分子印迹技术在水质检测传感器领域的应用

分子印迹技术是制备具有特异识别能力聚合物(分子印迹聚合物)的新兴化学合成技术,分子印迹聚合物可作为传感器识别元件,用于制备水质检测传感器。总结了分子印迹技术在水质检测传感器领域的应用研究现状,探讨了其发展趋势,指出以分子印迹聚合物作为敏感识别元件的水质检测传感器具有快速、准确、稳定和可反复利用等优点,可用于检测水体中农药、除草剂、神经毒剂以及无机离子化合物等,是实现水质现场快速检测的重要技术途径。     

基于分子印迹的电化学传感器研究进展

分子印迹技术是制备对特定分子具有选择性识别的聚合物的技术.将分子印迹技术与分析传感技术相结合,有可能弥补生物识别元件的不足,为传感技术的发展开辟新的途径.该文综述了分子印迹聚合物用作电化学仿生传感器识别元件的研究进展,分析了存在的困难并进行了展望.     

分子印迹技术研究进展

分子印迹技术是一项新兴的分子识别技术,由于其卓越的分子识别功能,势必会形成一个崭新的技术群体.该文较系统地介绍和综述了分子印迹聚合物的制备、选择性结合特性及应用等方面的研究进展.     

不同功能单体制备水杨酸分子印迹聚合物的分子模拟研究

采用分子模拟技术,研究水杨酸分子制备印迹聚合物过程中,水杨酸(SA)与不同功能单体之间的结合能.作者等先计算水杨酸与常用功能单体之间的结合能,再选出适合制备水杨酸分子印迹聚合物的功能单体.其次详细讨论丙烯酰胺(AM)和甲基丙烯酸(MAA)与SA的结合能以及聚合物与水杨酸分子之问的结合能.发现,(1)制备分子印迹聚合物过程中,水杨酸的作用功能基团是-COOH,AM、MAA的作用功能基团分别是-NH2、-COOH.(2)SA与AM形成的复合物结合能大于与MAA形成复合物的结合能.由此,推断以AM为功能单体的水杨酸分子印迹聚合物,比以MAA为功能单体的水杨酸分子印迹聚合物的吸附量大,与实验结果一致.     

智能分子印迹水凝胶

分子印迹技术是近几十年来迅猛发展的一项新技术.目前主要应用于分离工程,免疫分析,抗体-受体模拟物,生物传感器,催化和人工酶工程领域.然而,有关其在水凝胶方面的应用鲜有报导.该文主要介绍一类智能分子印迹水凝胶,它能够通过对外界刺激产生响应,发生体积相转变,并由此控制分子识别功能的开关.这类智能水凝胶结合的特定分子主要为蛋白质、聚肽、核酸、葡萄糖等生物大分子,因而在药物控释体系、生物传感器等领域有着巨大的应用潜力.     

Leu-Phe分子印迹预组装体系热力学平衡的计算模拟

使用Gaussian03软件研究以Leu-Phe为模板,甲基丙烯酸为功能单体,乙二醇二甲基丙烯酸酯为交联剂,三氟乙酸为助溶剂的分子印迹预组装体系.计算了功能单体、助溶剂和交联剂加入量对各种模板复合物浓度的影响,进而确定了Leu-Phe分子印迹聚合物的合成条件.按条件合成聚合物后考察其识别性能,并结合计算模拟和实验结果解释实验现象.计算模拟可能有助于合成高特异性和亲和性分子印迹聚合物,探讨了分子印迹聚合物的分子识别机理.     

烟酸分子印迹聚邻氨基酚敏感膜传感器的研制

研制了电化学聚合制备烟酸分子印迹聚邻氨基酚敏感膜传感器,并对分子印迹膜的结构和性能进行了探讨与研究.在弱酸性条件下,以邻氨基酚为单体,烟酸为模板分子,用循环伏安法电聚合成膜制备传感器.该传感器对烟酸具有良好选择性和敏感度,用恒电位计时安培法,烟酸浓度在2.0～5.0 mmol/L范围内与电流增量成线性关系,加标回收率在92%～104%之间.     

