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  1. Abstract

    Data‐intensive discovery of water‐splitting catalysts can accelerate the development of sustainable energy technologies, such as the photocatalytic and/or electrocatalytic production of renewable hydrogen fuel. Through computational screening, 13 materials were recently predicted as potential water‐splitting photocatalysts: Cu3NbS4, CuYS2, SrCu2O2, CuGaO2, Na3BiO4,Sr2PbO4, LaCuOS, LaCuOSe, Na2TeO4, La4O4Se3, Cu2WS4, BaCu2O2, and CuAlO2. Herein, these materials are synthesized, their bandgaps and band alignments are experimentally determined, and their photoelectrocatalytic hydrogen evolution properties are assessed. Using cyclic voltammetry and chopped illumination experiments, 9 of the 13 materials are experimentally found to have bandgaps and band alignments that straddle the potentials required for the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), as computationally predicted. During photocatalytic testing, 12 of the materials yield a measurable photocurrent. However, only three are found to be active for the HER, with Cu3NbS4, CuYS2, and Cu2WS4producing H2in amounts comparable to bare TiO2; a benchmark photocatalyst. This study provides experimental validation of computational bandgap and band alignment predictions while also successfully identifying active photocatalysts.

     
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  2. Abstract

    The Materials Genome Initiative (MGI) advanced a new paradigm for materials discovery and design, namely that the pace of new materials deployment could be accelerated through complementary efforts in theory, computation, and experiment. Along with numerous successes, new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI. In May 2017, the National Science Foundation sponsored the workshop “Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation, Experiment, and Theory: Opening New Frontiers” to review accomplishments that emerged from investments in science and infrastructure under the MGI, identify scientific opportunities in this new environment, examine how to effectively utilize new materials innovation infrastructure, and discuss challenges in achieving accelerated materials research through the seamless integration of experiment, computation, and theory. This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts.

     
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  3. Oxides of p-block metals (e.g., indium oxide) and semimetals (e.g., antimony oxide) are of broad practical interest as transparent conductors and light absorbers for solar photoconversion due to the tunability of their electronic conductivity and optical absorption. Comparatively, these oxides have found limited applications in solar-to-hydrogen photocatalysis primarily due to their high electronegativity, which impedes electron transfer for converting protons into molecular hydrogen. We have shown recently that inserting s-block metal cations into p-block oxides is effective at lowering electronegativities while affording further control of band gaps. Here, we explain the origins of this dual tunability by demonstrating the mediator role of s-block metal cations in modulating orbital hybridization while not contributing to frontier electronic states. From this result, we carry out a comprehensive computational study of 109 ternary oxides of s- and p-block metal elements as candidate photocatalysts for solar hydrogen generation. We downselect the most desirable materials using band gaps and band edges obtained from Hubbard-corrected density-functional theory with Hubbard parameters computed entirely from first principles, evaluate the stability of these oxides in aqueous conditions, and characterize experimentally four of the remaining materials, synthesized with high phase uniformity, to assess the accuracy of computational predictions. We thus propose seven oxide semiconductors, including CsIn3O5, Sr2In2O5, and KSbO2 which, to the extent of our literature review, have not been previously considered as water-splitting photocatalysts. 
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    Tuning the work functions of materials is of practical interest for maximizing the performance of microelectronic and (photo)electrochemical devices, as the efficiency of these systems depends on the ability to control electronic levels at surfaces and across interfaces. Perovskites are promising compounds to achieve such control. In this work, we examine the work functions of more than 1000 perovskite oxide surfaces (ABO 3 ) using data-driven (machine-learning) analysis and identify the factors that determine their magnitude. While the work functions of the BO 2 -terminated surfaces are sensitive to the energy of the hybridized oxygen p bands, the work functions of the AO-terminated surfaces exhibit a much less trivial dependence with respect to the filling of the d bands of the B-site atom and of its electronic affinity. This study shows the utility of interpretable data-driven models in analyzing the work functions of cubic perovskites from a limited number of electronic-structure descriptors. 
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