核-壳式纳米Al2O3 / PS 复合粒子改性聚苯乙烯的选区激光烧结实验研究

针对纳米粒子易团聚的特点, 利用乳液聚合方法制备纳米Al₂O₃ / PS 复合粒子。用TEM、FTIR 对复合粒子结构进行了表征。结果表明, 所制备的复合粒子具备以纳米氧化铝为核、以聚苯乙烯为壳的核2壳式结构, 而且包覆层厚度大约为10～20 nm。用复合粒子改性选区激光烧结制备聚苯乙烯基纳米复合材料, 通过SEM 和FE2SEM 研究纳米复合材料烧结体的显微结构, 发现纳米粒子较好地分散在聚合物基体中, 且纳米氧化铝与聚合物基体之间的界面相容性和粘结性较好, 烧结体结构较致密。      

核壳式纳米Al2O3/PS复合粒子的表征及增韧选区激光烧结聚苯乙烯的研究

运用TEM,FTIR对乳液聚合方法制备纳米Al2O3/PS复合粒子结构进行了表征,结果表明,制备出的复合粒子具备以纳米氧化铝为核、以聚苯乙烯为壳的核壳式结构;并将核壳式复合粒子用来增韧选区激光烧结聚苯乙烯,结果发现,其缺口冲击强度达到12.1kJ/m2,较纯聚苯乙烯提高了50%左右,比添加未经任何改性处理纳米氧化铝粒子的复合材料提高了30%;利用FE-SEM对试件的冲击断面进行了微观结构分析,结果表明:核壳式纳米Al2O3/PS复合粒子改善了纳米粒子与基体表面极性的差异,增强了其与聚合物基体之间的界面相容性,从而改性了选区激光烧结制备聚苯乙烯基复合材料,并很好地起到增韧的效果.     

复合粒子对聚丙烯增韧改性的研究进展

文中综述了采用两段聚合工艺制备聚丙烯高抗冲釜内合金(IPC)和采用共混制备多相多组分包覆型复合粒子增韧聚丙烯(PP),重点讨论了IPC、橡胶(弹性体)/热塑性塑料复合粒子、聚合物/无机刚性复合粒子、无机纳米复合粒子等对PP性能的影响,简要介绍了不同类型复合粒子的增韧机理,并对该领域今后的研究方向进行了展望。     

纳米粒子在润滑材料中的应用进展

介绍了纳米润滑粒子的制备方法,探讨了纳米粒子的润滑机理,包括膜作用机理、类滚珠作用机理和表面优化作用机理.着重综述了纳米粒子在润滑材料中的应用,具体包括:纳米无机粒子可作为添加剂在润滑油、聚合物基复合材料中起抗磨、减摩作用;纳米粒子应用于非聚合物基复合涂层,可形成一种根据环境自我调节的活泼纳米复合涂层,此涂层具有广泛的应用前景.     

SiO_2表面包覆改性钛酸钡/聚酰亚胺复合薄膜的制备与性能

以聚酰亚胺(PI)为基体,将聚酰亚胺与钛酸钡(BaTiO3)纳米粒子进行复合,采用原位聚合法制备BaTiO3/PI复合薄膜。为提高BaTiO3纳米粒子的分散性和表面性能,采用SiO2对BaTiO3纳米粒子进行表面包覆改性,并制备改性BaTiO3/PI复合薄膜。采用红外光谱、X射线衍射、扫描电镜等对制备得到的改性BaTiO3进行了表征,测试了复合薄膜的介电性能。结果发现,SiO2与BaTiO3粒子间仅是物理包覆,没有新物质形成。测试频率为103 Hz时,质量分数为5%的SiO2包覆改性使复合薄膜的介电常数增大到21.8,介电损耗为0.00521,击穿强度为76 MV/m,储能密度为0.56J/cm3。研究表明,采用SiO2对BaTiO3改性使得复合薄膜的介电性能有所提高。     

原位聚合制备有机-无机复合材料的研究进展

综述了原位聚合有机-无机纳米材料的制备方法,其中包括聚合物与纳米SiO2的复合,聚合物与层状硅酸盐纳米材料的复合,聚合物与纳米TiO2复合以及聚合物与其它纳米粒子的复合等方面的研究进展;较详细介绍了其发展现况和存在的问题,并对制备方法进行了展望.     

聚合物/无机纳米粒子复合微球化学制备的研究进展

聚合物/无机纳米粒子复合微球,具有良好的可设计性、流动性、热稳定性、功能基表面特性等优良的综合性能.聚合物/无机纳米粒子复合微球的形貌、粒径及分布、表面结构等可根据制备方法发生显著变化,从而影响其理化性能.本文综述了乳液聚合法、悬浮聚合法、分散聚合法、两步复合法、自组装法、物理诱导和模板辅助法等化学方法在制备聚合物/无...     

