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  1. Photocatalytic water splitting is a wireless method for solar-to-hydrogen conversion. To date, however, the efficiency of photocatalytic water splitting is still very low. Here, we have investigated the design, synthesis, and characterization of quadruple-band InGaN nanowire arrays, which consist of In 0.35 Ga 0.65 N, In 0.27 Ga 0.73 N, In 0.20 Ga 0.80 N, and GaN segments, with energy bandgaps of ∼2.1 eV, 2.4 eV, 2.6 eV, and 3.4 eV, respectively. Such multi-band InGaN nanowire arrays are integrated directly on a nonplanar wafer for enhanced light absorption. Moreover, a doping gradient is introduced along the lateral dimension of the nanowires, which forms a built-in electric field and promotes efficient charge carrier separation and extraction for water redox reactions. We have demonstrated that the quadruple-band InGaN nanowire photocatalyst can exhibit a solar-to-hydrogen efficiency of ∼5.2% with relatively stable operation. This work demonstrates a novel strategy using multi-band semiconductor nanostructures for artificial photosynthesis and solar fuel conversion with significantly improved performance.
  2. Abstract Highly-directional image artifacts such as ion mill curtaining, mechanical scratches, or image striping from beam instability degrade the interpretability of micrographs. These unwanted, aperiodic features extend the image along a primary direction and occupy a small wedge of information in Fourier space. Deleting this wedge of data replaces stripes, scratches, or curtaining, with more complex streaking and blurring artifacts—known within the tomography community as “missing wedge” artifacts. Here, we overcome this problem by recovering the missing region using total variation minimization, which leverages image sparsity-based reconstruction techniques—colloquially referred to as compressed sensing (CS)—to reliably restore images corrupted by stripe-like features. Our approach removes beam instability, ion mill curtaining, mechanical scratches, or any stripe features and remains robust at low signal-to-noise. The success of this approach is achieved by exploiting CS's inability to recover directional structures that are highly localized and missing in Fourier Space.