Graphdiyne as a high-capacity lithium ion battery anode material

Using the first-principles calculations, we explored the feasibility of using graphdiyne, a 2D layer of sp and sp2 hybrid carbon networks, as lithium ion battery anodes. We found that the composite of the Li-intercalated multilayer ??-graphdiyne was C6Li7.31 and that the calculated voltage was suitable for the anode. The practical specific/volumetric capacities can reach up to 2719 mAh g-1/2032 mAh cm-3, much greater than the values of ???372 mAh g-1/???818 mAh cm -3, ???1117 mAh g-1/???1589 mAh cm-3, and ???744 mAh g-1 for graphite, graphynes, and ??-graphdiyne, respectively. Our calculations suggest that multilayer ??-graphdiyne can serve as a promising high-capacity lithium ion battery anode.open3

Evaluation of Penalized and Nonpenalized Methods for Disease Prediction with Large-Scale Genetic Data

Owing to recent improvement of genotyping technology, large-scale genetic data can be utilized to identify disease susceptibility loci and this successful finding has substantially improved our understanding of complex diseases. However, in spite of these successes, most of the genetic effects for many complex diseases were found to be very small, which have been a big hurdle to build disease prediction model. Recently, many statistical methods based on penalized regressions have been proposed to tackle the so-called "large P and small N" problem. Penalized regressions including least absolute selection and shrinkage operator (LASSO) and ridge regression limit the space of parameters, and this constraint enables the estimation of effects for very large number of SNPs. Various extensions have been suggested, and, in this report, we compare their accuracy by applying them to several complex diseases. Our results show that penalized regressions are usually robust and provide better accuracy than the existing methods for at least diseases under consideration

Surface innovation to enhance anti-droplet and hydrophobic behavior of breathable compressed-polyurethane masks

With the emergence of the coronavirus disease (COVID-19), it is essential that face masks demonstrating significant anti-droplet and hydrophobic characteristics are developed and distributed. In this study, a commercial compressed-polyurethane (C-PU) mask was modified by applying a hydrophobic and anti-droplet coating using a silica sol, which was formed by the hydrolysis of tetraethoxysilane (TEOS) under alkaline conditions and hydrolyzed hexadecyltrimethoxysilane (HDTMS) to achieve hydrophobization. The modified mask (C-PU/Si/HDTMS) demonstrated good water repellency resulting in high water contact angle (132 degrees) and low sliding angle (17 degrees). Unmodified and modified masks were characterized using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy, scanning electron microscopy (SEM), energy-dispersive Xray spectroscopy (EDS), and X-ray photoelectron spectroscopy (XPS). A drainage test confirmed the strong interaction between the mask surface and coating. Moreover, the coating had negligible effect on the average pore size of the C-PU mask, which retained its high breathability after modification. The application of this coating is a facile approach to impart anti-droplet, hydrophobic, and self-cleaning characteristics to C-PU masks. (C) 2020 Elsevier B.V. All rights reserved

NiFeOx decorated Ge-hematite/perovskite for an efficient water splitting system

To boost the photoelectrochemical water oxidation performance of hematite photoanodes, high temperature annealing has been widely applied to enhance crystallinity, to improve the interface between the hematite-substrate interface, and to introduce tin-dopants from the substrate. However, when using additional dopants, the interaction between the unintentional tin and intentional dopant is poorly understood. Here, using germanium, we investigate how tin diffusion affects overall photoelectrochemical performance in germanium:tin co-doped systems. After revealing that germanium is a better dopant than tin, we develop a facile germanium-doping method which suppresses tin diffusion from the fluorine doped tin oxide substrate, significantly improving hematite performance. The NiFeOx@Ge-PH photoanode shows a photocurrent density of 4.6mAcm(-2) at 1.23 V-RHE with a low turn-on voltage. After combining with a perovskite solar cell, our tandem system achieves 4.8% solar-to-hydrogen conversion efficiency (3.9mAcm(-2) in NiFeOx@Ge-PH/perovskite solar water splitting system). Our work provides important insights on a promising diagnostic tool for future co-doping system design. Germanium (Ge) has potential as a dopant suitable for the hematite-based photoelectrochemical water splitting system. Here, the authors report the fabrication of Ge doped porous hematite and demonstrate an efficient tandem system of Ge doped porous hematite and the perovskite solar cell

Stabilization of LiBH4 superionic phase by halide doping and H disordering

We performed first-principles density-functional theory calculations to investigate the structural properties and the effect of halide ion doping to the superionic-conducting, high-temperature phase and the effect of halide doping on the phase of LiBH4. It is computationally demonstrated that the superionic phase is stabilized owing to the halide doping with the large ions which fill the interlayer space of the superionic phase. The H-disordered phase is observed in the structure and is found to contribute to the stabilization of the superionic phase.close0

