Sichuan University

About: Sichuan University is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Population & Catalysis. The organization has 107623 authors who have published 102844 publications receiving 1612131 citations. The organization is also known as: Sìchuān Dàxué.

SiO2/graphene composite for highly selective adsorption of Pb(II) ion.

Abstract: SiO2/graphene composite was prepared through a simple two-step reaction, including the preparation of SiO2/graphene oxide and the reduction of graphene oxide (GO). The composite was characterized by UV–Vis spectroscopy, Fourier transform infrared spectroscopy, scanning electron microscope, and X-ray photoelectron spectroscopy, and what is more, the adsorption behavior of as-synthesized SiO2/graphene composite was investigated. It was interestingly found that the composite shows high efficiency and high selectivity toward Pb(II) ion. The maximum adsorption capacity of SiO2/graphene composite for Pb(II) ion was found to be 113.6 mg g−1, which was much higher than that of bare SiO2 nanoparticles. The results indicated that SiO2/graphene composite with high adsorption efficiency and fast adsorption equilibrium can be used as a practical adsorbent for Pb(II) ion.

DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems

Abstract: The rapid convergence of legacy industrial infrastructures with intelligent networking and computing technologies (e.g., 5G, software-defined networking, and artificial intelligence), have dramatically increased the attack surface of industrial cyber–physical systems (CPSs). However, withstanding cyber threats to such large-scale, complex, and heterogeneous industrial CPSs has been extremely challenging, due to the insufficiency of high-quality attack examples. In this article, we propose a novel federated deep learning scheme, named DeepFed, to detect cyber threats against industrial CPSs. Specifically, we first design a new deep learning-based intrusion detection model for industrial CPSs, by making use of a convolutional neural network and a gated recurrent unit. Second, we develop a federated learning framework, allowing multiple industrial CPSs to collectively build a comprehensive intrusion detection model in a privacy-preserving way. Further, a Paillier cryptosystem-based secure communication protocol is crafted to preserve the security and privacy of model parameters through the training process. Extensive experiments on a real industrial CPS dataset demonstrate the high effectiveness of the proposed DeepFed scheme in detecting various types of cyber threats to industrial CPSs and the superiorities over state-of-the-art schemes.

Highly enhanced visible light photocatalysis and in situ FT-IR studies on Bi metal@defective BiOCl hierarchical microspheres

Abstract: 3D plasmonic Bi metal@defective BiOCl hierarchical microspheres were constructed by a one-step solvothermal method. The effects of solvent thermal temperature on the microstructure and the photocatalytic performance were investigated. The photocatalyst prepared at 200 °C (Bi/BiOCl-200) showed most efficient visible photocatalytic activity for NOx removal. The enhanced activity can be ascribed to synergistic effects of oxygen vacancies and Bi metals. The oxygen vacancies in BiOCl induce the formation of an intermediate level to allow electrons transition from the valence band to intermediate level and then to the conduction band as revealed by density functional theory (DFT). The surface plasmon resonance (SPR) effect of elemental Bi enables the improvement of the visible light absorption efficiency and the promotion of the charge carrier separation as evidenced from the charge difference distribution between Bi atoms and Bi-O layers in BiOCl. The NO adsorption and reaction processes on Bi/BiOCl-200 were dynamically monitored by in situ infrared spectroscopy (FTIR). The mechanism of Bi metals and oxygen vacancies co-mediated photocatalytic performance on Bi/BiOCl was proposed based on the results of intermediate products analysis, radicals trapping and DFT calculation. The present work could provide new insights into the mechanistic understanding of the non-noble metal Bi-based plasmonic photocatalysts and offer a new technique to reveal the mechanism of gas-phase photocatalytic reaction.

Health status and risk for depression among the elderly: a meta-analysis of published literature

Abstract: Objective: the goal of this study was to determine the relationship between health status, including self-rated health status and chronic disease, and risk for depression among the elderly. Method: MEDLINE, EMBASE and The Cochrane Library Database were used to identify potential studies. The studies were classified into cross-sectional and longitudinal subsets. For each study, the numbers of the total participants, cases (for cross-sectional study) or incident cases (for longitudinal study) of depression in each health status group were extracted and entered into Review Manager 4.2. The quantitative meta-analysis of cross-sectional studies and that of longitudinal studies were performed, respectively. For prevalence and incidence rates of depression, odds risk and relative risk (RR) were calculated, respectively. Results: the quantitative meta-analysis showed that, compared with the elderly without chronic disease, those with chronic disease had higher risk for depression (RR: 1.53, 95% confidence intervals (CI): 1.20–1.97). Compared with the elderly with good self-rated health, those with poor self-rated health had higher risk for depression (RR: 2.40, 95% CI: 1.94–2.97). Conclusions: despite the methodological limitations of this meta-analysis, both poor self-rated health status and the presence of chronic disease are risk factors for depression among the elderly. In the elderly, poor self-reported health status appears to be more strongly associated with depression than the presence of chronic disease.

Robust superhydrophobicity: mechanisms and strategies

Abstract: Superhydrophobic surfaces hold great prospects for extremely diverse applications owing to their water repellence property The essential feature of superhydrophobicity is micro-/nano-scopic roughness to reserve a large portion of air under a liquid drop However, the vulnerability of the delicate surface textures significantly impedes the practical applications of superhydrophobic surfaces Robust superhydrophobicity is a must to meet the rigorous industrial requirements and standards for commercial products In recent years, major advancements have been made in elucidating the mechanisms of wetting transitions, design strategies and fabrication techniques of superhydrophobicity This review will first introduce the mechanisms of wetting transitions, including the thermodynamic stability of the Cassie state and its breakdown conditions Then we highlight the development, current status and future prospects of robust superhydrophobicity, including characterization, design strategies and fabrication techniques In particular, design strategies, which are classified into passive resistance and active regeneration for the first time, are proposed and discussed extensively