Liquid-rubber-modified epoxy adhesives cured with dicyandiamide. II.–Morphology and adhesion strengths

The lap shear and T-peel strengths of n-butylacrylate/acrylic acid (nBA/AA)-copolymer-modified DGEBA adhesive were evaluated. Adherends used in this study included carbon-fiber-reinforced plastics (CFRP), glass-fiber-reinforced plastics (GFRP), and aluminum. It was found that the adhesive strengths could be improved by incorporating nBA/AA copolymer in the DGEBA matrix. An optimum functionality of the copolymer existed for maximum lap shear strength. Morphological studies revealed that the adhesive strength was also influenced by the compastibility between the copolymer and the matrix, the property of the adherend as well as the addition of the bisphenol A.     

Determination of oil palm fruit phenolic compounds and their antioxidant activities using spectrophotometric methods

There is scarce information on the phenolics of oil palm fruits (Elaeis guineensis). In this study, phenolics were extracted from oil palm fruits and analysed using spectrophotometry for information on the different types of palm phenolics and their antioxidative activities. Analyses of the total phenolic content (TPC), total flavonoid content (TFC), o‐diphenols index, hydroxycinnamic acid index, flavonols index and phenol index showed ranges between 5.64 and 83.97 g L^?1 gallic acid equivalent (GAE), 0.31–7.53 g L^?1 catechin equivalent, 4.90–93.20 g L^?1 GAE, 23.74–77.46 g L^?1 ferulic acid equivalent, 3.62–95.33 g L^?1 rutin equivalent and 15.90–247.22 g L^?1 GAE, respectively. The antioxidant assay, 2,2‐diphenyl‐2‐picrylhydrazyl radical scavenging assay, showed antioxidative activities in all the extracts with results ranging from 4.41 to 61.98 g L^?1 trolox equivalent. The high antioxidant activities of the oil palm fruit phenolics were also found to increase with increasing TPC and TFC.     

Mechanical behavior and microstructural evolution upon annealing of the accumulative roll-bonding (ARB) processed Al alloy 1100

The results of mechanical testing on ultra-fine grained aluminum processed by accumulative roll-bonding (ARB) were analyzed with TEM observation and in accordance of the microstructural evolution upon annealing. It was found that rapid grain growth, with the corresponding decrease in strength, did not occur until the annealing temperature of 200 °C or higher. The oxide rolled into the material near the bonding interfaces was seen to act as an obstruction for grain boundary migration across said interfaces. More interestingly, the strain near the interface due to the surface preparation technique used during ARB was found to form discontinuous segregates consisting of smaller grains formed during annealing or even ARB processing of higher number of cycles. Such phenomenon is attributed to recovery or polygonization due to the strain incurred. This study has also demonstrated that yield point phenomenon may be observed in a commercially pure fcc metal when the grain size is within a certain range.     

Design of low-temperature co-fired ceramic bandpass filter with transmission zeros in the stopband

A multilayered bandpass filter with low-temperature co-fired ceramic technology is proposed. By integrating two types of filters as one filter, four transmission zeros can be generated. Each type of filter with a source-load coupling capacitor and a grounded coupling inductor, simultaneously, can generate two transmission zeros located on the opposite side of the passband skirt separately. Moreover, the fabricated filter provides good matching with the electromagnetic simulation and can prove the availability of the filter configuration.     

The effect of annealing and gold deposition on the performance of magnetoelastic biosensors

Magnetoelastic materials have a mass-sensitive, characteristic, resonant frequency that make them an ideal choice for application as the platform for a biosensor. The biosensor is formed by immobilizing a bio-molecular recognition element directly onto the surface of the magnetoelastic material. When exposed to the analyte, this bio-molecular recognition element binds the target species (spore or bacteria of interest), thereby increasing the mass and decreasing the resonance frequency of the sensor. This change in characteristic resonance frequency can be determined by monitoring remotely the magnetic flux in response to an applied, time varying, magnetic field with a pick-up coil. However, the environmental stability and bioactivity of the biosensor platform material are very important to performance of the overall biosensor. This research investigates the effects of annealing and deposition of a gold layer on the environmental stability and detection sensitivity of a magnetoelastic biosensor platform. The results show that deposition of a gold layer by sputtering additionally improved bioreactivity while maintaining a balance of environmental stability and detection sensitivity. Two annealing steps in a vacuum oven, one before and one after gold deposition, can effectively increase the magnetoelastic platform's environmental stability, as well as the Q-factor of the resonance signal.     

