Highly sensitive and selective visual detection of Cr(VI) ions based on etching of silver-coated gold nanorods

We report a visual detection of Cr(VI) ions using silver-coated gold nanorods (AuNR@Ag) as sensing probes. Au NRs were prepared by a seed-mediated growth process and AuNR@Ag nanostructures were synthesized by growing Ag nanoshells on Au NRs. Successful coating of Ag nanoshells on the surface of Au NRs was demonstrated with TEM, EDS, and UV–vis spectrometer. By increasing the overall amount of the deposited Ag on Au NRs, the localized surface plasmon resonance (LSPR) band was significantly blue-shifted, which allowed tuning across the visible spectrum. The sensing mechanism relies on the redox reaction between Cr(VI) ions and Ag nanoshells on Au NRs. As the concentration of Cr(VI) ions increased, more significant red-shift of the longitudinal peak and intensity decrease of the transverse peak could be observed using UV–vis spectrometer. Several parameters such as concentration of CTAB, thickness of the Ag nanoshells and pH of the sample were carefully optimized to determine Cr(VI) ions. Under optimized condition, this method showed a low detection limit of 0.4 μM and high selectivity towards Cr(VI) over other metal ions, and the detection range of Cr(VI) was tuned by controlling thickness of the Ag nanoshells. From multiple evaluations in real sample, it is clear that this method is a promising Cr(VI) ion colorimetric sensor with rapid, sensitive, and selective sensing ability.This research was supported under the framework of Nano Material Technology Development Program (NRF-2015M3A7B6027970) and Basic Science Research Program (NRF-2018R1D1A1B07051249) by National Research Foundation, South Korea. Also, this work was supported by the Center of Integrated Smart Sensors funded by the Ministry of Science, ICT and Future Planning, South Korea, as Global Frontier Project (CISS-012M3A6A6054186

The Development of Therapeutic Antibodies That Neutralize Homologous and Heterologous Genotypes of Dengue Virus Type 1

Antibody protection against flaviviruses is associated with the development of neutralizing antibodies against the viral envelope (E) protein. Prior studies with West Nile virus (WNV) identified therapeutic mouse and human monoclonal antibodies (MAbs) that recognized epitopes on domain III (DIII) of the E protein. To identify an analogous panel of neutralizing antibodies against DENV type-1 (DENV-1), we immunized mice with a genotype 2 strain of DENV-1 virus and generated 79 new MAbs, 16 of which strongly inhibited infection by the homologous virus and localized to DIII. Surprisingly, only two MAbs, DENV1-E105 and DENV1-E106, retained strong binding and neutralizing activity against all five DENV-1 genotypes. In an immunocompromised mouse model of infection, DENV1-E105 and DENV1-E106 exhibited therapeutic activity even when administered as a single dose four days after inoculation with a heterologous genotype 4 strain of DENV-1. Using epitope mapping and X-ray crystallographic analyses, we localized the neutralizing determinants for the strongly inhibitory MAbs to distinct regions on DIII. Interestingly, sequence variation in DIII alone failed to explain disparities in neutralizing potential of MAbs among different genotypes. Overall, our experiments define a complex structural epitope on DIII of DENV-1 that can be recognized by protective antibodies with therapeutic potential

Synthesis of spherical and cubic magnetic iron oxide nanocrystals at low temperature in air

Synthesis of magnetite nanocrystals typically requires harsh reaction conditions, including high reaction pressures and/or temperatures, to obtain morphology-controlled nanocrystals like cubic magnetite nanocrystals. We report the synthesis of cubic magnetite nanocrystals with a size of 9 nm at reaction temperatures less than 100 C in air. The synthesized magnetite nanocubes exhibited uniform size and highly crystalline nature. In addition, we synthesized size-controlled spherical magnetite nanocrystals in the size range of 2.5 nm to 9 nm by modifying the reaction conditions. 2018 Elsevier Inc. All rights reserved

Synthesis of Au–Cu Alloy Nanoparticles as Peroxidase Mimetics for H2O2 and Glucose Colorimetric Detection

The detection of hydrogen peroxide (H2O2) is essential in many research fields, including medical diagnosis, food safety, and environmental monitoring. In this context, Au-based bimetallic alloy nanomaterials have attracted increasing attention as an alternative to enzymes due to their superior catalytic activity. In this study, we report a coreduction synthesis of gold–copper (Au–Cu) alloy nanoparticles in aqueous phase. By controlling the amount of Au and Cu precursors, the Au/Cu molar ratio of the nanoparticles can be tuned from 1/0.1 to 1/2. The synthesized Au–Cu alloy nanoparticles show good peroxidase-like catalytic activity and high selectivity for the H2O2-mediated oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB, colorless) to TMB oxide (blue). The Au–Cu nanoparticles with an Au/Cu molar ratio of 1/2 exhibit high catalytic activity in the H2O2 colorimetric detection, with a limit of detection of 0.141 μM in the linear range of 1–10 μM and a correlation coefficient R2 = 0.991. Furthermore, the Au–Cu alloy nanoparticles can also efficiently detect glucose in the presence of glucose oxidase (GOx), and the detection limit is as low as 0.26 μM

