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Dissolution of electrocatalysts during long-term and dynamic operation is a challenging problem in energy conversion and storage devices such as fuel cells and electrolyzers. To develop stable electrocatalysts, we adopt the design concept of segregation engineering, which uses solute segregation prone to electrochemical dissolution at internal defects, i.e., grain boundaries and dislocations. We showcase the feasibility of this approach by stabilizing a model Pt catalyst with an addition of more noble Au (approximately 5 atomic percent). We characterized the defects' nanoscale structure and chemistry, and monitored the electrochemical dissolution of Pt and PtAu alloys by online inductively coupled plasma mass spectrometry. Once segregated to defects, Au atoms can stabilize and hence passivate the most vulnerable sites against electrochemical dissolution and improve the stability and longevity of the Pt electrocatalysts by more than an order of magnitude. This opens pathways to use solute segregation to defects for the development of more stable nanoscale electrocatalysts, a concept applicable for a wide range of catalytic systems.

Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. We trained a convolutional neural network (CNN) using twenty thousand synthesized images of grain interiors, GBs, or triple junctions. Such a trained CNN automatically detects the locations of GBs from APT data. Those GBs are then subjected to compositional mapping and analysis, including revealing their in-plane chemical decoration patterns. We applied this approach to experimentally obtained APT data sets pertaining to three case studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we extracted GB-specific segregation features as a function of misorientation and coincidence site lattice character. Secondly, we revealed interfacial excesses and in-plane chemical features that could not have been found by standard compositional analyses. Lastly, we tracked the temporal evolution of chemical decoration from early-stage solute GB segregation in the dilute limit to interfacial phase separation, characterized by the evolution of complex composition patterns. This machine-learning-based approach provides quantitative, unbiased, and automated access to GB chemical analyses, serving as an enabling tool for new discoveries related to interface thermodynamics, kinetics, and the associated chemistry-structure-property relations.

We present the results of a time-series analysis of 130 echelle spectra of epsilon Ori (B0 Ia), acquired over seven observing seasons between 1998 and 2006 at Ritter Observatory. The equivalent widths of Halpha (net) and He I 5876 were measured and radial velocities were obtained from the central absorption of He I 5876. Temporal variance spectra (TVS) revealed significant wind variability in both Halpha and He I 5876. The He I TVS have a double-peaked profile consistent with radial velocity oscillations. A periodicity search was carried out on the equivalent width and radial velocity data, as well as on wavelength-binned spectra. This analysis has revealed several periods in the variability with time scales of 2-7 d. Many of these periods exhibit sinusoidal modulation in the associated phase diagrams. Several of these periods were present in both Halpha and He I, indicating a possible connection between the wind and the photosphere. Due to the harmonic nature of these periods, stellar pulsations may be the origin of some of the observed variability. Periods on the order of the rotational period were also detected in the He I line in the 98-99 season and in both lines during the 04-05 season. These periods may indicate rotational modulation due to structure in the wind., Comment: 18 pages, 12 figures, 7 tables; changed format to more closely resemble published version, added two refs., deleted two refs

This paper presents the results of a computational study related to the path-geodesic correspondence in causal sets. For intervals in flat spacetimes, and in selected curved spacetimes, we present evidence that the longest maximal chains (the longest paths) in the corresponding causal set intervals statistically approach the geodesic for that interval in the appropriate continuum limit., Comment: To the celebration of the 60th birthday of Rafael D. Sorkin

In this paper we give a pedagogical review of the recent observational results in cosmology from the study of type Ia supernovae and anisotropies in the cosmic microwave background. By providing consistent constrainst on the cosmological parameters, these results paint a concrete picture of our present-day universe. We present this new picture and show how it can be used to answer some of the basic questions that cosmologists have been asking for several decades. This paper is most appropriate for students of general relativity and/or relativistic cosmology., Comment: pedagogical review, 33 pgs., 6 figs. Version 2 in RevTex, some typos corrected - new ones added (probably)