Reconstruction of Low Dimensional Electronic States by Altering the Chemical Arrangement at the SrTiO3 Surface

Developing reliable methods for modulating the electronic structure of the 2D electron gas (2DEG) in SrTiO₃ is crucial for utilizing its full potential and inducing novel properties. Herein, it is shown that relatively simple surface preparation reconstructs the 2DEG at the SrTiO₃ (STO) surface, leading to a Lifshitz-like transition. Combining experimental methods, such as angle-resolved photoemission spectroscopy (ARPES) and X-ray photoemission spectroscopy with ab initio calculations, that the modulation of the surface band structures can be effectively achieved via transforming the chemical composition at the atomic scale is found. In addition, ARPES experiments demonstrate that vacuum ultraviolet light can be efficiently employed to alter the band renormalization of the 2DEG system and control the electron-phonon interaction . This study provides a robust and straightforward route to stabilize and tune the low-dimensional electronic structure via the chemical degeneracy of the STO surface.     

Electrospinning of ultrafine non-hydrolyzed silk sericin/PEO fibers on PLA: A bilayer scaffold fabrication

We report the feasibility of electrospinning of protein-polymer multilayered scaffolds with selected materials such as non-hydrolyzed silk sericin (SS), polyethylene oxide (PEO), and polylactic acid (PLA), with tuned fiber size and properties for each layer. We present a new innovative way for the electrospinning (ES) of non-hydrolyzed SS mixed with PEO yielding fibers with an average diameter ranging between 120 and 150 nm. Different SS:PEO ratios have been electrospun to study the effect of the concentration of SS protein on the fibers size and shape, as well and their electrospinnability. Electrospun SS:PEO fibers display weak to no mechanical resistance (non-measurable) and their deposition onto a sturdier scaffold is necessary to allow their use in biomedical and/or pharmaceutical fields. Therefore, bilayer scaffolds have been fabricated consisting of a PLA support and SS:PEO fibers obtained from the optimized SS:PEO ratio (1.2:4). They are composed of a sturdy hydrophobic layer of PLA fibers and a layer of sticky hydrophilic SS:PEO fibers. The scaffolds have been characterized extensively by Fourier transforms infra-red (FTIR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and their resistance to mechanical stress. Finally, hydrophobicity of both layers has been determined by measuring the contact angle of water droplets on the scaffolds, further proving the bilayer nature of the scaffolds.     

Subpixel Deblurring of Anti-Aliased Raster Clip-Art

Artist generated clip-art images typically consist of a small number of distinct, uniformly colored regions with clear boundaries. Legacy artist created images are often stored in low-resolution (100x100px or less) anti-aliased raster form. Compared to anti-aliasing free rasterization, anti-aliasing blurs inter-region boundaries and obscures the artist's intended region topology and color palette; at the same time, it better preserves subpixel details. Recovering the underlying artist-intended images from their low-resolution anti-aliased rasterizations can facilitate resolution independent rendering, lossless vectorization, and other image processing applications. Unfortunately, while human observers can mentally deblur these low-resolution images and reconstruct region topology, color and subpixel details, existing algorithms applicable to this task fail to produce outputs consistent with human expectations when presented with such images. We recover these viewer perceived blur-free images at subpixel resolution, producing outputs where each input pixel is replaced by four corresponding (sub)pixels. Performing this task requires computing the size of the output image color palette, generating the palette itself, and associating each pixel in the output with one of the colors in the palette. We obtain these desired output components by leveraging a combination of perceptual and domain priors, and real world data. We use readily available data to train a network that predicts, for each anti-aliased image, a low-blur approximation of the blur-free double-resolution outputs we seek. The images obtained at this stage are perceptually closer to the desired outputs but typically still have hundreds of redundant differently colored regions with fuzzy boundaries. We convert these low-blur intermediate images into blur-free outputs consistent with viewer expectations using a discrete partitioning procedure guided by the characteristic properties of clip-art images, observations about the antialiasing process, and human perception of anti-aliased clip-art. This step dramatically reduces the size of the output color palettes, and the region counts bringing them in line with viewer expectations and enabling the image processing applications we target. We demonstrate the utility of our method by using our outputs for a number of image processing tasks, and validate it via extensive comparisons to prior art. In our comparative study, participants preferred our deblurred outputs over those produced by the best-performing alternative by a ratio of 75 to 8.5.