A facile method was developed to fabricate porous tube-like ZnS by sulfurizing rod-like ZIF-L with thioacetamide (TAA) at different durations and the formation mechanism of the porous tube-like ZnS was discussed in detail. The series of sulfide products (ZS-X) were characterized by powder X-ray diffraction (PXRD), Fourier transform infrared spectroscopy (FTIR), solid-state nuclear magnetic resonance spectroscopy (SSNMR), transmission electron microscopy (TEM), UV–visible diffuse-reflectance spectroscopy (UV–vis DRS). The photocatalytic performances of ZS-X toward Cr(VI) reduction and organic pollutant degradation were explored. It was discovered that ZS-3 (porous tube-like ZnS) exhibited excellent activities under UV light and displayed good reusability and stability after several experimental cycles. In addition, Cr(VI) reduction and organic pollutant degradation were investigated under different pH values and existence of different foreign ions. The photocatalytic activities of ZS-3 were tested toward the matrix of Cr(VI) and reactive red X–3B. The mechanism was proposed and verified by both electrochemical analysis and electron spin resonance (ESR) measurement.
Pharmaceuticals and personal care products (PPCPs) have been widely detected in ecosystems. However, effective water purification technologies for PPCPs degradation are lacking. In this work, an active activated carbon fiber supported titanate nanotubes (TNTs@ACF) composite was synthesized via one-step hydrothermal process, which was applied for adsorption and photocatalytic degradation of PPCPs under simulated solar light. Characterizations indicated that the successful grafting of TNTs onto ACF was achieved and surface modification occurred. Diclofenac (DCF, a model PPCPs) was rapidly adsorbed onto TNTs@ACF, and subsequently photodegraded (98.8 %) under solar light within 2 h. TNTs@ACF also performed well over a wide range of pH, and was resistant to humic acid. The good adsorption and photocatalytic activity of TNTs@ACF was attributed to the well-defined hybrid structure, enabling corporative adsorption of DCF by TNTs and ACF, and extending the light absorbance to visible region. Furthermore, the description of degradation pathway and evaluation of ecotoxicity for DCF and its intermediates/byproduct were proposed based on experimental analysis, density functional theory (DFT) calculation and quantitative structure–activity relationship (QSAR) analysis, respectively, indicating the photocatalytic degradation of DCF can offer the step-by-step de-toxicity. Our study is expected to offer new strategy as “pre-accumulation and in-situ destruction” for environmental application.
Scale formation is an important challenge in water and wastewater treatment systems. However, due to the complex nature of membrane surfaces, the effects of specific membrane surface characteristics on scale formation are poorly understood. In this study, the independent effect of surface hydrophobicity on gypsum (CaSO4·2H2O) scale formation via surface-induced nucleation and bulk homogeneous nucleation was investigated using quartz crystal microbalance with dissipation (QCM-D) on self-assembled monolayers (SAMs) terminated with −OH, −CH3, and −CF3 functional groups. Results show that higher surface hydrophobicity enhances both surface-induced nucleation of gypsum and attachment of gypsum crystals formed from homogeneous nucleation in the bulk solution. The enhanced surface-induced nucleation is attributed to the lower nucleation energy barrier on a hydrophobic surface, while the increased gypsum crystal attachment results from the favorable hydrophobic interactions between gypsum and more hydrophobic surfaces. Contrary to previous findings, the role of Ca2+ adsorption in surface-induced nucleation was found to be relatively small and similar on the different SAMs. Therefore, increasing material hydrophilicity is a potential approach to reduce gypsum scaling.
