The structure of graphite oxide (GO) has been systematically studied using various tools such as SEM, TEM, XRD, Fourier transform infrared spectroscopy (FT-IR), X-ray photoemission spectroscopy (XPS), (13)C solid-state NMR, and O K-edge X-ray absorption near edge structure (XANES). The TEM data reveal that GO consists of amorphous and crystalline phases. The XPS data show that some carbon atoms have sp(3) orbitals and others have sp(2) orbitals. The ratio of sp(2) to sp(3) bonded carbon atoms decreases as sample preparation times increase. The (13)C solid-state NMR spectra of GO indicate the existence of -OH and -O- groups for which peaks appear at 60 and 70 ppm, respectively. FT-IR results corroborate these findings. The existence of ketone groups is also implied by FT-IR, which is verified by O K-edge XANES and (13)C solid-state NMR. We propose a new model for GO based on the results; -O-, -OH, and -C=O groups are on the surface.

Interfacial spin glass state and exchange bias in manganite bilayers with competing magnetic orders

Graph algorithms are becoming increasingly important for analyzing large datasets in many fields. Real-world graph data follows a pattern of sparsity, that is not uniform but highly skewed towards a few items. Implementing graph traversal, statistics and machine learning algorithms on such data in a scalable manner is quite challenging. As a result, several graph analytics frameworks (GraphLab, CombBLAS, Giraph, SociaLite and Galois among others) have been developed, each offering a solution with different programming models and targeted at different users. Unfortunately, the "Ninja performance gap" between optimized code and most of these frameworks is very large (2-30X for most frameworks and up to 560X for Giraph) for common graph algorithms, and moreover varies widely with algorithms. This makes the end-users' choice of graph framework dependent not only on ease of use but also on performance. In this work, we offer a quantitative roadmap for improving the performance of all these frameworks and bridging the "ninja gap". We first present hand-optimized baselines that get performance close to hardware limits and higher than any published performance figure for these graph algorithms. We characterize the performance of both this native implementation as well as popular graph frameworks on a variety of algorithms. This study helps endusers delineate bottlenecks arising from the algorithms themselves vs. programming model abstractions vs. the framework implementations. Further, by analyzing the system-level behavior of these frameworks, we obtain bottlenecks that are agnostic to specific algorithms. We recommend changes to alleviate these bottlenecks (and implement some of them) and reduce the performance gap with respect to native code. These changes will enable end-users to choose frameworks based mostly on ease of use.

Graphite oxide is an amorphous insulator. Although several models have been suggested, its structure remains controversial. To elucidate this issue, 5 samples were prepared by the Brodie process and the Staudenmaier process. The electronic structure of graphite oxide was examined with x-ray absorption near edge structure and the ratio of sp 2 to sp 3 bonded carbon atoms was investigated with x-ray photoemission spectroscopy as a function of sample preparation times. It was found that this ratio approaches 0.3 exponentially with a characteristic time of 1.5 weeks. We believe this long characteristic time is the reason the structure has remained unclear.