金针菇多糖对微冻大黄鱼及其鱼片品质变化的影响

为研究金针菇多糖（polysaccharide from Flammulina velutipes，FVP）对微冻大黄鱼及鱼片在贮藏期间肌原纤维蛋白性质的变化及水分分布的影响，实验分别选用0.03、0.06、0.09 g/L FVP浸渍处理大黄鱼和鱼片，以无菌水处理为对照组，分析微冻贮藏期间样品的感官指标得分、总挥发性盐基氮含量、总巯基含量、Ca2＋-ATPase活性、蛋白流变学性质以及水分迁移变化规律。结果表明：FVP可有效抑制整鱼总挥发性盐基氮含量上升和感官得分的下降；减缓整鱼及鱼片在微冻过程中总巯基含量、Ca2＋-ATPase活性下降和水分流失；此外FVP还能够延缓大黄鱼因腐败而出现的蛋白凝胶能力减弱。在本实验选取的多糖浓度范围内，0.09 g/L FVP处理组保鲜效果较强。该研究结果可为FVP用于水产品贮运保鲜提供理论参考。     

High-resolution FIB-TEM-STEM structural characterization of grain boundaries in the high dielectric constant perovskite CaCu3Ti4O12

In this work, the grain boundaries composition of the polycrystalline CaCu₃Ti₄O₁₂ (CCTO) was investigated. A Focused Ion Beam (FIB)/lift-out technique was used to prepare site-specific thin samples of the grain boundaries interface of CCTO ceramics. Scanning transmission electron microscopy (STEM) coupled with energy dispersive X-ray spectrometry (EDXS) and Electron Energy Loss Spectroscopy (EELS) systems were used to characterize the composition and nanostructure of the grain and grain boundaries region. It is known that during conventional sintering, discontinuous grain growth occurs and a Cu-rich phase appears at grain boundaries. This Cu-rich phase may affect the final dielectric properties of CCTO but its structure and chemical composition remained unknown. For the first time, this high-resolution FIB-TEM-STEM study of CCTO interfacial region highlights the composition of the phases segregated at grain boundaries namely CuO, Cu₂O and the metastable phase Cu₃TiO_4.     

In2O3:Ga and In2O3:P-based one-electrode gas sensors: Comparative study

Gas sensing characteristics of one-electrode sensors based on the In₂O₃ ceramics doped by gallium and phosphorus have been discussed. In₂O₃-based ceramic was prepared by sol–gel technology. Ozone, CO, CH₄ and H₂ were used as tested gases. The doping concentration effect on the sensor parameters such as magnitude of response, operating temperature, response and recovery times, sensitivity to the air humidity, and selectivity have been analyzed. It was shown that In₂O₃ doping by Ga and P could be used for the sensor performance optimization. It was assumed that the appearance of the second phase (InPO₄ and Ga₂O₃) and the change of structural parameters, taking place during doping process, were the main factors controlling the change of operating characteristics in In₂O₃:P and In₂O₃:Ga-based sensors.     

Multi-innovation auto-constructed least squares identification for 4 DOF ship manoeuvring modelling with full-scale trial data

This research is concerned with the problem of 4 degrees of freedom (DOF) ship manoeuvring identification modelling with the full-scale trial data. To avoid the multi-innovation matrix inversion in the conventional multi-innovation least squares (MILS) algorithm, a new transformed multi-innovation least squares (TMILS) algorithm is first developed by virtue of the coupling identification concept. And much effort is made to guarantee the uniformly ultimate convergence. Furthermore, the auto-constructed TMILS scheme is derived for the ship manoeuvring motion identification by combination with a statistic index. Comparing with the existing results, the proposed scheme has the significant computational advantage and is able to estimate the model structure. The illustrative examples demonstrate the effectiveness of the proposed algorithm, especially including the identification application with full-scale trial data.     

Mechanical properties of graphene films enhanced by homo-telechelic functionalized polymer fillers via π–π stacking interactions

Most researches on graphene/polymer composites are focusing on improving the mechanical and electrical properties of polymers at low graphene content instead of paying attention to constructing graphene’s macroscopic structures. In current study the homo-telechelic functionalized polyethylene glycols (FPEGs) were tailored with π-orbital-rich groups (namely phenyl, pyrene and di-pyrene) via esterification reactions, which enhanced the interaction between polyethylene glycol (PEG) molecules and chemical reduced graphene oxide (RGO) sheets. The π–π stacking interactions between graphene sheets and π-orbital-rich groups endowed the composite films with enhanced tensile strength and tunable electrical conductivity. The formation of graphene network structure mediated by the FPEGs fillers via π–π stacking non-covalent interactions should account for the experimental results. The experimental investigations were also complemented with theoretical calculation using a density functional theory. Atomic force microscope (AFM), scanning electron microscope (SEM), X-ray diffraction (XRD), nuclear magnetic resonance (NMR), thermal gravimetric analysis (TGA), UV–vis and fluorescence spectroscopy were used to monitor the step-wise preparation of graphene composite films.     

Strength and dilatancy behaviors of deep sands in Shanghai with a focus on grain size and shape effect

With rapid development of infrastructures like tunnels and open excavations in Shanghai, investigations on deeper soils have become critically important. Most of the existing laboratory works were focused on the clayey strata up to Layer 6 in Shanghai, i.e. at depth of up to 40 m. In this paper, Layers 7, 9, and 11, which were mostly formed of sandy soils at depth of up to 150 m, were experimentally investigated with respect to physico-mechanical behaviors. The stress–strain behaviors were analyzed by the consolidated drained/undrained (CD/CU) triaxial tests under monotonic loading. One-dimensional (1D) oedometer tests were performed to investigate the consolidation properties of the sandy soils. Specimens were prepared at three different relative densities for each layer. Also, the micro-images and particle size analyzers were used to analyze the shape and size of the sand grains. The influences of grain size, density, and angularity on the stress–strain behaviors and compressibility were also studied. Compared to the other layers, Layer 11 had the smallest mean grain size (D₅₀), highest compressibility, and lowest shear strength. In contrast, Layer 9 had the largest mean grain size, lowest compressibility, and highest shear strength. Layer 7 was of intermediate mean grain size, exhibiting more compressibility and less shear strength than that of Layer 9. Also, the critical state parameters and maximum dilatancy rate of different layers were discussed.     

Prediction of process parameters of water-assisted injection molding based on inverse radial basis function neural network

Because of the introduction of new processing parameters in water-assisted injection molding (WAIM), processes control has become more difficult. First, design of experiment (DOE) was carried out by using optimized Latin hypercubes (Opt LHS). On the basis of this, computational fluid dynamics (CFD) method was used to simulate and calculate hollowed core ratios and wall thickness differences of cooling water pipe at different positions. Then inverse radial basis function (RBF) neural network model reflecting the fitting relationship between processing parameters and molding quality was established, and accuracy of the model was detected by cross validation. Finally, expected molding quality was applied to predict processing parameters, and the obtained molding quality under the predicted processing parameters was verified by computer aided engineering (CAE) simulation and experimental methods. The results showed that mean relative precisions of processing parameters such as melt temperature, delay time, short shot size, water pressure, and mold temperature for inverse RBF model were 98.6%, 93.6%, 98.5%, 93.9%, and 97.9%, respectively, which met the accuracy requirements. Furthermore, compared with expected values of hollowed core ratios and wall thickness differences, the average errors of CAE and experiment were 2.3% and 4.9%, respectively.