Evaluation of Penalized and Nonpenalized Methods for Disease Prediction with Large-Scale Genetic Data

Owing to recent improvement of genotyping technology, large-scale genetic data can be utilized to identify disease susceptibility loci and this successful finding has substantially improved our understanding of complex diseases. However, in spite of these successes, most of the genetic effects for many complex diseases were found to be very small, which have been a big hurdle to build disease prediction model. Recently, many statistical methods based on penalized regressions have been proposed to tackle the so-called “large P and small N” problem. Penalized regressions including least absolute selection and shrinkage operator (LASSO) and ridge regression limit the space of parameters, and this constraint enables the estimation of effects for very large number of SNPs. Various extensions have been suggested, and, in this report, we compare their accuracy by applying them to several complex diseases. Our results show that penalized regressions are usually robust and provide better accuracy than the existing methods for at least diseases under consideration

Boron Nitride Nanotube (BNNT) Membranes for Energy and Environmental Applications

Owing to their extraordinary thermal, mechanical, optical, and electrical properties, boron nitride nanotubes (BNNTs) have been attracting considerable attention in various scientific fields, making it more promising as a nanomaterial compared to other nanotubes. Recent studies reported that BNNTs exhibit better properties than carbon nanotubes, which have been extensively investigated for most environment-energy applications. Irrespective of its chirality, BNNT is a constant wide-bandgap insulator, exhibiting thermal oxidation resistance, piezoelectric properties, high hydrogen adsorption, ultraviolet luminescence, cytocompatibility, and stability. These unique properties of BNNT render it an exceptional material for separation applications, e.g., membranes. Recent studies reported that water filtration, gas separation, sensing, and battery separator membranes can considerably benefit from these properties. That is, flux, rejection, anti-fouling, sensing, structural, thermal, electrical, and optical properties of membranes can be enhanced by the contribution of BNNTs. Thus far, a majority of studies have focused on molecular simulation. Hence, the requirement of an extensive review has emerged. In this perspective article, advanced properties of BNNTs are analyzed, followed by a discussion on the advantages of these properties for membrane science with an overview of the current literature. We hope to provide insights into BNNT materials and accelerate research for environment-energy applications

Efficacy of Electrically-Polarized 3D Printed Graphene-blended Spacers on the Flux Enhancement and Scaling Resistance of Water Filtration Membranes

In this research, an electrically polarized graphene-polylactic acid (E-GRP) spacer is introduced for the first time by a novel fabrication method, which consists of 3D printing followed by electrical polarization under a high voltage electric field (1.5 kV/cm). The fabricated E-GRP was tested in an osmotic-driven process (forward osmosis system) to evaluate its performance in terms of water flux, reverse solute flux, and ion attraction compared to a 3D printed nonpolarized graphene-polylactic acid (GRP) spacer and a polylactic acid (PLA) spacer. The use of the developed E-GRP as a draw spacer showed >50% water flux enhancement (32.4 +/- 2 Liter/m(2)/h (LMH)) compared to the system employing the GRP (20.5 +/- 2.3 LMH) or PLA (20.8 +/- 2.1 LMH) spacer. This increased water flux was attributed to the increased local osmotic pressure across the membrane surface due to the ions adsorbed by the polarized (E-GRP) spacer. As a feed spacer, the E-GRP also retarded the gypsum scaling on the membrane compared to the GRP spacer due to the dispersion effect of electrostatic forces between the gypsum aggregation and negatively charged surfaces. The electric polarization of the E-GRP spacer was shown to be maintained for >100 h by observing its salt adsorption properties (in a 3 M NaCl solution)

Electrically Polarized Graphene-Blended Spacers for Organic Fouling Reduction in Forward Osmosis

In membrane processes, a spacer is known to play a key role in the mitigation of membrane fouling. In this study, the effect of electric polarization on a graphene-blended polymer spacer (e.g., poly(lactic acid), PLA) for organic fouling on membrane surfaces was investigated. A pristine PLA spacer (P-S), a graphene-blended spacer (G-S), and an electrically polarized graphene-blended spacer (EG-S) were successfully fabricated by 3D printing. Organic fouling tests were conducted by the 5-h filtration of CaCl2 and a sodium alginate solution through commercially available membranes, which were placed together with the fabricated spacers. Membranes utilizing P-S, G-S, and EG-S were characterized in terms of the fouling amount on the membrane surface and fouling roughness. Electrostatic forces of EG-S provided 70% less and 90% smoother fouling on the membrane surface, leading to an only 14% less water flux reduction after 5 h of fouling. The importance of nanomaterial blending and polarization was successfully demonstrated herein