On‐the‐Spot Immobilization of Quantum Dots,Graphene Oxide,and Proteins via Hydrophobins

Class I hydrophobin Vmh2, a peculiar surface active and versatile fungal protein, is known to self‐assemble into chemically stable amphiphilic films, to be able to change wettability of surfaces, and to strongly adsorb other proteins. Herein, a fast, highly homogeneous and efficient glass functionalization by spontaneous self‐assembling of Vmh2 at liquid–solid interfaces is achieved (in 2 min). The Vmh2‐coated glass slides are proven to immobilize not only proteins but also nanomaterials such as graphene oxide (GO) and quantum dots (QDs). As models, bovine serum albumin labeled with Alexa 555 fluorophore, anti‐immunoglobulin G antibodies, and cadmium telluride QDs are patterned in a microarray fashion in order to demonstrate functionality, reproducibility, and versatility of the proposed substrate. Additionally, a GO layer is effectively and homogeneously self‐assembled onto the studied functionalized surface. This approach offers a quick and simple alternative to immobilize nanomaterials and proteins, which is appealing for new bioanalytical and nanobioenabled applications.     

Effect of Initial Grain Size on Friction Stir Weldability for Rolled and Cast CoCrFeMnNi High-Entropy Alloys

Metals and Materials International - This study investigated the influence of the initial grain size on the plastic deformation and tunnel defects that occurred from friction stir welding of...     

Al2O3 Colloidal Nanocrystals with Strong UV Emission

A facile sol–gel procedure has been developed for the synthesis of colloidal alumina nanocrystals. For the first time, optical characterization procedures were employed to study the quantum confinement effects in optical properties of the prepared Al₂O₃ sol. Accordingly, the hyperbolic band model was used to determine the optical band gap of colloidal alumina nanocrystals. X‐Ray diffraction pattern was used to study the crystallographic phase of the dried gel. Morphological characterization was performed using scanning electron microscopy (SEM). Inductively Coupled Plasma (ICP) emission spectroscopy was used to determination purity of the Al₂O₃ powder. High‐resolution TEM showed that the diameter of colloidal nanocrystals is about 10 nm. Photoluminescence spectroscopy demonstrated that quantum yields for colloidal nanocrystals are 68% with 300 nm excitation wavelength. The experimental observations confirm that highly stable alumina sol with strong UV emission was synthesized. The mentioned optical properties have not been reported before.     

A Distribution‐freeCUSUMChart for Monitoring Variability of AutocorrelatedProcesses

We present a distribution‐free tabular cumulative sum chart for monitoring the variability of an autocorrelated process. A quantity known as the asymptotic variance parameter is employed as a measure of the variability, and a distribution‐free tabular cumulative sum chart is applied to variance estimates calculated from batches of nonoverlapping samples. The proposed chart is applicable to a stationary process with a general marginal distribution and a general autocorrelation structure. It also determines control limits analytically without trial‐and‐error simulations. The performance of the proposed chart is tested on stationary processes with both normal and nonnormal marginals with various autocorrelation structures. Copyright © 2014 John Wiley & Sons, Ltd.     

High-efficiency Silicon Solar Cells: A Review

Over the past few decades, crystalline silicon solar cells have been extensively studied due to their high efficiency, high reliability, and low cost. In addition, these types of cells lead the industry and account for more than half of the market. For the foreseeable future, Si will still be a critical material for photovoltaic devices in the solar cell industry. In this paper, we discuss key issues, cell concepts, and the status of recent high-efficiency crystalline silicon solar cells.     

Visualizing for the Non‐Visual: Enabling the Visually Impaired to Use Visualization

The majority of visualizations on the web are still stored as raster images, making them inaccessible to visually impaired users. We propose a deep‐neural‐network‐based approach that automatically recognizes key elements in a visualization, including a visualization type, graphical elements, labels, legends, and most importantly, the original data conveyed in the visualization. We leverage such extracted information to provide visually impaired people with the reading of the extracted information. Based on interviews with visually impaired users, we built a Google Chrome extension designed to work with screen reader software to automatically decode charts on a webpage using our pipeline. We compared the performance of the back‐end algorithm with existing methods and evaluated the utility using qualitative feedback from visually impaired users.