Automatic Electronic Tongue for On-Line Detection and Quantification of Organophosphorus and Carbamate Pesticides Using Enzymatic Screen Printed Biosensors

This work presents an automatic system, based on an electronic tongue, for resolution of mixtures of three pesticides. Inhibition detections were performed during the steady state of biosensors response. Three biosensors were built using two enzymes, electric eel (EE), genetically-modified Drosophila melanogaster (B131), and electric eel co-immobilized with drosophila melanogaster (BH). Calibrations curves for paraoxon, dichorlvos, and carbofuran were performed in the ranges 0.4–50.4 µM, 0.01–1.01 µM, 0.01–0.41 µM with LOD of 3.91 × 10^?8, 6.30 × 10^?11, and 5.84 × 10^?10, respectively. An artificial neural network (ANN) was used to model the combined response of three pesticides. A set of 19 mixtures were prepared in order to train the artificial neural network, the modeling was validated with a set of 6 spiked samples of river water. The error and recovery yields were found in consistent with expected values.     

Formula Optimization of Emulsifiers for Preparation of Multiple Emulsions Based on Artificial Neural Networks

Formulation optimization of emulsifiers for preparing multiple emulsions was performed in respect of stability by using artificial neural network (ANN) technique. Stability of multiple emulsions was expressed by the percentage of reserved emulsion volume of freshly prepared sample after centrifugation. Individual properties of multiple emulsions such as droplet size, δ, viscosity of the primary and the multiple emulsions were also considered. A back‐propagation (BP) network was well trained with experimental data pairs and then used as an interpolating function to estimate the stability of emulsions of different formulations. It is found that using mixtures of Span 80 and Tween 80 with different mass ratio as both lipophilic and hydrophilic emulsifiers, multiple W/O/W emulsions can be prepared and the stability is sensitive to the mixed HLB numbers and concentration of the emulsifiers. By feeding ANN with 39 pairs of experimental data, the ANN is well trained and can predict the influences of several formulation variables to the immediate emulsions stability. The validation examination indicated that the immediate stability of the emulsions predicted by the ANN is in good agreement with measured values. ANN therefore could be a powerful tool for rapid screening emulsifier formulation. However, the long‐term stability of the emulsions is not good, possibly due to the variation of the HLB number of the mixed monolayers by diffusion of emulsifier molecules, but can be greatly improved by using a polymer surfactant Arlacel P135 to replace the lipophilic emulsifier.     

Copper-containing dendromesogens: the influence of the metal on the mesomorphism

The synthesis and liquid crystalline behaviour of the first and second generations of a dendrimeric structure based on poly(propyleneimine)(DAB-dendr(NH₂)_x) are reported. 4-(4-n-Alkoxybenzoyloxy)salicylaldehydes are used as mesogenic moieties attached at the peripheral amino groups of the dendrimers giving rise to dendromesogens with four and eight mesogenic branches. From these dendromesogens, considered as organic ligands, were prepared six metal-containing dendrimers which incorporate two or four copper atoms in their structures. All the dendrimeric ligands and three of the metal-containing dendrimers exhibit liquid crystalline properties which were studied by optical microscopy, DSC, X-ray diffraction and EPR spectroscopy.     

Assessment of mobility of heavy metals in two soil types by use of column leaching experiments and chemometric evaluation of elution curves

The objectives of this study were to evaluate the mobility of heavy metals (HMs) in two types of soils (acidic forest soil and neutral agricultural soil) by leaching with calcium chloride solution in column experiments. The screening properties of neutral agricultural soil towards pollution by heavy metals (Ni, Cu, Zn and Cd) are approximately 10 times higher than those of acid forest soil. The neutral agricultural soil, polluted artificially by one pore volume (PV) of an HMs solution of concentration 200 mg L^?1, can screen the leaching of these metals over several hundreds of years. The higher apparent desorption rate and per cent desorption of HMs (especially Cd) in acid forest soil indicated a higher potential of intensive migration of the metals across the profile and indicated potential risk of Cd pollution for this type of soil. The latest approach of artificial neural networks to describe transport of HMs in soil has been also evaluated. Using a simple three-layer perceptron topology with three hidden neurons, the experimental data could be simulated. The results suggested that the pH of soil is a major factor controlling the retention of the heavy metals in the soils.     

