518 research outputs found
Oxide-Supported IrNiO<sub>x</sub> 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 FeGe<sub>2</sub> 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
- …