The effect of recycling number on the mechanical,chemical, thermal,and rheological properties of PBT/PC/ABS ternary blends: With and without glass‐fiber

The recycling possibilities of poly(butylene terephthalate)/polycarbonate/acrylonitrile–butadiene–styrene (PBT/PC/ABS) ternary blend with and without glass‐fiber content were investigated using repeated injection molding process. In this study, PBT/PC/ABS ternary blends were reprocessed at five times and the results were presented after each recycling process. The recycling possibility of PBT/PC/ABS ternary blend was evaluated by measuring the mechanical, chemical, thermal, and rheological properties. Mechanical properties were determined by the tensile strength, yield strength, strain at break, elastic modulus, impact strength, flexural strength, and flexural modulus. Chemical and thermal properties were evaluated by Fourier transform infrared spectroscopy, differential scanning calorimetry, thermal gravimetric analysis, and scanning electron microscopy. Rheological properties of the ternary blends were studied by melt flow index measurement. From the results, it was found that mechanical properties of recycled composites were better than virgin PBT/PC/ABS ternary blends. POLYM. COMPOS., 35:2074–2084, 2014. © 2014 Society of Plastics Engineers     

Petrochemicals from ethanol over a W-Si-based nanocomposite bidisperse solid acid catalyst

Very high ethylene selectivity values approaching 100% and very high ethanol conversion values approaching 85% were obtained in dehydration of ethanol over a new W-silicate-based nanocomposite catalyst having both meso and macropores and containing a W/Si atomic ratio of 0.85. Silicotungsticacid was successfully incorporated into the catalyst structure following a one-pot hydrothermal synthesis procedure. This catalyst is highly stable and does not loose activity in polar solvents and it has a sufficiently high surface area for catalytic applications. Calcination temperature of the catalyst was found to have a very significant effect on the catalyst structure and also on its catalytic performance in ethanol dehydration. Maximum selectivity of the second major reaction product diethylether was obtained as 0.7 at 200 °C, with the catalyst which was calcined at 400 °C. Very high ethylene and diethylether yield values obtained in this study at different reaction conditions are highly promising for the production of petrochemicals from ethanol.     

Ultra-fast boriding of nickel aluminide

In this study, we explored the possibility of ultra-fast electrochemical boriding of nickel aluminide (Ni₃Al) in a molten borax electrolyte. Electrochemical boriding was performed at 950 °C for 15 min and at current densities ranging from 0.1 to 0.5 A/cm². The boride layers formed on the test samples were 50 to 260 μm thick depending on the current density. The mechanical, structural, and chemical characterization of the boride layers was carried out using a Vickers micro-hardness test machine, optical and scanning electron microscopes, and a thin film X-ray diffractometer. The hardness of boride layer was in the range from 800 to 1200 ± 50 HV depending on the load and the region from which the hardness measurements were taken. X-ray diffraction studies confirmed that the boride layers were primarily composed of Ni₃B, Ni₄B₃ and Ni₂₀AlB₁₄ phases. Structurally, the boride layer was very homogenous and uniformly thick across the borided surface area.     

Synthesis and characterization of V,Mo and Nb incorporated micro–mesoporous MCM-41 materials

Highly microporous metal-MCM-41 ordered mesoporous structure catalysts having different metal/Si (V, Mo, Nb) atomic ratios and combinations of metal sources were hydrothermally synthesized. The structural properties estimated using different techniques were found to be in agreement with each other. Metals were successfully incorporated into MCM-41 without deteriorating the ordered hexagonal structure. The metal ions in the synthesis solutions probably settled on the hydrophilic end of the template hence the metal incorporation resulted improvements in the micropore structure. Low loading of metals caused an increase in the surface area and pore volume values of the catalysts. The highest total (1310 m² g^?1) and micropore surface area values (1083 m² g^?1) were obtained by Nb incorporation. The micro- and mesopore dimensions of MCM-41 increased from 0.5 to 1.1 nm and from 2.5 to 2.8 nm, respectively, with metal incorporation. Low V/Si ratios and presence of Nb in the starting solution enhanced narrow mesopore size distribution. The pore dimension and wall thickness values estimated from nitrogen adsorption and X-ray diffraction methods were consistent with the corresponding values obtained using transmission electron microscopy.     

