A fractal model of reinforcement of elastoplastic nanocomposites

Within the framework of fractal analysis and percolation theory, an alternative model of reinforcement of filled polymers is offered. Practically, this model can be used only to describe the reinforcement of nanocomposites, because, according to the treatment considered, a pronounced reinforcement can be reached only at ratios of filler particle diameter to the statistical segment length of about 10 and less. A theoretical calculation showed a good qualitative and quantitative agreement with experiments. The type of reinforcement mechanism of composites is determined by the type of the space (fractal or Euclidean) in which the structure of the polymeric matrix is formed. __________ Translated from Mekhanika Kompozitnykh Materialov, Vol. 42, No. 6, pp. 797–802, November–Decem ber, 2006.     

Action learning versus strategy learning

This article seeks to ascertain whether the strategy‐learning model of Hanaki, Sethi, Erev, and Peterhansl (2003) better accounts for observed behavior than do the various action‐learning models. It does so by measuring the goodness‐of‐fit of the models' predictions against published experimental results for such games as Coordination, Prisoner's Dilemma, and Chicken. The fit is measured via the mean squared deviation (MSD) between the observed behavior and the one predicted by the model. The results show that, for Chicken, the strategy‐learning model fits the observed data much better than do the action‐learning models. The best action‐learning model, on the other hand, fits the observed data well in Coordination. Overall, the strength of the strategy‐learning model is best shown in games where alternations between the two stage‐game Nash equilibria are often observed in the laboratory experiments. © 2004 Wiley Periodicals, Inc. Complexity 9: 41–50, 2004     

Transport of surface-modified iron nanoparticle in porous media and application to arsenic(III) remediation

The surface-modified iron nanoparticles (S-INP) were synthesized, characterized and tested for the remediation of arsenite (As(III)), a well known toxic groundwater contaminant of concern. The S-INP material was fully dispersed in the aqueous phase with a particle size distribution of 2–10 nm estimated from high-resolution transmission electron microscopy (HR-TEM). X-ray photoelectron spectroscopy (XPS) revealed that an Fe(III) oxide surface film was present on S-INP in addition to the bulk zero-valent Fe⁰ oxidation state. Transport of S-INP through porous media packed in 10 cm length column showed particle breakthroughs of 22.1, 47.4 and 60 pore volumes in glass beads, unbaked sand, and baked sand, respectively. Un-modified INP was immobile and aggregated on porous media surfaces in the column inlet area. Results using S-INP pretreated 10 cm sand-packed columns containing ∼2 g of S-INP showed that 100 % of As(III) was removed from influent solutions (flow rate 1.8 mL min⁻¹) containing 0.2, 0.5 and 1.0 mg L⁻¹ As(III) for 9, 7 and 4 days providing 23.3, 20.7 and 10.4 L of arsenic free water, respectively. In addition, it was found that 100% of As(III) in 0.5 mg/L solution (flow rate 1.8 mL min⁻¹) was removed by S-INP pretreated 50 cm sand packed column containing 12 g of S-INP for more than 2.5 months providing 194.4 L of arsenic free water. Field emission scanning electron microscopy (FE-SEM) showed S-INP had transformed to elongated, rod-like shaped corrosion product particles after reaction with As(III) in the presence of sand. These results suggest that S-INP has great potential to be used as a mobile, injectable reactive material for in-situ sandy groundwater aquifer treatment of As(III).     

Application of a Near-Optimal Reinforcement Learning Controller to a Robotics Problem in Manufacturing: A Hybrid Approach

Optimization theory provides a framework for determining the best decisions or actions with respect to some mathematical model of a process. This paper focuses on learning to act in a near-optimal manner through reinforcement learning for problems that either have no model or the model is too complex. One approach to solving this class of problems is via approximate dynamic programming. The application of these methods are established primarily for the case of discrete state and action spaces. In this paper we develop efficient methods of learning which act in complex systems with continuous state and action spaces. Monte-Carlo approaches are employed to estimate function values in an iterative, incremental procedure. Derivative-free line search methods are used to obtain a near-optimal action in the continuous action space for a discrete subset of the state space. This near-optimal control policy is then extended to the entire continuous state space via a fuzzy additive model. To compensate for approximation errors, a modified procedure for perturbing the generated control policy is developed. Convergence results under moderate assumptions and stopping criteria are established.     

