Oxide-Supported IrNiO_x Core-Shell Particles as Efficient, Cost-Effective, and Stable Catalysts for Electrochemical Water Splitting

Active and highly stable oxide-supported IrNiOx core–shell catalysts for electrochemical water splitting are presented. IrNix@IrOx nanoparticles supported on high-surface-area mesoporous antimony-doped tin oxide (IrNiOx /Meso-ATO) were synthesized from bimetallic IrNix precursor alloys (PA-IrNix /Meso-ATO) using electrochemical Ni leaching and concomitant Ir oxidation. Special emphasis was placed on Ni/NiO surface segregation under thermal treatment of the PA-IrNix /Meso-ATO as well as on the surface chemical state of the particle/oxide support interface. Combining a wide array of characterization methods, we uncovered the detrimental effect of segregated NiO phases on the water splitting activity of core–shell particles. The core–shell IrNiOx /Meso-ATO catalyst displayed high water-splitting activity and unprecedented stability in acidic electrolyte providing substantial progress in the development of PEM electrolyzer anode catalysts with drastically reduced Ir loading and significantly enhanced durability

Power-law distributions in empirical data

Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.Comment: 43 pages, 11 figures, 7 tables, 4 appendices; code available at http://www.santafe.edu/~aaronc/powerlaws

Fluctuation-driven capacity distribution in complex networks

Maximizing robustness and minimizing cost are common objectives in the design of infrastructure networks. However, most infrastructure networks evolve and operate in a highly decentralized fashion, which may significantly impact the allocation of resources across the system. Here, we investigate this question by focusing on the relation between capacity and load in different types of real-world communication and transportation networks. We find strong empirical evidence that the actual capacity of the network elements tends to be similar to the maximum available capacity, if the cost is not strongly constraining. As more weight is given to the cost, however, the capacity approaches the load nonlinearly. In particular, all systems analyzed show larger unoccupied portions of the capacities on network elements subjected to smaller loads, which is in sharp contrast with the assumptions involved in (linear) models proposed in previous theoretical studies. We describe the observed behavior of the capacity-load relation as a function of the relative importance of the cost by using a model that optimizes capacities to cope with network traffic fluctuations. These results suggest that infrastructure systems have evolved under pressure to minimize local failures, but not necessarily global failures that can be caused by the spread of local damage through cascading processes

In Situ Formation of Crystallographically Oriented Semiconductor Nanowire Arrays via Selective Vaporization for Optoelectronic Applications

Direct transformation of bulk crystals to single-crystalline crystallographically oriented semiconductor nanowire arrays is presented. Real-time imaging during in situ environmental scanning electron microscopy experiment clearly demonstrates that the nanowire arrays form through a selective vaporization process with respect to the crystallography of wurtzite crystals. Due to the high quality of the prepared semiconductor nanowire arrays, photodetectors constructed from them can present superior optoelectronic performances

Ordered structure of FeGe₂ formed during solid-phase epitaxy

Fe3Si/Ge(Fe,Si)/Fe3Si thin-film stacks were grown by a combination of molecular beam epitaxy and solid-phase epitaxy (Ge on Fe3Si). The stacks were analyzed using electron microscopy, electron diffraction, and synchrotron x-ray diffraction. The Ge(Fe,Si) films crystallize in the well-oriented, layered tetragonal structure FeGe2 with space group P4mm. This kind of structure does not exist as a bulk material and is stabilized by the solid-phase epitaxy of Ge on Fe3Si. We interpret this as an ordering phenomenon induced by minimization of the elastic energy of the epitaxial film

Large Scale Cross-Correlations in Internet Traffic

The Internet is a complex network of interconnected routers and the existence of collective behavior such as congestion suggests that the correlations between different connections play a crucial role. It is thus critical to measure and quantify these correlations. We use methods of random matrix theory (RMT) to analyze the cross-correlation matrix C of information flow changes of 650 connections between 26 routers of the French scientific network `Renater'. We find that C has the universal properties of the Gaussian orthogonal ensemble of random matrices: The distribution of eigenvalues--up to a rescaling which exhibits a typical correlation time of the order 10 minutes--and the spacing distribution follow the predictions of RMT. There are some deviations for large eigenvalues which contain network-specific information and which identify genuine correlations between connections. The study of the most correlated connections reveals the existence of `active centers' which are exchanging information with a large number of routers thereby inducing correlations between the corresponding connections. These strong correlations could be a reason for the observed self-similarity in the WWW traffic.Comment: 7 pages, 6 figures, final versio