Hydrogen isotope separation using graphene-based membranes in liquid water

Hydrogen isotope separation has been effectively achieved using gaseous H2/D2 filtered through graphene/Nafion composite membranes. Nevertheless, deuteron nearly does not exist in the form of gaseous D2 in nature but in liquid water. Thus, it is a more feasible way to separate and enrich deuterium from water. Herein we have successfully transferred monolayer graphene to a rigid and porous polymer substrate PITEM (polyimide tracked film), which could avoid the swelling problem of the Nafion substrate, as well as keep the integrity of graphene. Meanwhile, defects in large area of CVD graphene could be successfully repaired by interfacial polymerization resulting in high separation factor. Moreover, a new model was proposed for the proton transport mechanism through monolayer graphene based on the kinetic isotope effect (KIE). In this model, graphene plays the significant role in the H/D separation process by completely breaking the O-H/O-D bond, which can maximize the KIE leading to prompted H/D separation performance. This work suggests a promising application of using monolayer graphene in industry and proposes a pronounced understanding of proton transport in grapheneComment: 10 pages, 4 figures (6pages, 6figures for SI

Lifestyle factors, serum parameters, metabolic comorbidities, and the risk of kidney stones: a Mendelian randomization study

Background and objectiveThe early identification of modifiable risk factors is important for preventing kidney stones but determining causal associations can be difficult with epidemiological data. We aimed to genetically assess the causality between modifiable factors (lifestyle factors, serum parameters, and metabolic comorbidities) and the risk of kidney stones. Additionally, we aimed to explore the causal impact of education on kidney stones and its potential mediating pathways.MethodsWe conducted a two-sample Mendelian randomization (MR) study to explore the causal association between 44 modifiable risk factors and kidney stones. The FinnGen dataset initially explored the causal relationship of risk factors with kidney stones and the UK Biobank dataset was used as the validation set. Then, a meta-analysis was conducted by combining discovery and validation datasets. We used two-step MR to assess potential mediators and their mediation proportions between education and kidney stones.ResultsThe combined results indicated that previous exposures may increase the risk of kidney stones, including sedentary behavior, urinary sodium, the urinary sodium/potassium ratio, the urinary sodium/creatinine ratio, serum calcium, 25-hydroxyvitamin D (25OHD), the estimated creatinine-based glomerular filtration rate (eGFRcrea), GFR estimated by serum cystatin C (eGFRcys), body mass index (BMI), waist circumference, type 2 diabetes mellitus (T2DM), fasting insulin, glycated hemoglobin, and hypertension. Coffee intake, plasma caffeine levels, educational attainment, and the urinary potassium/creatinine ratio may decrease the risk of kidney stones. Ranked by mediation proportion, the effect of education on the risk of kidney stones was mediated by five modifiable risk factors, including sedentary behavior (mediation proportion, 25.7%), smoking initiation (10.2%), BMI (8.2%), T2DM (5.8%), and waist circumference (3.2%).ConclusionThis study provides MR evidence supporting causal associations of many modifiable risk factors with kidney stones. Sedentary lifestyles, obesity, smoking, and T2DM are mediating factors in the causal relationship between educational attainment and kidney stones. Our results suggest more attention should be paid to these modifiable factors to prevent kidney stones

Modulation of (-)-Epicatechin Metabolism by Coadministration with Other Polyphenols in Caco-2 Cell Model

ABSTRACT Widely consumed beverages such as red wine, tea, and cocoaderived products are a great source of flavanols. Epidemiologic and interventional studies suggest that cocoa flavanols such as (-)-epicatechin may reduce the risk of cardiovascular diseases. The interaction of (-)-epicatechin with food components including other polyphenols could modify its absorption, metabolism, and finally its bioactivity. In the present study we investigate (-)-epicatechin absorption and metabolism when coexposed with other polyphenols in the intestinal absorptive Caco-2 cell model. Depending on the type of polyphenols coadministered, the total amount of 39-O-methyl-epicatechin and 39-O-sulfate-epicatechin conjugates found both in apical and basal compartments ranged from 19 to 801 nM and from 6 to 432 nM, respectively. The coincubation of (-)-epicatechin with flavanols, chlorogenic acid, and umbelliferone resulted in similar amounts of 39-O-methyl-epicatechin effluxed into the apical compartment relative to control. Coincubation with isorhamnetin, kaempferol, diosmetin, nevadensin, chrysin, equol, genistein, and hesperitin promoted the transport of 39-Omethyl-epicatechin toward the basolateral side and decreased the apical efflux. Quercetin and luteolin considerably inhibited the appearance of this (-)-epicatechin conjugate both in the apical and basolateral compartments. In conclusion, we could demonstrate that the efflux of (-)-epicatechin conjugates to the apical or basal compartments of Caco-2 cells is modulated by certain classes of polyphenols and their amount. Ingesting (-)-epicatechin with specific polyphenols could be a strategy to increase the bioavailability of (-)-epicatechin and to modulate its metabolic profile

Employing Dietary Comparators to Perform Risk Assessments for Anti-Androgens Without Using Animal Data