Recent studies have suggested that the cognitive process of the human brain is realized as probabilistic inference and can be further modeled by probabilistic graphical models like Markov random fields. Nevertheless, it remains unclear how probabilistic inference can be implemented by a network of spiking neurons in the brain. Previous studies have tried to relate the inference equation of binary Markov random fields to the dynamic equation of spiking neural networks through belief propagation algorithm and reparameterization, but they are valid only for Markov random fields with limited network structure. In this paper, we propose a spiking neural network model that can implement inference of arbitrary binary Markov random fields. Specifically, we design a spiking recurrent neural network and prove that its neuronal dynamics are mathematically equivalent to the inference process of Markov random fields by adopting mean-field theory. Furthermore, our mean-field approach unifies previous works. Theoretical analysis and experimental results, together with the application to image denoising, demonstrate that our proposed spiking neural network can get comparable results to that of mean-field inference.
We compare three countries where public policy has explicitly sought to align incentives of employers and educational institutions around closing the gap between skill formation and labor market demand. In large, heterogeneous countries such as the United States, Russia and China, collaborative arrangements such apprenticeships and other forms of public-private partnerships can be constructed at the subnational level by building on direct, face-to-face ties across educational, business, government, and civic sectors. Drawing on existing literature as well as fieldwork studying a number of specific cases in the three countries, the paper develops a typology of such arrangements and proposes an explanation for the observed variation. It emphasizes the importance of two sets of factors: those that induce cooperation on the part of firms and schools, and those that influence the character of such partnerships.
Identifying and quantifying the major sources of atmospheric particulate matter (PM) is essential for the development of pollution mitigation strategies to protect public health. However, urban PM is affected by local primary emissions, transport, and secondary formation; therefore, advanced methods are needed to elucidate the complex sources and transport patterns. Here, an improved source apportionment method was developed by incorporating the receptor model, Lagrangian simulation, and emissions inventories to quantify PM2.5 sources for an industrial city in China. PM2.5 data including ions, metals, organic carbon, and elemental carbon were obtained by analyzing 1 year of sampling results at urban and rural sites. This method identified coal combustion (30.64%), fugitive dust (13.25%), and vehicles (12.51%) as major primary sources. Secondary sources, including sulfate, nitrate, and secondary organic aerosols also contributed strongly (25.28%–30.76% in total) over urban and rural areas. Hebei Province was the major regional source contributor (43.05%–57.51%) except for fugitive dust, on which Inner Mongolia had a greater impact (43.51%). The megacities of Beijing and Tianjin exerted strong regional impacts on the secondary nitrate and secondary organic aerosols factors, contributing 11.32% and 15.65%, respectively. Pollution events were driven largely by secondary inorganic aerosols, highlighting the importance of reducing precursor emissions at the regional scale, particularly in the Beijing–Tianjin–Hebei region. Overall, our results demonstrate that this novel method offers good flexibility and efficiency for quantifying PM2.5 sources and regional contributions, and that it can be extended to other cities.
Biological aging is the gradual, progressive decline in system integrity that occurs with advancing chronological age, causing morbidity and disability. Measurements of the pace of aging are needed as surrogate endpoints in trials of therapies designed to prevent disease by slowing biological aging. We report a blood-DNA-methylation measure that is sensitive to variation in pace of biological aging among individuals born the same year. We first modeled change-over-time in 18 biomarkers tracking organ-system integrity across 12 years of follow-up in n = 954 members of the Dunedin Study born in 1972-1973. Rates of change in each biomarker over ages 26-38 years were composited to form a measure of aging-related decline, termed Pace-of-Aging. Elastic-net regression was used to develop a DNA-methylation predictor of Pace-of-Aging, called DunedinPoAm for Dunedin(P)ace(o)f(A)ging(m)ethylation. Validation analysis in cohort studies and the CALERIE trial provide proof-of-principle for DunedinPoAm as a single-time-point measure of a person's pace of biological aging. People's bodies age at different rates. Age-related biological changes that increase the risk of disease and disability progress rapidly in some people. In others, these processes occur at a slower pace, allowing