Ultrathin Au nanowires supported on rGO/TiO2 as an efficient photoelectrocatalyst

We report the synthesis of stable rGO/TiO2/Au nanowire hybrids showing excellent electrocatalytic activity for ethanol oxidation. Phase-pure anatase TiO2 nanoparticles (similar to 3 nm) were grown on GO sheets followed by the growth of ultrathin Au nanowires leading to the formation of a multidimensional ternary structure (0-D TiO2 and 1-D Au on 2-D graphene oxide). The oleylamine used for the synthesis of the Au nanowires not only leads to stable Au nanowires anchored on the GO sheets but also leads to the functionalization and room temperature reduction of GO. Using control experiments, we delineate the role of the three components in the hybrid and show that there is a significant synergy. We show that the catalytic activity for ethanol oxidation primarily stems from the Au nanowires. While TiO2 triggers the formation of oxygenated species on the Au nanowire surface at a lower potential and also imparts photoactivity, rGO provides a conducting support to minimize the charge transfer resistance in addition to stabilizing the Au nanowires. Compared with nanoparticle hybrids, the nanowire hybrids display a much better electrocatalytic performance. In addition to high efficiency, the nanowire hybrids also show a remarkable tolerance towards H2O2. While our study has a direct bearing on fuel cell technology, the insights gained are sufficiently general such that they provide guiding principles for the development of multifunctional ternary hybrids.

Real-time maximum a-posteriori image reconstruction for fluorescence microscopy

Rapid reconstruction of multidimensional image is crucial for enabling real-time 3D fluorescence imaging. This becomes a key factor for imaging rapidly occurring events in the cellular environment. To facilitate real-time imaging, we have developed a graphics processing unit (GPU) based real-time maximum a-posteriori (MAP) image reconstruction system. The parallel processing capability of GPU device that consists of a large number of tiny processing cores and the adaptability of image reconstruction algorithm to parallel processing (that employ multiple independent computing modules called threads) results in high temporal resolution. Moreover, the proposed quadratic potential based MAP algorithm effectively deconvolves the images as well as suppresses the noise. The multi-node multi-threaded GPU and the Compute Unified Device Architecture (CUDA) efficiently execute the iterative image reconstruction algorithm that is similar to 200-fold faster (for large dataset) when compared to existing CPU based systems. (C) 2015 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.

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.