选择性激光烧结法制备聚合物/Al2O3纳米复合材料

介绍了一种新的简单实用的制备大块聚合物/无机纳米复合材料的方法,即探索性地采用选择性激光烧结(SLS)法来制备聚合物/Al2O3纳米复合材料.结果表明,以表面已预处理的聚合物与纳米粒子Al2O3混合粉料作为SLS用粉,在一定的激光烧结工艺参数条件下,可将其烧结成纳米粒子均匀分散在聚合物基体中的块体材料;并可通过控制激光功率和扫描速度等激光烧结工艺参数获得不同性能的聚合物/纳米复合材料.     

纳米复合薄膜及其在果蔬保鲜中的应用

纳米复合包装薄膜一般为聚合物基纳米复合材料,可分为纳米材料/合成聚合物复合材料与纳米材料/天然聚合物复合材料。阐述了这2种复合材料的制备方法和性能特点,综述了纳米TiO2复合薄膜、纳米SiO2复合薄膜、纳米CaCO3复合薄膜、纳米银复合薄膜在果蔬保鲜中的应用研究,并展望了纳米复合薄膜在果蔬保鲜应用方面的研究方向。     

双糖聚合物接枝金纳米星的制备及光热性能研究

通过酶促反应和可逆加成-断裂链转移聚合(RAFT)聚合得到温度敏感性的双糖无规聚合物聚(二聚乙二醇单甲醚甲基丙烯酸酯-6-O-乙烯基己二酸-D-吡喃型葡萄糖/半乳糖酯)[Poly(DEGMA-co-OVNGmix)];利用还原剂将聚合物末端的硫酯键还原,得到末端为-SH的聚合物分子以S-Au键修饰到金纳米星表面。采用傅里叶红外光谱仪、透射电镜和核磁氢谱等手段对聚合物和复合金纳米星的理化性质和光热转化能力进行研究。结果表明:AuNSs@Poly(DEGMA-co-OVNGmix)成功制备,该复合粒子的平均粒径为50~100nm,并且具有良好的稳定性。光热性能测试结果表明,复合粒子的光热转换效率达到50.54%,说明经修饰的金纳米星具有优异的光热转换能力。     

Development of a robust calibration model for nonlinear in-line process data

A comparative study involving a global linear method (partial least squares), a local linear method (locally weighted regression), and a nonlinear method (neural networks) has been performed in order to implement a calibration model on an industrial process. The models were designed to predict the water content in a reactor during a distillation process, using in-line measurements from a near-infrared analyzer. Curved effects due to changes in temperature and variations between the different batches make the problem particularly challenging. The influence of spectral range selection and data preprocessing has been studied. With each calibration method, specific procedures have been applied to promote model robustness. In particular, the use of a monitoring set with neural networks does not always prevent overfitting. Therefore, we developed a model selection criterion based on the determination of the median of monitoring error over replicate trials. The back-propagation neural network models selected were found to outperform the other methods on independent test data.     

Identification model of multi-layered neural network parameters and its applications in the petroleum production

This paper creates a LM (Levenberg-Marquardt) algorithm model which is appropriate to solve the problem about weights value of feedforward neural network. On the base of this model, we provide two applications in the oilfield production. Firstly, we simulated the functional relationships between the petrophysical and electrical properties of the rock by neural networks model, and studied oil saturation. Under the precision of data is confirmed, this method can reduce the number of experiments. Secondly, we simulated the relationships between investment and income by the neural networks model, and studied invest saturation point and income growth rate. It is very significant to guide the investment decision. The research result shows that the model is suitable for the modeling and identification of nonlinear systems due to the great fit characteristic of neural network and very fast convergence speed of LM algorithm.     

On reaction network implementations of neural networks

This paper is concerned with the utilization of deterministically modelled chemical reaction networks for the implementation of (feed-forward) neural networks. We develop a general mathematical framework and prove that the ordinary differential equations (ODEs) associated with certain reaction network implementations of neural networks have desirable properties including (i) existence of unique positive fixed points that are smooth in the parameters of the model (necessary for gradient descent) and (ii) fast convergence to the fixed point regardless of initial condition (necessary for efficient implementation). We do so by first making a connection between neural networks and fixed points for systems of ODEs, and then by constructing reaction networks with the correct associated set of ODEs. We demonstrate the theory by constructing a reaction network that implements a neural network with a smoothed ReLU activation function, though we also demonstrate how to generalize the construction to allow for other activation functions (each with the desirable properties listed previously). As there are multiple types of ‘networks’ used in this paper, we also give a careful introduction to both reaction networks and neural networks, in order to disambiguate the overlapping vocabulary in the two settings and to clearly highlight the role of each network’s properties.     

