Preparation,morphology, and mechanical properties of elastomers based on α,ω‐dihydroxy‐polydimethylsiloxane/polystyrene blends

α,ω‐Dihydroxy‐polydimethylsiloxane/polystyrene (PDMS/PS) blends were prepared by the solution polymerization of styrene (St) in the presence of α,ω ‐dihydroxy‐polydimethylsiloxane (PDMS), using toluene as solvent and benzoyl peroxide (BPO) as initiator. The PDMS/PS blends obtained by this method are a series of stable, white gums, which were vulcanized to elastomers at room temperature with methyl‐triethoxysilicane (MTES). The use level of MTES was far more than the necessary amount used to end‐link hydroxy‐terminated chains of PDMS, with the excess being hydrolyzed to crosslinked networks, which were similar to SiO₂ and acted as filler. Investigations were carried out on the elastomeric materials by extraction measurement, swelling measurement, and scanning electron microscopy. The extraction data show that at each composition the amount of soluble fraction is less than expected and the difference between experimental and theoretical values becomes more and more significant as PS content increases. This is mainly due to the grafting of PS onto PDMS and the entanglement of PS in the interpenetrating polymer network (IPN), which consists of either directly linked PDMS chains or chains linked via PS grafts and is formed by free radical crosslinking of PDMS during the radical polymerization of St. PS grafted on PDMS is insoluble and PS entangled in the IPN is difficult to extract. Both render the soluble fraction to be less than expected. As the St content in preparing PDMS/PS blends increases, the probability of grafting PS onto PDMS also increases, which may subsequently produce a higher crosslinking level of PDMS networks that linked via PS grafts by radical crosslinking. As a result, not only the amount of insoluble PS increases but also PS entangled in the IPN is more difficult to extract. Scanning electron microscopy demonstrates that the elastomer system has a microphase‐separated structure and a certain amount of PS remains in the PDMS networks after extraction, which is in accordance with the extraction data. Moreover, the mechanical properties of the elastormeric materials have been studied in detail. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 3542–3548, 2004     

广角摄像机在机器人视觉定位中的应用

为了保证广角摄像机在现代机器人视觉定位应用中的准确性,以广角摄像机像素坐标的反畸变过程为研究对象,提出一种在畸变对应表的基础上使用中心迭代法以及区域插值法确定物体实际位置的方法。通过实验证明了该方法能够测量物体的位置,其精度能够满足工业使用要求。     

扎赉诺尔褐煤制取高炉喷吹料和中热值煤气研究 (Ⅰ)煤气、焦油产率及性质

本文是在10kg／h固体热载体干馏实验装置上用扎赉诺尔褐煤制取高炉喷吹料和中热值煤气的研究结果。在干馏温度450℃～650℃范围制得煤气110Nm3/t～400Nm3/t，煤气热值为14．7MJ／Nm3～17．5MJ／Nm3，属中热值煤气。可供民用，也可用于补充钢铁企业燃气不足，平衡能源。得低温焦油2％～9％，可加工成化工产品和燃料油。半焦可用于高炉喷吹。     

乙烯二聚制高纯丁烯－1技术经济评价

本文对生产高纯度丁烯－１的Ａｌｐｈａｂｎｔｏｌ工艺进行了技术经济评价，认为该工艺条件简单、反应条件温和，而且投资少，所以很适合事在发展中国家应用。     

多重网格法在地球物理正反演应用研究中的进展

现阶段地球物理三维勘探面临越来越复杂的问题,其要求更精细的三维网格剖分,现有的一些方法随着网格节点的增加其收敛速度相应减慢。对于应用中更复杂实际模型、更细密网格剖分以及更快收敛速度要求,难于有进一步提高。要有所突破,需借助计算数学最新进展,引入新的高效算法。多重网格法是近二十年迅速发展的一种求解微分方程近乎最优的新算法。本文首先简单介绍了多重网格法基本原理和运算格式,着重介绍了当前国内外多重网格法在地球物理正反演中的应用成果和发展现状。此外,对多重网格法在地球物理正反演中的应用前景及发展趋势进行了展望。     

Full-scaled deep metric learning for pedestrian re-identification

Multimedia Tools and Applications - The pedestrian re-identification problem (i.e., re-id) is essential and pre-requisite in multi-camera video surveillance studies, provided the fact that...     

A novel spherical-ordered macroporous CuO nanocatalyst for the electrochemical reduction of carbon dioxide

Journal of Applied Electrochemistry - The electrocatalytic reduction of CO2 is a promising research direction in resource utilization and sustainable energy development. However, there is still a...     

Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations

Due to the complexity of the machine tool structure and the cutting process, the dynamics of machining processes are still not completely understood. This is especially true due to the demand of high-speed machining to increase productivity. In order to model and control these complex processes, new approaches, which can represent complex phenomenon combined with learning ability, are needed. The combined neural–fuzzy approach appears to be ideally suited for this purpose. In this paper, the recently developed fuzzy adaptive network (FAN) is used to model surface roughness in turning operations. The FAN network has both the learning ability of neural network and linguistic representation of complex, not well-understood, vague phenomenon. Furthermore, it can continuously improve the initially obtained rough model based on the daily operating data. To illustrate this approach, a model representing the influences of machining parameters on surface roughness is established and then the model is verified by the use of the results of pilot experiments. Finally, a comparison with the results based on statistical regression is provided.