Synthesis of Co/SiO2 hybrid nanocatalyst via twisted Co3Si2O5(OH)4 nanosheets for high-temperature Fischer-Tropsch reaction

A cobalt-silica hybrid nanocatalyst bearing small cobalt particles of diameter ～5 nm was prepared through a hydrothermal reaction and hydrogen reduction.The resulting material showed very high CO conversion (＞82％) and high hydrocarbon productivity (～1.0 gHc·g-1cat,·h-11) with high activity (～8.5 x 10-5 molco·g-1Co·S-1) in the Fischer-Tropsch synthesis reaction.     

Improvement in mechanical properties of recycled GF prepreg with CNT reinforced composites using a spray coating method

Dispersion and shape of nanoparticles, as well as interfacial conditions, add significantly to difficulties in composite manufacture. In the work reported here, an innovative method of recycling composites using out-of-date prepreg was investigated in which the carbon nanotube (CNT) on the prepreg was optimally coated. Nanocomposites utilizing the out-of-date prepreg were coated with CNT and fabricated by a sheet molding method. CNT nanofillers were observed to be uniformly dispersed on epoxy prepreg by spray coating. The mechanical and interfacial properties of these CNT coated nanocomposites were improved over those of more conventionally manufactured carbon fiber/epoxy composites. The CNT nanofillers were embedded at the epoxy and fiber interface, as a result of etching of the epoxy prepreg surface by a CNT dispersion solution which enhanced interfacial reactivity.     

Surface modification of polyetherurethaneureas and their antithrombogenicity

Polyetherurethaneurea (PU) films were treated by oxygen plasma discharge followed by acrylic acid (AA) grafting. The carboxyl groups of the AA-grafted PU (PU-AA) surface were coupled with bovine serum albumin and heparin via water soluble carbodiimide. Surface characterization of the modified PUs was carried out by attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and electron spectroscopy for chemical analysis (ESCA). The amount of immobilized albumin and heparin on the PU surface was 1.8 and 1.5 g/cm², respectively, as determined by the dye interaction method. Interactions between the surface-modified PUs and blood components such as plasma proteins and platelets were investigated to evaluate the blood compatibility of the samples. Plasma recalcification time (PRT) and activated partial thromboplastin time (APTT) of the albumin-immobilized PU (PU-Al) were almost the same as those of PU, while platelets were less adhered on the PU-Al than on PU. On the other hand, PRT and APTT of the PU-He were significantly longer than those of the PU, PU-AA, and PU-Al. Moreover, adhesion of platelets was effectively suppressed on the PU-He, leading to good in vitro blood compatibility.     

Microstructure and fracture of SiC-particulate-reinforced cast A356 aluminum alloy composites

A microstructural analysis of local microfracture of cast A356 Al-SiC _p composites fabricated by permanent mold re-casting and squeeze-casting methods was made. Notch fracture toughness tests were conducted on these composites to identify critical fracture parameters using a stress-modified critical-strain criterion. The composite microstructure shows continuous networks of densely populated SiC and eutectic Si particles along the intercellular regions. Squeeze casting produces a more homogeneous structure and larger spacing of brittle particles and increases the tensile ductility and fracture toughness, while strength levels are almost identical to the re-casting case. The calculated values of the microstructurally characteristic distancel* for the re-cast and squeeze-cast composites are about 40 μm, which is comparable to the average sizes of the intercellular network. However, the reference critical strain for squeeze casting is larger than that for re-casting, showing a trend to higher ductility and fracture toughness.     

SSPQL: Stochastic shortest path-based Q-learning

Reinforcement learning (RL) has been widely used as a mechanism for autonomous robots to learn state-action pairs by interacting with their environment. However, most RL methods usually suffer from slow convergence when deriving an optimum policy in practical applications. To solve this problem, a stochastic shortest path-based Q-learning (SSPQL) is proposed, combining a stochastic shortest path-finding method with Q-learning, a well-known model-free RL method. The rationale is, if a robot has an internal state-transition model which is incrementally learnt, then the robot can infer the local optimum policy by using a stochastic shortest path-finding method. By increasing state-action pair values comprising of these local optimum policies, a robot can then reach a goal quickly and as a result, this process can enhance convergence speed. To demonstrate the validity of this proposed learning approach, several experimental results are presented in this paper.     