Efficiency parameters estimation in gemstones cut design using artificial neural networks

This paper deals with the problem of estimating cut results for faceted gemstones. The proposed approach applies artificial neural networks for a faceted gemstones analysis tool that could be further developed for incorporation in a computer-aided-design (CAD) context. Basic concepts concerning gemstone processing are introduced and the design of computational tools using neural networks is discussed. The model presented proposes two criteria to assess the efficiency of lapidary designs for rock crystal quartz: brilliance and yield. Closing the article, 62 different lapidary models were used to train and test the neural network tool.     

Neural modeling of dynamic systems with nonmeasurable statevariables

The paper studies the ability possessed by recurrent neural networks to model dynamic systems when some relevant state variables are not measurable. Neural architectures based on virtual states-which naturally arise from a space state representation-are introduced and compared with the more traditional neural output error ones. Despite the evident potential model ability possessed by virtual state architectures we experimented that their performances strongly depend on the training efficiency. A novel validation criterion for neural output error architectures is suggested which allows to assess the neural network not only in terms of its approximation accuracy but also with respect to stability issues     

High-capacity neural networks on nonideal hardware

We present a new training-out algorithm for neural networks that permits good performance on nonideal hardware with limited analog neuron and weight accuracy. Optical neural networks are emphasized with the error sources including nonuniform beam illumination and nonlinear device characteristics. We compensate for processor nonidealities during gated learning (off-line training); thus our algorithm does not require real-time neural networks with adaptive weights. This permits use of high-accuracy nonadaptive weights and reduced hardware complexity. The specific neural network we consider is the Ho-Kashyap associative processor because it provides the largest storage capacity. Simulation results and optical laboratory data are provided. The storage measure we use is the ratio M/N of the number of vectors stored (M) to the dimensionality of the vectors stored (N). We show a storage capacity of M/N = 1.5 on our optical laboratory system with excellent recall accuracy, > 95%. The theoretical maximum storage is M/N = 2 (as N approaches infinity), and thus the storage and performance we demonstrate are impressive considering the processor nonidealities we present. Our techniques can be applied to other neural network algorithms and other nonideal processing hardware.     

Product Assembly Cost Estimation Based on Artificial Neural Networks

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Application of genetic algorithm-back propagation for prediction of mercury speciation in combustion flue gas

Coal combustion is one of the main sources of mercury emission. Studies using artificial neural networks (ANNs) to predict mercury emission have shown the feasibility of ANN method. Such analyses aimed to provide guidance for mercury emission control in coal combustion. A mercury emission prediction model was developed by modifying the traditional back propagation (BP) neural networks, and a genetic algorithm (GA) based on global search was used, so called the GA-BP neural networks. In total, six main factors were evaluated and selected as the characteristics parameters. Totally, 20 coal-fired boilers were used as training samples, and three different types of mercury including elemental mercury, oxidized mercury, and particulate mercury were used as outputs. The accuracy of prediction results was analyzed, and source of error was discussed. Results show that correlation efficiency for the training samples was as high as 0.895. Three additional samples were studied to test the predictive model. Results of training and predicting were highly correlated with actual measurement results. It is shown that GA-BP is a promising model for mercury speciation prediction.     

Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers

In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm (GA), has been used to predict collection efficiency in venturi scrubbers. The experimental input data, including particle diameter, throat gas velocity, liquid to gas flow rate ratio, throat hydraulic diameter, pressure drop across the venturi scrubber and collection efficiency as an output, have been used to create a GA-artificial neural network (ANN) model. The testing results from the model are in good agreement with the experimental data. Comparison of the results of the GA optimized ANN model with the results from the trial-and-error calibrated ANN model indicates that the GA-ANN model is more efficient. Finally, the effects of operating parameters such as liquid to gas flow rate ratio, throat gas velocity, and particle diameter on collection efficiency were determined.     

Prediction of strength parameters of FRP-confined concrete

This research deals with the prediction of compressive strength and crushing strain of FRP-confined concrete using neural networks and regression models. Basic information on neural networks and the types of neural networks most suitable for the analysis of experimental results are given. A set of experimental data, covering a large range of parameters, for the training and testing of neural networks is used. The prediction models based on neural network are presented. The influence of raw and the non-dimensional group of variables on compressive strength and crushing strain of FRP-confined concrete is studied through sensitivity analysis, which provided a basis for the development of a new regression based model. The neural networks based model gave high prediction accuracy and the results demonstrated that the use of neural networks in assessing the compressive strength and crushing strain of FRP-confined concrete is both practical and beneficial.