends. © 1998 John Wiley & Sons, Inc. J Polym Sci B: Polym Phys 36: 1919–1925, 1998     

Supermolecular structures and flow birefringence in polymer solutions

Experimental studies of supermolecular structures and localized flow birefringence in solutions of high-molecular weight polymer are described. Advantage is taken of poly(ethylene oxide) and polyisobutylene. Supermolecular structures are examined with the aid of optical microscopy using freeze-dried samples of the polymer solutions. Birefringence is investigated that arises in planar elongational flow in a cross-slot cell. Flow velocities at which the onset of the localized birefringence occurs are determined. Then these velocities are correlated with viscoelastic characteristics of the solutions. The presence of a liquid-crystalline fibrillar network in the polymer solutions exhibiting flow birefringence is ascertained. The fibrils are birefringent objects. The fibrils are birefringent objects. The localized birefringence phenomenon is explained in term of the orientation of the fibrils in elongational flow. It has been shown that the onset of localized birefringence occurs at a critical Weissenberg number, the value of which is close to unity.