those individuals to live longer, healthier lives. This observation has led scientists to try to develop therapies that slow aging. The hope is that such treatments could prevent or delay diseases like heart disease or dementia, for which older age is the leading risk factor. Studies in animals have identified treatments that extend the creatures' lives and slow age-related disease. But testing these treatments in humans is challenging. Our lives are much longer than the worms, flies or mice used in the experiments. Scientists would have to follow human study participants for decades to detect delays in disease onset or an extension of their lives. An alternative approach is to try to develop a test that measures the pace of aging, or essentially "a speedometer for aging". This would allow scientists to more quickly determine if treatments slow the aging process. Now, Belsky et al. show a blood test designed to measure the pace of aging predicts which people are at increased risk of poor health, chronic disease and an earlier death. First, data about chemical changes to an individual's DNA, called DNA methylation, were analyzed from white blood cell samples collected from 954 people in a long-term health study known as "The Dunedin Study". Using the data, Belsky et al. then developed an algorithm - named "DunedinPoAm" - that identified people with an accelerated or slowed pace of aging based on a single blood test. Next, they used the algorithm on samples from participants in three other long-term studies. This verified that those people the algorithm identified as aging faster had a greater risk of poor health, developing chronic diseases or dying earlier. Similarly, those identified as aging more slowly performed better on tests of balance, strength, walking speed and mental ability, and they also looked younger to trained raters. Additionally, Belsky et al. used the test on participants in a randomized trial testing whether restricting calories had potential to extend healthy lifespan. The results suggested that the calorie restriction could counter the effects of an accelerated pace of aging. The test developed by Belsky et al. may provide an alternate way of measuring whether age-slowing treatments work. This would allow faster testing of treatments that can extend the healthy lifespan of humans. The test may also help identify individuals with accelerated aging. This might help public health officials test whether policies or programs can help people lead longer, healthier lives. eng
How does the capacity removal policy affect China’s economy? To quantify the policy outcomes and costs, a four-sector model with vertical market structures is built. The calibrated model shows that, to achieve the policy goal, 10% of equipment operation in the high energy-consuming sectors must be shut down. This policy leads to an improved energy structure in which total energy consumption drops by 4.75% at the cost of a contraction in economic growth, where the total output declines by 12.31%. The numerical experiments find that the optimal policy is to limit the production scale in both the iron/steel industry and the fossil energy industry, closing 9% and 7% of the production, respectively, since doing so minimizes output loss and improves the energy structure. This paper quantifies the impact of the current capacity removal policy and provides policy alternatives to reach the same policy target with a lower output loss.
Abstract Soluble and miscible states are two important thermodynamic states in academic research and practical applications but their quantitative distinctions are still fuzzy. In this study, for the first time, the mathematical formulations of the quantitative criteria for distinguishing the thermodynamic soluble and miscible states are analytically developed by means of the Flory–Huggins solution theory and solubility parameter. The quantitative bottom and upper solubility limits for a total of 13 binary and ternary mixtures are calculated at different conditions. Moreover, the composition, temperature and pore radius are specifically studied to evaluate their effects on the soluble and miscible states. On the basis of the work from this study, the insoluble, soluble but immiscible, and miscible states are definitively quantified and clearly distinguished for the first time.
McArdle S, Endo S, Aspuru-Guzik A, Benjamin SC, Yuan X. Quantum computational chemistry. Reviews of Modern Physics. 2020;92(1):015003.
In this letter, we report a quasi-vertical GaN Schottky barrier diode (SBD) fabricated on a hetero-epitaxial layer on silicon with low dislocation density and high carrier mobility. The reduction of dislocation is realized by inserting a thin layer with high density of Ga vacancies to promote the dislocation bending. The dislocation density is $1.6\times 10^8$ cm?2 with a GaN drift layer thickness of $4.5 μ \textm$ . The fabricated prototype GaN SBD delivers a high on/off current ratio of $10^10$ , a high forward current density of 1.6 kA/cm2@3 V, a low specific on-resistance of 1.1 $\textmØmega \cdot \text cm^2$ , and a low ideality factor of 1.23.