声表面波β-CD巯基衍生物传感器检测DMMP的研究

以β-环糊精巯基衍生物单[6-脱氧(1,10癸二硫醇)巯基]β-环糊精为敏感膜材料和自组装的镀膜方法,在声表面波延迟线上制备了对有机膦化合物有选择性检测能力的分子印迹传感器.研究了环境温度对检测的影响、吸附动力学的过程,同时对分子印迹的机理进行了讨论.     

苯氧基乙酸分子印迹聚合物色谱行为研究

该文以苯氧基乙酸为印迹分子,制备了苯氧基乙酸印迹聚合物.以苯氧基乙酸印迹聚合物作为色谱固定相对其色谱行为进行了研究.结果表明苯氧基乙酸印迹聚合物具有良好的分离选择性能.通过改变流动相的pH值以及醇水比例发现苯氧基乙酸印迹聚合物的分离性能和流动相的性质有很大的关系.     

苯氧基乙酸印迹聚合物的选择性研究

该文以苯氧基乙酸为模板分子,制备了苯氧基乙酸印迹聚合物.采用柱吸附并结合紫外-可见分光光度法研究了苯氧基乙酸印迹聚合物对苯氧基乙酸、对氯苯氧基乙酸和2,4-二氯苯氧基乙酸的选择性,同时与空白聚合物进行了比较.研究了印迹聚合物作为固定相的柱分离特性.研究了分别以甲基丙烯酸和4-乙烯基吡啶为功能单体的苯氧基乙酸印迹聚合物的选择性能,结果表明苯氧基乙酸印迹聚合物相比空白聚合物有较好的选择性,并且选择性还与功能单体有关.     

有机磷水解酶生物传感器载体固定化酶的研究

有机磷水解酶（OPH）传感器作为检测农产品中农药残留的新型检测装置,其酶的固定化对OPH传感器的灵敏度和稳定性有重要的影响。研究了几种酶固定化载体、孔径大小、固定方式、固定方法（试剂组成）对传感器pH值的影响。结果显示：采用孔径为0.45μm的硝酸纤维素膜制备固定化酶片的pH值要大于其余几种;采用浸泡方式制备固定化酶片的pH值明显大于传统的滴定法;采用牛血清白蛋白（BSA）、戊二醛交联固定的效果优于酶直接吸附法和BSA固定法,且当戊二醛体积分数为2.5％,BSA为10％时,酶固定化效果最好。     

便携式AChE传感器系统的开发与研究

酶传感器体积小、适合野外分析,在环境污染检测、食品农药残留快速分析中具有重要的应用价值。本文基于酶抑制分析原理,构建了一套便携式AChE酶传感器系统。整个系统由恒电位仪采集数据,通过基于ARM7的系统板控制恒电位仪,采用丝网印刷工艺印制电极。在基于μClinux嵌入式系统应用软件的控制下,实现酶传感器检测农药的功能。整个系统提供了良好的控制界面,实验结果显示对硫磷检测限可低于2×10-9(2ppb)。     

手写粉笔数字自动识别方法研究

手写粉笔字在生产生活中应用广泛,针对手写粉笔字易与背景混淆的特点,提出了手写粉笔数字自动识别方法,分别通过粉笔字普适性分割算法和基于SVM的手写数字分层分类自动识别技术解决了分割和识别两个关键问题,并结合钢材板坯号自动识别系统的应用进行方法的实验,达到了较高的识别率,具有重大的应用价值和推广意义。     

语音识别及自组织映射神经网络的应用

该文阐述了语音信号的特点，语音识别过程及技术，重点讨论了例谱技术及其在语音信号特征提取中的应用，并将自组织映射神经网络应用到语音识别中，提出了网络模型。     

数字图像识别在人脸检测中的应用

利用数字图像模式识别技术实现了人脸的自动检测及特征定位.对数字图像处理中的颜色模型、肤色建模的原理及在人脸识别中的应用进行了概述,分析了人脸识别过程中存在的困难,展望了人脸识别技术的发展方向.     

