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

Highly optimized tolerance and power laws in dense and sparse resource regimes

Power law cumulative frequency $(P)$ vs. event size $(l)$ distributions $P(\geq l)\sim l^{-\alpha}$ are frequently cited as evidence for complexity and serve as a starting point for linking theoretical models and mechanisms with observed data. Systems exhibiting this behavior present fundamental mathematical challenges in probability and statistics. The broad span of length and time scales associated with heavy tailed processes often require special sensitivity to distinctions between discrete and continuous phenomena. A discrete Highly Optimized Tolerance (HOT) model, referred to as the Probability, Loss, Resource (PLR) model, gives the exponent $\alpha=1/d$ as a function of the dimension $d$ of the underlying substrate in the sparse resource regime. This agrees well with data for wildfires, web file sizes, and electric power outages. However, another HOT model, based on a continuous (dense) distribution of resources, predicts $\alpha= 1+ 1/d$. In this paper we describe and analyze a third model, the cuts model, which exhibits both behaviors but in different regimes. We use the cuts model to show all three models agree in the dense resource limit. In the sparse resource regime, the continuum model breaks down, but in this case, the cuts and PLR models are described by the same exponent.Comment: 19 pages, 13 figure

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

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

A Poissonian explanation for heavy-tails in e-mail communication

Patterns of deliberate human activity and behavior are of utmost importance in areas as diverse as disease spread, resource allocation, and emergency response. Because of its widespread availability and use, e-mail correspondence provides an attractive proxy for studying human activity. Recently, it was reported that the probability density for the inter-event time $\tau$ between consecutively sent e-mails decays asymptotically as $\tau^{-\alpha}$, with $\alpha \approx 1$. The slower than exponential decay of the inter-event time distribution suggests that deliberate human activity is inherently non-Poissonian. Here, we demonstrate that the approximate power-law scaling of the inter-event time distribution is a consequence of circadian and weekly cycles of human activity. We propose a cascading non-homogeneous Poisson process which explicitly integrates these periodic patterns in activity with an individual's tendency to continue participating in an activity. Using standard statistical techniques, we show that our model is consistent with the empirical data. Our findings may also provide insight into the origins of heavy-tailed distributions in other complex systems.Comment: 9 pages, 5 figure

Methane Decomposition and Carbon Growth on Y₂O₃, Yttria-Stabilized Zirconia, and ZrO₂

Carbon deposition following thermal methane decomposition under dry and steam reforming conditions has been studied on yttria-stabilized zirconia (YSZ), Y2O3 and ZrO2 by a range of different chemical, structural and spectroscopic characterization techniques, including aberration-corrected electron microscopy, Raman spectroscopy, electric impedance spectroscopy and volumetric adsorption techniques. Concordantly, all experimental techniques reveal the formation of a conducting layer of disordered nanocrystalline graphite covering the individual grains of the respective pure oxides after treatment in dry methane at temperatures T ≥ 1000 K. In addition, treatment under moist methane conditions causes additional formation of carbon-nanotube-like architectures by partial detachment of the graphite layers. All experiments show that during carbon growth, no substantial reduction of any of the oxides takes place. Our results therefore indicate that these pure oxides can act as efficient nonmetallic substrates for methane-induced growth of different carbon species with potentially important implications regarding their use in solid oxide fuel cells. By comparison of the three oxides we could moreover elucidate differences in the methane reactivities of the respective SOFC-relevant purely oxidic surfaces under typical SOFC operation conditions without the presence of metallic constituents

Phase coexistence of multiple copper oxides on AgCu catalysts during ethylene epoxidation

Alloy catalysts under reaction conditions are complex entities. In oxidizing atmospheres, multiple phases can coexist on a catalyst s surface as a result of phase segregation and preferential oxidation. Such a scenario can result in unusual substoichiometric and metastable phases that could play important roles in catalytic processes. For instance, AgCu alloys known to exhibit enhanced epoxide selectivity in partial oxidation of ethylene form an oxide like surface structure under reaction conditions. Under these conditions, copper oxides are stable, while silver oxides are not. Consequently, copper segregates to the alloy s surface and forms an oxide overlayer. Little is known about the structure or function of such overlayers, and it is unknown whether they play an active role in the catalyst s enhanced selectivity. In order to develop a clearer picture of such catalysts, the current work utilizes several in situ spectroscopic and microscopic techniques to examine the copper oxide phases that form when AgCu is exposed to epoxidation conditions. It is found that several forms of oxidic Cu coexist simultaneously on the active catalyst s surface, namely, CuO, Cu2O, and some previously unreported form of oxidized Cu, referred to here as CuxOy. Online product analysis, performed during the in situ spectroscopic measurements, shows that increased epoxide selectivity is correlated with the presence of mixed copper oxidation states and the presence of the CuxOy species. These results support previous theoretical predictions that oxidic copper overlayers on silver play an active role in epoxidation. These results furthermore emphasize the need for in situ spectromicroscopic methods to understand the complexity of alloy catalyst