Cooperative Catalytic Effect of ZrO2 and a-Fe2O3 Nanoparticles on BiVO4 Photoanodes for Enhanced Photoelectrochemical Water Splitting

Photoelectrochemical water splitting with metal oxide semiconductors offers a cost-competitive alternative for the generation of solar fuels. Most of the materials studied so far suffer from poor charge-transfer kinetics at the semiconductor/liquid interface, making compulsory the use of catalytic layers to overcome the large overpotentials required for the water oxidation reaction. Herein, we report a very soft electrolytic synthesis deposition method, which allows remarkably enhanced water oxidation kinetics of BiVO4 photoanodes by the sequential addition of Zr and Fe precursors. Upon a heat treatment cycle, these precursors are converted into monoclinic ZrO2 and α-Fe2O3 nanoparticles, which mainly act as catalysts, leading to a five-fold increase of the water oxidation photocurrent of BiVO4. This method provides a versatile platform that is easy to apply to different semiconductor materials, fully reproducible, and facile to scale-up on large area conductive substrates with attractive implications for technological deploymentWe thank financial support from the University Jaume I through the project P1⋅1B2011-50. Serveis Centrals at UJI (SCIC) are also acknowledged

An effective suggestion method for keyword search of databases

This paper solves the problem of providing high-quality suggestions for userkeyword queries over databases. With the assumption that the returned suggestions areindependent, existing query suggestion methods over databases score candidate suggestions individually and return the top-k best of them. However, the top-k suggestions have high redundancy with respect to the topics. To provide informative suggestions, the returned k suggestions are expected to be diverse, i.e., maximizing the relevance to the user query and the diversity with respect to topics that the user might be interested in simultaneously. In this paper, an objective function considering both factors is defined for evaluating a suggestion set. We show that maximizing the objective function is a submodular function maximization problem subject to n matroid constraints, which is an NP-hard problem