Bayesian on-line spectral change point detection: a soft computing approach for on-line ASR

Current automatic speech recognition (ASR) works in off-line mode and needs prior knowledge of the stationary or quasi-stationary test conditions for expected word recognition accuracy. These requirements limit the application of ASR for real-world applications where test conditions are highly non-stationary and are not known a priori. This paper presents an innovative frame dynamic rapid adaptation and noise compensation technique for tracking highly non-stationary noises and its application for on-line ASR. The proposed algorithm is based on a soft computing model using Bayesian on-line inference for spectral change point detection (BOSCPD) in unknown non-stationary noises. BOSCPD is tested with the MCRA noise tracking technique for on-line rapid environmental change learning in different non-stationary noise scenarios. The test results show that the proposed BOSCPD technique reduces the delay in spectral change point detection significantly compared to the baseline MCRA and its derivatives. The proposed BOSCPD soft computing model is tested for joint additive and channel distortions compensation (JAC)-based on-line ASR in unknown test conditions using non-stationary noisy speech samples from the Aurora 2 speech database. The simulation results for the on-line AR show significant improvement in recognition accuracy compared to the baseline Aurora 2 distributed speech recognition (DSR) in batch-mode.     

边缘检测在外耳识别技术中的应用

外耳识别技术是一种新的生物个体识别技术 ,有它自身的应用范围和特点 ,以其为应用背景 ,比较了Canny算子、Sobel算子等边缘处理技术之后 ,从区分图像边缘类型视觉与算法角度出发 ,提出了一种基于外耳识别技术的边缘检测的实用方法 ,具有一定实际应用价值。     

Enantioselective molecular imprinting polymer coated QCM for the recognition of l-tryptophan

A new quartz crystal microbalance (QCM) sensor that provides enantioselectivity to tryptophan enantiomers, with a high selectivity and sensitivity, was fabricated by the use of the molecularly imprinted polymers (MIPs) as the artificial biomimetic recognition material. Non-covalent molecular imprinting polymers were synthesized using acrylamide (AM) and trimethylolpropane trimethacrylate (TRIM) as a functional monomer and a cross-linking agent, respectively. The preparation of the thin permeable film coatings on QCM surface is described as well as the results and discussion on the sensitivity and selectivity of the coatings to tryptophan enatiomers under different conditions. The sensor was able to discriminate between the l- and d-tryptophan enantiomers in citric acid buffer solutions owing to the enantioselectivity of the imprinted sites. The detection limit is 8.8 μM. The influence of the cross-linking agent concentration on the sensitivity and selectivity of the fabricated polymer films was investigated and optimized. The enantionmeric composition of l- and d-tryptophan enantiomer mixtures can be determined by measuring the frequency shifts of the sample. The characteristics of the thiol monolayer and MIP film on the surface of the QCM were studied by atomic force microscopy (AFM).     

A robust technique based on invariant moments – ANFIS for recognition of human parasite eggs in microscopic images

In this study, we propose a robust technique based on invariant moments – adaptive network based fuzzy inference system (IM-ANFIS). In this technique, some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and morphological process are used for feature extraction stage of IM-ANFIS approach used in this study. Recently, the pattern recognition principles have come into prominence. The pattern recognition includes operation and design of systems that recognize patterns in data sets. Important application areas of pattern recognition techniques are character recognition, speech analysis, image segmentation, man and machine diagnostics and industrial inspection. The technique presented in this study enables to classify 16 different parasite eggs from their microscopic images. This proposed recognition method includes three stages. In first stage, a preprocessing subsystem is realized for obtaining unique features from the same group of patterns. In second stage, a feature extraction mechanism which is based on the invariant moments is used. In third stage, an adaptive network based fuzzy inference system (ANFIS) classifier is used for recognition process. We conduct computer simulations on MATLAB environment. The overall success rate is almost 95%.