Artificial Neural Networks in water analysis: Theory and applications

Artificial Neural Networks (ANNs) have seen an explosion of interest over the last two decades and have been successfully applied in all fields of chemistry and particularly in analytical chemistry. Inspired from biological systems and originated from the perceptron, i.e. a program unit that learns concepts, ANNs are capable of gradual learning over time and modelling extremely complex functions. In addition to the traditional multivariate chemometric techniques, ANNs are often applied for prediction, clustering, classification, modelling of a property, process control, procedural optimisation and/or regression of the obtained data. This paper aims at presenting the most common network architectures such as Multi-layer Perceptrons (MLPs), Radial Basis Function (RBF) and Kohonen's self-organisations maps (SOM). Moreover, back-propagation (BP), the most widespread algorithm used today and its modifications, such as quick-propagation (QP) and Delta-bar-Delta, are also discussed. All architectures correlate input variables to output variables through non-linear, weighted, parameterised functions, called neurons. In addition, various training algorithms have been developed in order to minimise the prediction error made by the network. The applications of ANNs in water analysis and water quality assessment are also reviewed. Most of the ANNs works are focused on modelling and parameters prediction. In the case of water quality assessment, extended predictive models are constructed and optimised, while variables correlation and significance is usually estimated in the framework of the predictive or classifier models. On the contrary, ANNs models are not frequently used for clustering/classification purposes, although they seem to be an effective tool. ANNs proved to be a powerful, yet often complementary, tool for water quality assessment, prediction and classification.     

Handling of Environmental and Biological Samples via Pre-Column Technologies

Abstract

Sample handling is still a weak point in chromatography and in analytical chemisty in general. One consideration is the automation potential of new procedures. Solid-liquid extraction techniques in combination with pre-column technology are particularly promising in this regards. The construction and geometry of pre-columns both for conventional and narrow-bore HPLC are of major importance, since band broadening should be kept at a minimum for an optimal functioning of the analytical system. The various operations that can be carried out with such a pre-column are trace-enrichment, clean-up of the sample which depends on the type of adsorbents used in the precolumn, i.e., polar or apolar materials, ion exchangers or metal covered surfaces, etc., protection of the analytical column, field sampling and storage of samples and as a substrate for on-column chemical derivatizations. These various operations are demonstrated with practical examples from the fields of environmental and biological analysis. The selectivity can be further enhanced by coupling precolumn technology with selective detection modes such as diode array UV, electrochemical or fluorescence detection. This enables the construction of optimal and integrated analysis sytems which are fully automated and microprocessor controlled. They can also be made compatible with miniaturized LC-technology.     

两个4-[(8-羟基-5-喹啉)偶氮]苯磺酸配合物的合成、结构及性质研究

以4-[(8-羟基-5-喹啉)偶氮]苯磺酸(H₂L)为配体,采用溶剂热合成法合成了两个配合物[CuL(en)]·0.5H₂O (1)和[CuL₂][Cu(en)₂]·2H₂O (2)(en=乙二胺),并采用单晶X-射线衍射、红外光谱、元素分析、X-射线粉末衍射和热重分析对其进行表征。化合物1和2通过氢键和π…π相互作用连接形成了三维超分子网格。此外,还研究了两个化合物的荧光性质。     

航空通信网络中复杂干扰过滤技术研究

航空通信环境中存在复杂的干扰信号,对正常的通信信号造成强烈的干扰。利用传统算法进行干扰信号过滤,无法避免由于干扰信号过于复杂导致过滤不充分的缺陷。提出一种基于加权残差优化算法的航空通信网络中复杂干扰过滤方法。对原始信号进行降频处理,能够消除峰值干扰信号,提高了信号的准确性。根据加权残差修正优化算法的相关原理,对通信信号进行残差优化处理,经过处理后的通信信号在受到干扰时会形成零陷,实现了对复杂干扰信号的过滤。实验结果表明,利用该算法进行航空通信网络中复杂干扰信号过滤,能够有效提高过滤的准确性,效果令人满意。     

广东省能源需求预测模型构建及实证分析

为了对广东省的能源需求进行准确的预测,首先分析了影响广东省能源需求的各种因素,构建了预测指标体系.在此基础上,针对能源系统非线性等复杂系统特征,结合粒子群算法和BP神经网络的优点,构建了改进的PSO-BP神经网络的预测模型,并通过主成分分析法对指标体系进行数据降维,以降低神经网络的规模和复杂程度.以广东省1985-2013年的能源需求数据进行模拟与仿真,并对2014-2018年的能源需求量进行预测,理论分析和实证研究表明,该方法能够很好的反映广东省能源需求的特征,预测结果较为准确合理.     

一类非线性时滞网络控制系统的无源性分析

本文研究了一类非线性时滞网络控制系统的无源性问题.利用Lyapunov稳定性理论,结合线性矩阵不等式(LMI)技术,通过构造Lyapunov-Krasovskii泛函,在考虑两种不同时滞的情况下,获得了系统满足无源性的充分条件,最后通过仿真算例验证了结论的正确性和方法的有效性.