Cobalt oxides and (oxy)hydroxides have been widely studied as electrocatalysts for the oxygen evolution reaction (OER). For related Ni-based materials, the addition of Fe dramatically enhances OER activity. The role of Fe in Co-based materials is not well-documented. We show that the intrinsic OER activity of Co(1-x)Fe(x)(OOH) is ∼100-fold higher for x ≈ 0.6-0.7 than for x = 0 on a per-metal turnover frequency basis. Fe-free CoOOH absorbs Fe from electrolyte impurities if the electrolyte is not rigorously purified. Fe incorporation and increased activity correlate with an anodic shift in the nominally Co(2+/3+) redox wave, indicating strong electronic interactions between the two elements and likely substitutional doping of Fe for Co. In situ electrical measurements show that Co(1-x)Fe(x)(OOH) is conductive under OER conditions (∼0.7-4 mS cm(-1) at ∼300 mV overpotential), but that FeOOH is an insulator with measurable conductivity (2.2 × 10(-2) mS cm(-1)) only at high overpotentials >400 mV. The apparent OER activity of FeOOH is thus limited by low conductivity. Microbalance measurements show that films with x ≥ 0.54 (i.e., Fe-rich) dissolve in 1 M KOH electrolyte under OER conditions. For x < 0.54, the films appear chemically stable, but the OER activity decreases by 16-62% over 2 h, likely due to conversion into denser, oxide-like phases. We thus hypothesize that Fe is the most-active site in the catalyst, while CoOOH primarily provides a conductive, high-surface area, chemically stabilizing host. These results are important as Fe-containing Co- and Ni-(oxy)hydroxides are the fastest OER catalysts known.

Generative Adversarial Networks (GANs) are a machine learning approach capable of generating novel example outputs across a space of provided training examples. Procedural Content Generation (PCG) of levels for video games could benefit from such models, especially for games where there is a pre-existing corpus of levels to emulate. This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus. The approach successfully generates a variety of levels similar to one in the original corpus, but is further improved by application of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Specifically, various fitness functions are used to discover levels within the latent space of the GAN that maximize desired properties. Simple static properties are optimized, such as a given distribution of tile types. Additionally, the champion A* agent from the 2009 Mario AI competition is used to assess whether a level is playable, and how many jumping actions are required to beat it. These fitness functions allow for the discovery of levels that exist within the space of examples designed by experts, and also guide the search towards levels that fulfill one or more specified objectives.

There exists a gap between visualization design guidelines and their application in visualization tools. While empirical studies can provide design guidance, we lack a formal framework for representing design knowledge, integrating results across studies, and applying this knowledge in automated design tools that promote effective encodings and facilitate visual exploration. We propose modeling visualization design knowledge as a collection of constraints, in conjunction with a method to learn weights for soft constraints from experimental data. Using constraints, we can take theoretical design knowledge and express it in a concrete, extensible, and testable form: the resulting models can recommend visualization designs and can easily be augmented with additional constraints or updated weights. We implement our approach in Draco, a constraint-based system based on Answer Set Programming (ASP). We demonstrate how to construct increasingly sophisticated automated visualization design systems, including systems based on weights learned directly from the results of graphical perception experiments.

Anion exchange membrane (AEM) electrolysis is a promising technology to produce hydrogen through the splitting of pure water. In contrast to proton-exchange-membrane (PEM) technology, which requires precious-metal oxide anodes, AEM systems allow for the use of earth-abundant anode catalysts. Here we report a study of first-row transition-metal (oxy)­hydroxide/oxide catalyst powders for application in AEM devices and compare physical properties and performance to benchmark IrO x catalysts as well as typical catalysts for alkaline electrolyzers. We show that the catalysts’ oxygen-evolution activity measured in alkaline electrolyte using a typical three-electrode cell is a poor indicator of performance in the AEM system. The best oxygen-evolution-reaction (OER) catalysts in alkaline electrolyte, NiFeO x H y oxyhydroxides, are the worst in AEM electrolysis devices where a solid alkaline electrolyte is used along with a pure water feed. NiCoO x -based catalysts show the best performance in AEM electrolysis. The performance can be further improved by adding Fe species to the particle surface. We attribute the differences in performance in part to differences in the electrical conductivity of the catalyst phases, which are also measured and reported.

Ni-borate materials are oxygen evolution catalysts that operate at near-neutral pH and have been found previously to improve due to structural changes induced via anodic conditioning. We find that this increased activity after conditioning at 0.856 V vs. SCE, as measured on a turn-over frequency basis (TOF) at 400 mV overpotential (TOF = 0.38 s(-1)), accompanies significant Fe incorporation (14%). Films conditioned in Fe-free electrolyte exhibit ∼10 times lower activity (TOF = 0.03 s(-1)). By co-depositing Fe-Ni we demonstrate high activity without conditioning (TOF = 0.24 s(-1)) which improves further with shortened (∼30 min) conditioning (TOF = 1.4 s(-1)).

BackgroundRule-based modeling (RBM) is a powerful and increasingly popular approach to modeling cell signaling networks. However, novel visual tools are needed in order to make RBM accessible to a broad range of users, to make specification of models less error prone, and to improve workflows.ResultsWe introduce RuleBender, a novel visualization system for the integrated visualization, modeling and simulation of rule-based intracellular biochemistry. We present the user requirements, visual paradigms, algorithms and design decisions behind RuleBender, with emphasis on visual global/local model exploration and integrated execution of simulations. The support of RBM creation, debugging, and interactive visualization expedites the RBM learning process and reduces model construction time; while built-in model simulation and results with multiple linked views streamline the execution and analysis of newly created models and generated networks.ConclusionRuleBender has been adopted as both an educational and a research tool and is available as a free open source tool at http://www.rulebender.org. A development cycle that includes close interaction with expert users allows RuleBender to better serve the needs of the systems biology community.

Abstract-Procedural content generators for games produce artifacts from a latent design space. This space is often only implicitly defined, an emergent result of the procedures used in the generator. In this paper, we outline an approach to content generation that centers on explicit description of the design space, using domainindependent procedures to produce artifacts from the described space. By concisely capturing a design space as an answer set program, we can rapidly define and expressively sculpt new generators for a variety of game content domains. We walk through the reimplementation of a reference evolutionary content generator in a tutorial example, and review existing applications of answer set programming to generative-content design problems in and outside of a game context. Index Terms-Answer set programming, constraint programming, game design, logic programming, procedural content generation.

There exists a gap between visualization design guidelines and their application in visualization tools. While empirical studies can provide design guidance, we lack a formal framework for representing design knowledge, integrating results across studies, and applying this knowledge in automated design tools that promote effective encodings and facilitate visual exploration. We propose modeling visualization design knowledge as a collection of constraints, in conjunction with a method to learn weights for soft constraints from experimental data. Using constraints, we can take theoretical design knowledge and express it in a concrete, extensible, and testable form: the resulting models can recommend visualization designs and can easily be augmented with additional constraints or updated weights. We implement our approach in Draco, a constraint-based system based on Answer Set Programming (ASP). We demonstrate how to construct increasingly sophisticated automated visualization design systems, including systems based on weights learned directly from the results of graphical perception experiments.