Hydrogel‐like elastic membrane consisting of semi‐interpenetrating polymer networks based on a phosphorylcholine polymer and a segmented polyurethane

To obtain a hydrogel‐like elastic membrane, we prepared semi‐interpenetrating polymer networks (IPNs) by the radical polymerization of methacrylates such as 2‐methacryloyloxyethyl phosphorylcholine (MPC), 2‐hydroxyethylmethacrylate, and triethyleneglycol dimethacrylate diffused into segmented polyurethane (SPU) membranes swollen with a monomer mixture. The values of Young's modulus for the hydrated semi‐IPN membranes were less than that for an SPU membrane because of higher hydration, but they were much higher than that for a hydrated MPC polymer gel (non‐SPU). According to a thermal analysis, the MPC polymer influenced the segment association of SPU. The diffusion coefficient of 8‐anilino‐1‐naphthalenesulfonic acid sodium salt from the semi‐IPN membrane could be controlled with different MPC unit concentrations in the membrane, and it was about 7 × 10² times higher than that of the SPU membrane. Fibroblast cell adhesion on the semi‐IPN membrane was effectively reduced by the MPC units. We concluded that semi‐IPNs composed of the MPC polymer and SPU may be novel polymer materials possessing attractive mechanical, diffusive‐release, and nonbiofouling properties. © 2002 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 41: 68–75, 2003     

CuO/Al2O3催化剂高温固相反应的原位XRD和Raman研究

采用原位XRD和激光Raman光谱等技术对CuO/Al2O3系列催化剂高温下的表面组成和体相结构的变化进行研究.结果表明,随着焙烧温度升高,CuO首先与载体Al2O3发生固相反应生成CuAl2O4.CuAl2O4层能阻止外层CuO进一步向载体Al2O3扩散,从而使部分CuO稳定在CuO/Al2O3催化剂的表层.     

A NOVEL EOR POLYMER (Ⅱ)——INVESTIGATION ON IN-SITU GELATION OF SMRF SYSTEM IN BEREA CORE

In-situ gelation of aqueous sulfomethylated resorcinol formaldehyde (SMRF) system inBerea core has been investigated. Two sets of displacement experiments were conducted with thissystem (containing 5% NaCl, 0. 036% CaCl_2. 2H_2O). The brine permeabilities of the coreswere reduced significantly from about 600 to 0.1 md. The in-situ gelation in Berea core occurreda little bit earlier than gelation anticipated from bulk test in the experiments. The gel time waseasier to control at initial pH between 6 and 8. During injection of SMRF system, the apparentviscosity was less than 1 mPa·s at 41℃.     

透明导电玻璃(ITO)基材自加热传感静态芯片聚合酶链反应(PCR)

利用商品化ITO玻璃导电层的温阻效应, 无需任何微加工手段, 实现了自加热和传感的芯片温度自动程序控制, 最大程度地减小了传感滞后对温度控制稳定性的影响, 温度控制的稳定性达到了0.2 ℃, 升温速度最快可达20 ℃/s以上, 在冷却风扇辅助下降温速度最快达到了8 ℃/s. 芯片温控单元的引线从传统的两对(一对用于传感, 一对用于加热)减少为一对. 通过在该芯片上直接构建多个开放微池反应器的方法成功地实现了λDNA 157 bp片段的并行扩增. 将该芯片置于倒置荧光显微镜样品台上, 以蓝色(575 nm)发光二极管为光源, 以光电倍增管为检测手段检测了dsDNA和SYBR Green Ⅰ嵌合物的荧光强度随温度的实时变化曲线.     

含液晶聚合物的原位复合材料中界面相容性的改善策略

综述了近年来含液晶聚合物的原位复合材料中增容技术的一些进展,主要了四种增容技术,即加入具有增容作用的第三组分;在分散相液晶聚合物的主链上引入与基体树脂主链中相同或相似的单元,酯交换反应和多元共混技术,简要描术字原位复合材料的发展趋势。     

热致液晶共聚酯酰胺／PET共混物的流变性能和原位增强特性

采用Ｉｎｓｔｒｏｎ３２１１型毛细管流变仪研究了ＰＥＡ／ＰＥＴ共混物的流变性能，据此探讨共混物熔体的可纺性，讨论了共混物配比与复合纤维力学性能的关系，并且偏光显微镜，扫描电镜研究纤维的结构。结果表明，共混物的表观粘度低于ＰＥＴ的粘度，当ＰＥＡ为１０％，共混物的表观粘度达到最小值；且ＰＥＡ含量低于１０％时，共混物的可纺性即，表现出良好的原位增强效果。