Advanced Agent Identification With Fluctuation-Enhanced Sensing

Conventional agent sensing methods normally use the steady state sensor values for agent classification. Many sensing elements (Hines , 1999; Ryan, 2004; Young, 2003;Qian, 2004; Qian, 2006; Carmel, 2003) are needed in order to correctly classify multiple agents in mixtures. Fluctuation-enhanced sensing (FES) looks beyond the steady-state values and extracts agent information from spectra and bispectra. As a result, it is possible to use a single sensor to perform multiple agent classification. This paper summarizes the application of some advanced algorithms that can classify and estimate concentrations of different chemical agents. Our tool involves two steps. First, spectral and bispectral features will be extracted from the sensor signals. The features contain unique agent characteristics. Second, the features are fed into a hyperspectral signal processing algorithm for agent classification and concentration estimation. The basic idea here is to use the spectral/bispectral shape information to perform agent classification. Extensive simulations have been performed by using simulated nanosensor data, as well as actual experimental data using commercial sensor (Taguchi). It was observed that our algorithms are able to accurately classify different agents, and also can estimate the concentration of the agents. Bispectra contain more information than spectra at the expense of high-computational costs. Specific nanostructured sensor model data yielded excellent performance because the agent responses are additive with this type of sensor. Moreover, for measured conventional sensor outputs, our algorithms also showed reasonable performance in terms of agent classification.     

Predicting the accumulated number of plugged tubes in a steam generator using statistical methodologies

A steam generator (SG) plays a significant role not only with respect to the primary-to-secondary heat transfer but also as a fission product barrier to prevent the release of radionuclides. Tube plugging is an efficient way to avoid releasing radionuclides when SG tubes are severely degraded. However, this remedial action may cause the decrease of SG heat transfer capability, especially in transient or accident conditions. It is therefore crucial for the plant staff to understand the trend of plugged tubes for the SG operation and maintenance. Statistical methodologies are proposed in this paper to predict this trend. The accumulated numbers of SG plugged tubes versus the operation time are predicted using the Weibull and log–normal distributions, which correspond well with the plant measured data from a selected pressurized water reactor (PWR). With the help of these predictions, the accumulated number of SG plugged tubes can be reasonably extrapolated to the 40-year operation lifetime (or even longer than 40 years) of a PWR. This information can assist the plant policymakers to determine whether or when a SG must be replaced.     

Feature significance for multivariate kernel density estimation

Multivariate kernel density estimation provides information about structure in data. Feature significance is a technique for deciding whether features-such as local extrema-are statistically significant. This paper proposes a framework for feature significance in d-dimensional data which combines kernel density derivative estimators and hypothesis tests for modal regions. For the gradient and curvature estimators distributional properties are given, and pointwise test statistics are derived. The hypothesis tests extend the two-dimensional feature significance ideas of Godtliebsen et al. [Godtliebsen, F., Marron, J.S., Chaudhuri, P., 2002. Significance in scale space for bivariate density estimation. Journal of Computational and Graphical Statistics 11, 1-21]. The theoretical framework is complemented by novel visualization for three-dimensional data. Applications to real data sets show that tests based on the kernel curvature estimators perform well in identifying modal regions. These results can be enhanced by corresponding tests with kernel gradient estimators.     

Escaping a Grid by Edge-Disjoint Paths

Abstract. We present a technique for designing external memory data structures that support batched operations I/ O efficiently. We show how the technique can be used to develop external versions of a search tree, a priority queue, and a segment tree, and give examples of how these structures can be used to develop I/ O-efficient algorithms. The developed algorithms are either extremely simple or straightforward generalizations of known internal memory algorithms—given the developed external data structures.     

Size effect in reactivity of copper nanoparticles to carbon tetrachloride degradation

Surface area-normalized rate constants (k(SA)) of reaction between metallic nanoparticles and reducible contaminants, such as chlorinated hydrocarbons, heavy metals, and nitrate, have been reported to be dramatically increased as compared to that of commercial metallic powder. However, k(SA) for individual pollutants in previously published data vary by as much as 1-2 orders of magnitude and much of this variability is due to the effect of various sizes. The size dependence of the reactivity of nanoparticles is not yet fully understood; however, yielding nanoparticles with uniform size and without agglomeration during the period of reaction would demonstrate the effect of varying particle size. In this study, resin-supported zerovalent copper with average particle size of 7, 10, 18, 26, and 29, respectively, were successfully synthesized and evidenced no agglomeration during the reaction period of 10h. The k(SA) of copper nanoparticles (k(n,SA)) was 110-120 times higher than that of powdered copper particles (k(p,SA)) when the copper particle size was about 10nm. However, for diameters of 18-29 nm, the ratio of k(n,SA)/k(p,SA) was around 10-20, indicating that the reactivity of small copper nanoparticles (approximately 10nm) varies discontinuously. Thus, most variability in previous k(SA) is attributed to the presence of small nanoparticles.