Enhancing catalytic activity of TiO2 nanoparticles through acid treatment in Eosin-Y sensitized photohydrogen evolution reaction system

Light-driven water splitting has gained increasing attention as an eco-friendly method for hydrogen production. There is a pressing need to enhance the performance of catalysts for the commercial viability of this reaction. Many methods have been proposed to improve catalyst performance; however, an economical and straightforward approach remains a priority. This paper presents an uncomplicated technique called acid treatment, which augments the catalytic performance of nanoparticles. The method promotes a change in the catalytic reactivity by causing a deficit in electron density of Ti and O on the surface of TiO2 nanoparticles without altering their size, morphology, or crystal structure. In the Eosin Y sensitized photocatalytic hydrogen production system, nitric acid treated TiO2 (16.95 μmol/g) exhibited 1.5 times the hydrogen production compared to bare TiO2 (11.15 μmol/g)

Single unit cell thick samaria nanowires and nanoplates

We report on the synthesis of samaria nanowires and nanoplates with a thickness of 1.1 nm. The cross-section of the nanowires is a rectangular shape with dimensions of 1.1 nm x 2.2 nm, which corresponded to the size of two unit cells of samaria. Under optimized conditions, we were able to synthesize as much as 10 g of the nanowires.

Sea urchin shaped carbon nanostructured materials: carbon nanotubes immobilized on hollow carbon spheres

Novel sea urchin shaped nanostructured carbon spheres (carbon nano-urchins) were fabricated by the growth of carbon nanotubes on the surface of hollow carbon spheres. The carbon nano-urchins were successfully used as a catalyst support for methanol electrochemical oxidation. © The Royal Society of Chemistry 2006.close333

Predicting the Effect of Processing Parameters on Caliber-Rolled Mg Alloys through Machine Learning

The multi-pass caliber rolling (MPCR) of Mg alloy has attracted much attention due to its engineering and manufacturing advantages. The MPCR process induces a unique microhardness variation, which has only been predicted using a finite element analysis thus far. This study employed machine learning as an alternative method of microhardness prediction for the first time. For this purpose, two machine-learning approaches were evaluated: the artificial neural network (ANN) approach and that aided by generative adversarial networks (GANs). These approaches predicted microhardness variation in the most difficult case (i.e., after the final-pass MPCR deformation). The machine-learning approaches provided a good prediction for the center area of the cross-section, because the prediction was relatively easy due to the small deviation in microhardness. In contrast, the ANN failed to anticipate the shifted hardness variation in the side sections, leading to a low predictability. Such an issue was effectively improved by integrating the GAN with the ANN

Predicting the Effect of Processing Parameters on Caliber-Rolled Mg Alloys through Machine Learning

The multi-pass caliber rolling (MPCR) of Mg alloy has attracted much attention due to its engineering and manufacturing advantages. The MPCR process induces a unique microhardness variation, which has only been predicted using a finite element analysis thus far. This study employed machine learning as an alternative method of microhardness prediction for the first time. For this purpose, two machine-learning approaches were evaluated: the artificial neural network (ANN) approach and that aided by generative adversarial networks (GANs). These approaches predicted microhardness variation in the most difficult case (i.e., after the final-pass MPCR deformation). The machine-learning approaches provided a good prediction for the center area of the cross-section, because the prediction was relatively easy due to the small deviation in microhardness. In contrast, the ANN failed to anticipate the shifted hardness variation in the side sections, leading to a low predictability. Such an issue was effectively improved by integrating the GAN with the ANN

Theoretical Study on Morphology of Copper Sulfide via Density Functional Theory Calculation

Copper sulfide has been attracted attention as a well-known p-type semiconductor because of low cost of precursor and its vast potential applications including photothermal therapy, optoelectronics, catalysis, and battery. The control of morphology is also critical influence factor to change their physical, optical, and chemical properties. In this study, the crystal growth and morphological changes of copper sulfide were studied by density functional theory (DFT) calculations. In particular, since one-dimensional (1D) nanoparticles including nanofiber, nanowire, and nanorod and two-dimensional (2D) such as nanoplate, and nanosheet have been experimentally synthesized, the DFT calculations have focused on both types. Especially, the CuS morphology was calculated as a hexagonal plate, through the stacking energy calculation of each surface, and morphological change to 1D structure were estimated according to the interaction with each surface with the surfactant. In addition, morphological changes were also observed depending on surfactant type because the interaction with each surface was different depending on the type of surfactant