This study investigated the use of androgen receptor (AR) reporter gene assay data in a non-animal exposure-led risk assessment in which in vitro anti-androgenic activity and exposure data were put into context using a naturally occurring comparator substance with a history of dietary consumption. First, several dietary components were screened to identify which selectively interfered with AR signaling in vitro, using the AR CALUX® test. The IC50 values from these dose-response data together with measured or predicted human exposure levels were used to calculate exposure:activity ratios (EARs) for the dietary components and a number of other well-known anti-androgenic substances. Both diindolylmethane (DIM) and resveratrol are specifically-acting dietary anti-androgens. The EARs for several anti-androgens were therefore expressed relative to the EAR of DIM, and how this ‘dietary comparator ratio’ (DCR) approach may be used to make safety decisions was assessed using an exposure-led case study for an anti-androgenic botanical ingredient. This highlights a pragmatic approach which allows novel chemical exposures to be put into context against dietary exposures to natural anti-androgenic substances. The DCR approach may have utility for other modes of action where appropriate comparators can be identified

A coherent image source method for flat waveguides with locally reacting boundaries

An image source method is presented for coherently evaluating the sound field from a point source in flat waveguides with two infinite and parallel locally reacting boundaries, where one is sound absorbing and the other is reflective. The method starts from formulating sound reflections into integrals by plane wave expansion, and the inherent intractability in solving these integrals in such spaces is avoided by introducing a physically plausible assumption that wave fronts remain the same before and after the reflection on a near-rigid boundary. By comparisons to the classical wave theory and the existing coherent ray-based methods, it is shown that the proposed method is considerably accurate to predict the sound propagation in flat waveguides with a sound absorbing ceiling and a reflective floor over a broad frequency range and for various source/receiver geometries, even if at large distances from the source compared to the waveguide height while the existing methods are shown to be erroneous.Published versio

Estimation of Forest Aboveground Biomass in Karst Areas Using Multi-Source Remote Sensing Data and the K-DBN Algorithm

Accurate estimation of forest biomass is the basis for monitoring forest productivity and carbon sink function, which is of great significance for the formulation of forest carbon neutralization strategy and forest quality improvement measures. Taking Guizhou, a typical karst region in China, as the research area, this study used Landsat 8 OLI, Sentinel-1A, and China national forest resources continuous inventory data (NFCI) in 2015 to build a deep belief network (DBN) model for aboveground biomass (AGB) estimation. Based on the introduction of forest canopy density (FCD), we improved the DBN model to design the K-DBN model with the highest estimation accuracy is selected for AGB inversion and spatial mapping. The results showed that: (1) The determination coefficients R2 of DBN is 0.602, which are 0.208, 0.101 higher than that of linear regression (LR) and random forest (RF) model. (2) The K-DBN algorithm was designed based on FCD to optimize the DBN model, which can alleviate the common problems of low-value overestimation and high-value underestimation in AGB estimation to a certain extent to improve the estimation accuracy. The maximum R2 of the model reached 0.848, and we mapped the forest AGB using the K-DBN model in the study area in 2015. The conclusion of this study: Based on multi-source optical and radar data, the retrieval accuracy of forest AGB can be improved by considering the FCD, and the deep learning algorithm K-DBN is excellent in forest AGB remote sensing estimation. These research results provide a new method and data support for the spatio-temporal dynamic remote sensing monitoring of forest AGB in karst areas

Nano-CL-20/HMX Cocrystal Explosive for Significantly Reduced Mechanical Sensitivity

Spray drying method was used to prepare cocrystals of hexanitrohexaazaisowurtzitane (CL-20) and cyclotetramethylene tetranitramine (HMX). Raw materials and cocrystals were characterized using scanning electron microscopy, X-ray diffraction, differential scanning calorimetry, Raman spectroscopy, and Fourier transform infrared spectroscopy. Impact and friction sensitivity of cocrystals were tested and analyzed. Results show that, after preparation by spray drying method, microparticles were spherical in shape and 0.5–5 µm in size. Particles formed aggregates of numerous tiny plate-like cocrystals, whereas CL-20/HMX cocrystals had thicknesses of below 100 nm. Cocrystals were formed by C–H⋯O bonding between –NO2 (CL-20) and –CH2– (HMX). Nanococrystal explosives exhibited drop height of 47.3 cm, and friction demonstrated explosion probability of 64%. Compared with raw HMX, cocrystals displayed significantly reduced mechanical sensitivity

Estimation of Forest Aboveground Biomass in Karst Areas Using Multi-Source Remote Sensing Data and the K-DBN Algorithm

Accurate estimation of forest biomass is the basis for monitoring forest productivity and carbon sink function, which is of great significance for the formulation of forest carbon neutralization strategy and forest quality improvement measures. Taking Guizhou, a typical karst region in China, as the research area, this study used Landsat 8 OLI, Sentinel-1A, and China national forest resources continuous inventory data (NFCI) in 2015 to build a deep belief network (DBN) model for aboveground biomass (AGB) estimation. Based on the introduction of forest canopy density (FCD), we improved the DBN model to design the K-DBN model with the highest estimation accuracy is selected for AGB inversion and spatial mapping. The results showed that: (1) The determination coefficients R2 of DBN is 0.602, which are 0.208, 0.101 higher than that of linear regression (LR) and random forest (RF) model. (2) The K-DBN algorithm was designed based on FCD to optimize the DBN model, which can alleviate the common problems of low-value overestimation and high-value underestimation in AGB estimation to a certain extent to improve the estimation accuracy. The maximum R2 of the model reached 0.848, and we mapped the forest AGB using the K-DBN model in the study area in 2015. The conclusion of this study: Based on multi-source optical and radar data, the retrieval accuracy of forest AGB can be improved by considering the FCD, and the deep learning algorithm K-DBN is excellent in forest AGB remote sensing estimation. These research results provide a new method and data support for the spatio-temporal dynamic remote sensing monitoring of forest AGB in karst areas