Simulation framework for small scale engagement

Developing future weapons systems has become increasingly complicated and costly. The armed forces of major nations use modeling and simulation techniques for new weapons systems from the conceptual stage to design, production, deployment and training stages to shorten the development cycle and guarantee their effectiveness. Failure in the development cycle carries too much loss in time and money. Therefore, computer-based modeling and simulation techniques are applied from the conceptual stage to gauge the efficacy of new weapons systems. The objective of this study is to develop a modeling and simulation methodology for small scale engagement using the DEVS formalism. The entities required for modeling and simulation are divided into three categories: combat, logical, and environmental entities. Combat entities represent the military hardware or combatants; logical entities represent the judgment and decision entities for the interaction between various entities; and environmental entities emulate the constituents of real combat environment. The combat entities are further modeled into Shell and Core Parts to maximize their reusability under various combat scenarios. The proposed framework is verified using a one-on-one combat engagement simulation (written in C++) between two submarines.     

Theoretical and experimental characterization of wettability of various nanolens arrayed polymer surfaces replicated with nanodimpled aluminum mold insert

Various types of polymer surfaces with a nanolens array, which has an entrant shape with a low aspect ratio, were fabricated and the wettability of the fabricated surfaces was evaluated in both theoretical and experimental ways. The nanolens array was replicated on three different polymer surfaces of polydimethylsiloxane (PDMS), cyclic olefin copolymer (COC), and polymethylmethacrylate (PMMA) by means of replica molding and hot embossing with a nanodimpled aluminum mold that was manufactured by a chemical oxidation process. From the theoretical and experimental evaluations of the wettability it was found that the measured contact angles were very similar with the theoretically estimated ones and also the hydrophilicity and hydrophobicity of the hydrophilic PMMA and hydrophobic PDMS, respectively, surfaces were reinforced by the nanolens array within the Wenzel wetting state.     

Optimization of optical lens-controlled scanning electron microscopic resolution using generalized regression neural network and genetic algorithm

A scanning electron microscope (SEM) is a sophisticated equipment employed for fine imaging of a variety of surfaces. In this study, prediction models of SEM were constructed by using a generalized regression neural network (GRNN) and genetic algorithm (GA). The SEM components examined include condenser lens 1 and 2 and objective lens (coarse and fine) referred to as CL1, CL2, OL-Coarse, and OL-Fine. For a systematic modeling of SEM resolution (R), a face-centered Box–Wilson experiment was conducted. Two sets of data were collected with or without the adjustment of magnification. Root-mean-squared prediction error of optimized GRNN models are GA 0.481 and 1.96×10^-12 for non-adjusted and adjusted data, respectively. The optimized models demonstrated a much improved prediction over statistical regression models. The optimized models were used to optimize parameters particularly under best tuned SEM environment. For the variations in CL2 and OL-Coarse, the highest R could be achieved at all conditions except a larger CL2 either at smaller or larger OL-Coarse. For the variations in CL1 and CL2, the highest R was obtained at all conditions but larger CL2 and smaller CL1.     

Organic wavelength‐converting‐film‐based hybrid planar white light‐emitting diodes

Abstract— In this study, organic wavelength‐converting films (WCFs) applied to InGaN blue LED‐based hybrid planar WLED has been fabricated. The organic dye layer in the WCF was formed between the upper and bottom polymer sheets by using a simple roll‐laminating technique. Subsequently, the hybrid planar WLEDs have been fabricated based upon these films. The luminous efficiency of green WCF‐based hybrid planar WLEDs with a single blue LED chip was 34.6 lm/W and that of red‐WCF‐assisted green WCF‐based hybrid planar WLEDs was 27.3 lm/W under 20 mA. The use of WCF to fabricate hybrid planar WLEDs showed better stability than that of directly coating organic color‐convergence materials (CCMs) on the LED chips. It only decreased to about 10% of the initial wavelength‐converting intensity after 1 hour of continual operation at 20 mA.