Poly [2-(cinnamoyloxy)ethyl methacrylate-co-octamethacryl-POSS] nanocomposites: Synthesis and properties

Poly [2-(cinnamoyloxy)ethyl methacrylate-co-octamethacryl-POSS] nanocomposites were synthesized from octamethacryl-POSS and 2-(cinnamoyloxy)ethyl methacrylate (CEM) by free radical polymerization. The chemical structures and morphologies of these nanocomposites were determined by FTIR, ²⁹Si NMR, energy-dispersive spectroscopy (EDS), X-ray powder diffraction (XRD), and scanning electron microscopy (SEM) techniques. The XRD data showed that the materials were amorphous in nature, indicating that POSS formed an aggregate instead of a crystalline form in the polymer matrix. The POSS-CEM nanocomposites exhibited high thermal stability. Excitation and emission of the CEM-incorporated POSS nanocomposites, studied in the solid state, exhibited blue emission with CIE (x, 0.178; y, 0.137) coordinates, in addition to an emission intensity that increased with increasing CEM (monomer) concentration.     

Synthesis of boron nitride nanotubes using thermal chemical vapor deposition of ball milled boron powder

Thermal chemical vapor deposition (TCVD) produces relatively pure boron nitride nanotubes (BNNTs), while it may not be enough for production. A ball milling–annealing process is beneficial in quantity; however, the generation of the impurity is most concerned. In this regard, we explored the dual syntheses of BNNTs based on the chemical reactions of solid and gaseous boron with nitrogen, respectively. Synthesis mechanisms for both were also analyzed by presenting the stepwise reaction routes. With these understandings, the production of BNNTs could be increased, while the impurities are reduced when compared with the individual one, once the experimental conditions are optimized.     

Training Algorithm with Incomplete Data for Feed-Forward Neural Networks

A new algorithm is developed to train feed-forward neural networks for non-linear input-to-output mappings with small incomplete data in arbitrary distributions. The developed Training-EStimation-Training (TEST) algorithm consists of 3 steps, i.e., (1) training with the complete portion of the training data set, (2) estimation of the missing attributes with the trained neural networks, and (3) re-training the neural networks with the whole data set. Error back propagation is still applicable to estimate the missing attributes. Unlike other training methods with missing data, it does not assume data distribution models which may not be appropriate for small training data. The developed TEST algorithm is first tested for the Iris benchmark data. By randomly removing some attributes from the complete data set and estimating the values latter, accuracy of the TEST algorithm is demonstrated. Then it is applied to the Diabetes benchmark data, of which about 50% contains missing attributes. Compared with other existing algorithms, the proposed TEST algorithm results in much better recognition accuracy for test data.     

Local Behavior of Deposition Velocity onto a Flat Plate in Horizontal Airflow under the Influence of Thermophoresis

In this study, the Gaussian Diffusion Sphere Model (GDSM) and the Statistical Lagrangian Particle Tracking (SLPT) approach were employed and adjusted to calculate the local deposition velocity onto a flat plate in horizontal airflow. The GDSM and the SLPT approach were validated by comparing the predicted local deposition velocities with those determined by solving the equation of convective diffusion. Both the GDSM and the SLPT approach were found to be accurate in calculating the local deposition velocity onto a flat plate in horizontal airflow. In addition, the GDSM was much more efficient than the SLPT approach in terms of the calculation time. Finally, a parametric study on the local deposition velocity onto a flat plate exposed to horizontal airflow was performed using the GDSM with the consideration of the effects of the gravity, convection, diffusion, and thermophoresis.

Copyright 2015 American Association for Aerosol Research     

Identification of naturally isolated Southern Louisiana's algal strains and the effect of higher CO2 content on fatty acid profiles for biodiesel production

BACKGROUND: Microalgae, with both high biomass productivity and oil content, are regarded as attractive candidates for the production of alternative biodiesel as well as for CO₂ biofixation. In the present study, four microalgal strains native to southeastern Louisiana's waters were isolated and identified to evaluate their potential for the production of biodiesel. Selected strains were identified through genomic DNA in sequencing of either 16S rRNA or 18S rRNA genes followed by lipid and fatty acid content characterization and quantification. RESULTS: High correlation was found with known nucleotide sequence identities at 98% with Sellaphora pupula, and 99% with Synechococcus sp., Chlorella sorokiniana, Scenedesmus abundans, and Chlorella vulgaris (control). The fatty acid profiles of these organisms changed when using 5% CO₂ aeration. Total fatty acids (TFA) decreased from 20.63 to 17.62, 54.83 to 24.4, and 29.82 to 23.99 g kg^?1 in Synechococcus sp., Sellaphora pupula and Chlorella sorokiniana, respectively. TFA increased from 14.14 to 31.49 and 15.14 to 47.52 g kg^?1 dry biomass in Scenedesmus abundans and Chlorella vulgaris (control), respectively. CONCLUSION: Chlorella sorokiniana, with a lower C18:3 and the highest biomass yield at 5% CO₂ aeration, was found to be the best candidate for biodiesel production. © 2012 Society of Chemical Industry     

Effects of mechanical force on crystalline structure of electrospun poly(1‐butene) membranes

Fibrous poly(1‐butene) membranes were prepared from solutions using mixed solvents via electrospinning. The electrospun fibrous poly(1‐butene) membranes were stretched at elongations of 8, 16 and 24%, respectively. Scanning electron microscopy and stress–strain analysis showed that the deformation was elastic at an elongation of 8%, while plastic deformation occurred above 16%. The crystalline transformation induced by mechanical stretching was investigated using wide‐angle X‐ray diffraction. The poly(1‐butene) membrane stretched at 24% exhibited decreased diffraction intensity at 2θ = 11.8° and 18.3°, corresponding to the (200) and (213) crystalline reflections, indicating that crystalline transformation from metastable form II to either stable form I or I′ occurred due to mechanical stretching. Copyright © 2011 Society of Chemical Industry