Melt intercalation/exfoliation of polystyrene-sodium-montmorillonite nanocomposites using sulfonated polystyrene ionomer compatibilizers

Quaternary ammonium salts of sulfonated polystyrene (SPS) were used as compatibilizers for melt intercalation of PS and pristine Na-montmorillonite. Tetra-octyl ammonium SPS and tetra-decyl ammonium SPS ionomeric compatibilizers produced significant exfoliation and a homogeneous dispersion of the polymer-clay nanocomposites. Wide angle X-ray diffraction and transmission electron microscopy were primarily used to characterize the morphology of the nanocomposites. Image analysis was used to measure the percentage exfoliation. Exfoliation increased with the increasing length of the alkyl chain of the ammonium counter-ion of the SPS ionomer. The nanocomposites containing ionomers exhibited higher storage moduli compared to nanocomposites without the compatibilizer.     

Esterification of acrylic acid with methanol by reactive chromatography: Experiments and simulations

The esterification of acrylic acid with methanol using Amberlyst 15 as a stationary phase has been investigated using a chromatographic reactor. Several experimental runs at various operating conditions have been conducted on a batch column. A classical reactive chromatography model including lumped kinetics, a linear driving force transport model and a heterogeneous kinetic model for the catalytic reaction has been developed. The additional dispersion of concentration fronts due to density gradient effects has been accounted for in the model. The model parameters have been determined in a fast and reliable way by directly fitting the batch column experiments. In general, a good agreement between experimental and calculated results is obtained. The evaluation of the covariance of the fitted model parameters reveals important insights about the system behavior.Based on the detailed batch column model, a complete model of a simulated-moving-bed reactor has been implemented and its optimal point of operation for the synthesis of methyl acrylate from acrylic acid has been determined. Particularly when considering the low-operating temperature, we can regard this process as a possible competition for current technologies.     

Hydrogen Yield from Low Temperature Steam Reforming of Ethanol

Low temperature steam reforming of ethanol in the temperature range of 200–360°C was studied to maximize the production of H₂. The optimum reaction conditions in presence of a suitable catalyst can produce mainly the desired products H₂ and CO₂. Cu/Al₂O₃ catalysts with six different concentrations ranging from 0 to 10 wt.% Mn, were prepared, characterized and studied for the ethanol-steam reforming reaction. Maximum ethanol conversion of 60.7% and hydrogen yield of 3.74 (mol H₂ / mol ethanol converted) were observed at 360°C for catalyst with 2.5 wt.% Mn loading.     

An Authentication Based Approach for Prevention of Spectrum Sensing Data Falsification Attacks in Cognitive Radio Network

Wireless Personal Communications - This is the era of Intelligent Cognitive Radio Network (CRN) technology that provides the available spectrum with efficient utilization. Cognitive Radio (CR)...     

Self-nanoemulsifying drug delivery system of docosahexanoic acid: development,in vitro,in vivo characterization

Context: Docosahexanoic acid (DHA) is an essential omega-3 fatty acid for normal brain development and its use has increased considerably in recent years.

Objective: The aim of this study is to develop and evaluate self-nanoemulsifying drug delivery systems (SNEDDS) of DHA for improved palatability, dispersibility and bioavailability.

Methods: The SNEDDS were prepared and evaluated for miscibility, employing different combinations of olive oil and soyabean oil as oil phase, Span 80, Span 20, soya phosphatidylcholine, Labrafil M 1944 CS as surfactants while Tween 80, PEG 400, Cremophor RH40 and propylene glycol as cosurfactants. Thermodynamically stable SNEDDS were characterized for dispersibility, self-emulsification time, droplet size, zeta potential along with sensory analysis. The optimized formulation was subjected to ex vivo and in vivo evaluation such as intestinal permeability, memory performance test, brain concentration and histopathology studies.