Recognizing plankton images from the shadow image particle profiling evaluation recorder

We present a system to recognize underwater plankton images from the shadow image particle profiling evaluation recorder (SIPPER). The challenge of the SIPPER image set is that many images do not have clear contours. To address that, shape features that do not heavily depend on contour information were developed. A soft margin support vector machine (SVM) was used as the classifier. We developed a way to assign probability after multiclass SVM classification. Our approach achieved approximately 90% accuracy on a collection of plankton images. On another larger image set containing manually unidentifiable particles, it also provided 75.6% overall accuracy. The proposed approach was statistically significantly more accurate on the two data sets than a C4.5 decision tree and a cascade correlation neural network. The single SVM significantly outperformed ensembles of decision trees created by bagging and random forests on the smaller data set and was slightly better on the other data set. The 15-feature subset produced by our feature selection approach provided slightly better accuracy than using all 29 features. Our probability model gave us a reasonable rejection curve on the larger data set.     

Chimeric ribonuclease as a source of human adapter protein for targeted drug delivery

Assembled modular complexes for targeted drug delivery can bebased on strong non-covalent interactions between a cargo modulecontaining an adapter protein and a docking tag fused to a targetingprotein. We have recently constructed a completely humanizedadapter/docking tag system based on interactions between 15amino acid (Hu-tag) and 110 amino acid (HuS) fragments of humanribonuclease I (RNase I). Although recombinant HuS can be expressedand refolded into a functionally active form, the purificationprocedure is cumbersome and expensive, and more importantly,it yields a significant proportion of improperly folded proteins.Here we describe engineering, high-yield expression, and purificationof a chimeric bovine/human RNase (BH-RNase) comprising 1–29N-terminal amino acids of bovine ribonuclease A and 30–127amino acids of human RNase I. Unlike RNase I, the chimeric BH-RNasecan be cleaved by either subtilisin or proteinase K betweenA20 and S21, providing a functionally active HuS. The HuS obtainedfrom chimeric BH-RNase differs from wild-type HuS by an N24Tsubstitution; therefore, we have reverted this substitutionby mutating N24 to T24 in BH-RNase. This BH-RNase mutant canalso be cleaved by subtilisin or proteinase K yielding wild-typeHuS. The affinity of HuS obtained from BH-RNase to Hu-tag isapproximately five times higher than that for recombinant HuS,reflecting a higher percentage of properly folded proteins. Received June 9, 2003; revised August 4, 2003; accepted August 28, 2003.     

Dynamic-scale model construction from range imagery

The construction of a surface model from range data may be undertaken at any point in a continuum of scales that reflects the level of detail of the resulting model. This continuum relates the construction parameters to the scale of the model. We propose methods to dynamically reprocess range data at different scales. The construction result from a single scale is automatically evaluated, causing reconstruction at a different scale when user-defined criteria are not met. We demonstrate our methods in constructing a planar b-rep space envelope (a scene representation) for over 400 range images. The experiments demonstrate the ability to construct 100 percent valid models, with the scale of detail within specified requirements     

A Scalable Framework For Segmenting Magnetic Resonance Images

A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets. Two types of modifications to create incremental versions of fuzzy c-means are discussed. They are much faster when compared to fuzzy c-means for medium to extremely large data sets because they work on successive subsets of the data. They are comparable in quality to application of fuzzy c-means to all of the data. The clustering algorithms coupled with inhomogeneity correction and smoothing are used to create a framework for automatically segmenting magnetic resonance images of the human brain. The framework is applied to a set of normal human brain volumes acquired from different magnetic resonance scanners using different head coils, acquisition parameters and field strengths. Results are compared to those from two widely used magnetic resonance image segmentation programs, Statistical Parametric Mapping and the FMRIB Software Library (FSL). The results are comparable to FSL while providing significant speed-up and better scalability to larger volumes of data.