Effect of Micro-Structured Copper as Cathode Material for P3HT-Based Diode

Here, the effect of micro-structured cathode material on the device performance of indium tin oxide/poly(3hexylethiophene)/copper diode (ITO/P3HT/Cu) is investigated. Two different forms of copper namely bulk metal (Cu{B}) and nanoparticle (Cu{N}) were used as top electrode to probe its effect on device performance. Crystallographic structure and nanoscale morphology of top Cu electrodes were characterized using X-ray diffraction and scanning electronmicroscopy. Electrode formed by evaporation of copper nanoparticle showed enhancement in current density. From capacitance based spectroscopy we observed that density of trap states in ITO/P3HT/copper larger size grain (Cu-LG) are one order greater than that in ITO/P3HT/copper smaller size grain (Cu-SG) device.

Observation of photobleaching and intensity dependent kinetics in Ge22As22Se56 thin films under sub-bandgap light illumination

We experimentally demonstrate photobleaching (PB) in Ge22As22Se56 thin films, when illuminated with a diode pumped solid state laser (DPSSL) of wavelength 671 nm, which is far below the optical bandgap of the sample. Interestingly, we found that PB is a slow process and occurs even at moderate pump beam intensity of 0.2 W/cm(2), however the kinetics remain rather different.

Adaptive executions of hyperbolic block-structured AMR applications on GPU systems

A block-structured adaptive mesh refinement (AMR) technique has been used to obtain numerical solutions for many scientific applications. Some block-structured AMR approaches have focused on forming patches of non-uniform sizes where the size of a patch can be tuned to the geometry of a region of interest. In this paper, we develop strategies for adaptive execution of block-structured AMR applications on GPUs, for hyperbolic directionally split solvers. While effective hybrid execution strategies exist for applications with uniform patches, our work considers efficient execution of non-uniform patches with different workloads. Our techniques include bin-packing work units to load balance GPU computations, adaptive asynchronism between CPU and GPU executions using a knapsack formulation, and scheduling communications for multi-GPU executions. Our experiments with synthetic and real data, for single-GPU and multi-GPU executions, on Tesla S1070 and Fermi C2070 clusters, show that our strategies result in up to a 3.23 speedup in performance over existing strategies.

Effective Degradation of Aqueous Nitrobenzene Using the SrFeO3-delta Photocatalyst under UV Illumination and Its Kinetics and Mechanistic Studies

In this article, the SrFeO3-delta photocatalyst was synthesized by a solution combustion method and applied for the photocatalytic degradation of aqueous nitrobenzene in the presence and absence of H2O2. The SrFeO3-delta photocatalyst was characterized by XRD, FT-IR, FE-SEM, TEM, TG-DTG, XPS, and UV visible spectroscopy. The band gap energy of SrFeO3-delta was found to be 3.75 eV which lies in the UV region. The XPS results indicate that the oxidation state of Sr and Fe in SrFeO3-delta was 2+ and 3+, respectively, and the surface atomic ratio of Sr and Fe is 0.995. The photocatalytic activity reveals that the degradation of nitrobenzene over the SrFeO3-delta catalyst itself (UV/SFO) is superior compared to SrFeO3-delta in the presence of H2O2 (UV/SFO/H2O2) with a degradation efficiency of 99-96%. The degradation of nitrobenzene obeys first-order kinetics in both UV/SFO and UV/SFO/H2O2 processes. The decrease in degradation efficiency with UV/SFO/H2O2 was attributed due to the formation of strontium carbonate on the photocatalyst surface.

Low Resolution Face Recognition Across Variations in Pose and Illumination

We propose a completely automatic approach for recognizing low resolution face images captured in uncontrolled environment. The approach uses multidimensional scaling to learn a common transformation matrix for the entire face which simultaneously transforms the facial features of the low resolution and the high resolution training images such that the distance between them approximates the distance had both the images been captured under the same controlled imaging conditions. Stereo matching cost is used to obtain the similarity of two images in the transformed space. Though this gives very good recognition performance, the time taken for computing the stereo matching cost is significant. To overcome this limitation, we propose a reference-based approach in which each face image is represented by its stereo matching cost from a few reference images. Experimental evaluation on the real world challenging databases and comparison with the state-of-the-art super-resolution, classifier based and cross modal synthesis techniques show the effectiveness of the proposed algorithm.