Results: The optimized SNEDDS formulation showed emulsification time of 27?±?4.7?s with droplet size of 17.6?±?3.5?nm and zeta potential of??37.6?±?0.5?mV. Intestinal absorption study depicted 18.3%, 21.5%, 41.5%, 98.7% absorption of DHA with SNEDDS-based formulation in comparison to 8.2%, 15.1%, 28.8%, 46.1% absorption of DHA with oil-based marketed formulation after 0.5, 1, 2 and 4?h. DHA concentration in brain homogenate was found to be increased to 2.6-fold in comparison to DHA-marketed formulation. This could be ascribed to enhanced dispersibility and bioavailability of DHA from nanosized formulation.

Conclusion: The developed formulation led to enhanced dispersibility and bioavailability of DHA due to the formation of nanodroplets.     

Machine Learning-Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Electrohydrodynamic (EHD) processes are promising techniques for manufacturing nanoscopic products with different shapes (such as thin films, nanofibers, 2D/3D nanostructures, and nanoparticles) and materials at a low cost using simple equipment. A key challenge in their adoption by nonexperts is the requirement of enormous time and resources in identifying the optimum design/process parameters for the underlying material and EHD system. Machine learning (ML) has made exciting advancements in predictive modeling of different processes, provided it is trained on high-quality datasets at appropriate volumes. This article extends the suitability of such ML-enabled approaches to a new technological domain of EHD spraying and drop-on-demand printing. Different ML models like ridge regression, random forest regression, support vector regression, gradient boosting regression, and multilayer perceptron are trained and their performance using evaluation metrics like RMSE and R2_score is examined. Tree-based algorithms like gradient boosting regression are found to be the most suitable technique for modeling EHD processes. The trained ML models show substantially higher accuracy (average error < 5%) in replicating these nonlinear processes as compared to previously reported scaling laws (average error ≈ 42%) and are well suited for predictive modeling/analysis of the underlying EHD system and process.     

Speed scaling with a solar cell

We consider the setting of a device that obtains its energy from a battery and some regenerative source such as a solar cell. We consider the speed scaling problem of scheduling a collection of tasks with release times, deadlines, and sizes, so as to minimize the energy recharge rate of the regenerative source. This is the first theoretical investigation of speed scaling for devices with a regenerative energy source. We show that the problem can be expressed as a polynomial sized convex program. We show that, using the KKT conditions, one can obtain an efficient algorithm to verify the optimality of a schedule. We show that the energy optimal YDS schedule is 2-approximate with respect to the recharge rate. We show that the online algorithm BKP is O(1)$O\left(1\right)$-competitive with respect to recharge rate.     

Automated process planning method to machine A B-Spline free-form feature on a mill–turn center

In this paper, we present a methodology for automating the process planning and NC code generation for a widely encountered class of free-form features that can be machined on a 3-axis mill–turn center. The free-form feature family that is considered is that of extruded protrusions whose cross-section is a closed, periodic B-Spline curve. In this methodology, for machining a part with B-Spline protrusion located at the free end, the part is first rough turned to the maximum profile diameter of the B-Spline, followed by rough profile cutting and finish profiling with axially mounted end mill tools. The identification and sequencing of machining volumes is completely automated, as is the generation of actual NC code. The approach supports both convex and non-convex profiles. In the case of non-convex profiles, the process planning algorithm ensures that there is no gouging of the work piece by the tool. The algorithm also identifies when sections of the tool path lie outside the work piece and utilizes rapid traverses in these regions to reduce cutting time. This methodology presents an integrated turn–mill process planning where by making the process fully automated from design with no user intervention making the overall process planning efficient. The algorithm was tested on several examples and test parts using the unmodified NC code obtained from the implementation were run on a Moriseiki mill–turn center. The parts that were produced met the dimensional specifications of the desired part.     

Relational mountain (density) clustering method and web log analysis

The mountain clustering method and the subtractive clustering method are useful methods for finding cluster centers based on local density in object data. These methods have been extended to shell clustering. In this article, we propose a relational mountain clustering method (RMCM), which produces a set of (proto) typical objects as well as a crisp partition of the objects generating the relation, using a new concept that we call relational density. We exemplify RMCM by clustering several relational data sets that come from object data. Finally, RMCM is applied to web log analysis, where it produces useful user profiles from web log data. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 375–392, 2005.