Next-Generation Graphene-Based Membranes for Gas Separation and Water Purifications

Advanced membrane systems are regarded as an important portion of controllable separation processes, such as gas separation and water purification. The ideal materials should have good permeability for selected particle sizes, high stiffness to withstand high pressures applied, large surface area and micro- or nanopore structures for excellent selectivity. Recently, graphene with oxygen-containing functional groups and graphene oxide (GO) nanosheets, obtained via chemical oxidation of graphite, achieved tremendous properties that include excellent mechanical strength, large relative surface area, unique honeycomb lattice two-dimensional structure as well as narrow pore distribution, offering platform to be used as advanced, ultrathin membrane for a wide variety of purification process with high efficiency. In this review chapter, the potential application of such advanced materials for gas separation and water purification process is discussed. The fabrication and modification process and innovation of such advanced two-dimensional functional structure for purification and separation process are introduced. This review chapter will offer opportunity to understand details involved in gas and/or water molecular transport through thin, laminar graphene oxide and derived structures, as well as up to now progress in the field

Weighted Sparse Partial Least Squares for Joint Sample and Feature Selection

Sparse Partial Least Squares (sPLS) is a common dimensionality reduction technique for data fusion, which projects data samples from two views by seeking linear combinations with a small number of variables with the maximum variance. However, sPLS extracts the combinations between two data sets with all data samples so that it cannot detect latent subsets of samples. To extend the application of sPLS by identifying a specific subset of samples and remove outliers, we propose an $\ell_\infty/\ell_0$-norm constrained weighted sparse PLS ($\ell_\infty/\ell_0$-wsPLS) method for joint sample and feature selection, where the $\ell_\infty/\ell_0$-norm constrains are used to select a subset of samples. We prove that the $\ell_\infty/\ell_0$-norm constrains have the Kurdyka-\L{ojasiewicz}~property so that a globally convergent algorithm is developed to solve it. Moreover, multi-view data with a same set of samples can be available in various real problems. To this end, we extend the $\ell_\infty/\ell_0$-wsPLS model and propose two multi-view wsPLS models for multi-view data fusion. We develop an efficient iterative algorithm for each multi-view wsPLS model and show its convergence property. As well as numerical and biomedical data experiments demonstrate the efficiency of the proposed methods

Research on Construction Project Management Strategy Based on EPC General Contracting

At present, the rapid development of urban economy in our country and the rise of the construction industry make the contract management model more specialized and standardized. Among them, EPC is a common type of project general contracting mode, which can be divided into design, procurement, construction and other modules according to the requirements and actual conditions of project construction. When carrying out project management of construction projects, the use of EPC general contracting mode can achieve good management results. Therefore, from the perspective of EPC general contracting, this paper discusses the relevant countermeasures of construction project management, aiming to realize the unified and standardizing management of all aspects of engineering construction and improving the level of project management

Inhibition of Glucose-6-Phosphate Dehydrogenase Could Enhance 1,4-Benzoquinone-Induced Oxidative Damage in K562 Cells

Benzene is a chemical contaminant widespread in industrial and living environments. The oxidative metabolites of benzene induce toxicity involving oxidative damage. Protecting cells and cell membranes from oxidative damage, glucose-6-phosphate dehydrogenase (G6PD) maintains the reduced state of glutathione (GSH). This study aims to investigate whether the downregulation of G6PD in K562 cell line can influence the oxidative toxicity induced by 1,4-benzoquinone (BQ). G6PD was inhibited in K562 cell line transfected with the specific siRNA of G6PD gene. An empty vector was transfected in the control group. Results revealed that G6PD was significantly upregulated in the control cells and in the cells with inhibited G6PD after they were exposed to BQ. The NADPH/NADP and GSH/GSSG ratio were significantly lower in the cells with inhibited G6PD than in the control cells at the same BQ concentration. The relative reactive oxygen species (ROS) level and DNA oxidative damage were significantly increased in the cell line with inhibited G6PD. The apoptotic rate and G2 phase arrest were also significantly higher in the cells with inhibited G6PD and exposed to BQ than in the control cells. Our results suggested that G6PD inhibition could reduce GSH activity and alleviate oxidative damage. G6PD deficiency is also a possible susceptible risk factor of benzene exposure

Reconfiguring Gaussian Curvature of Hydrogel Sheets with Photoswitchable Host–Guest Interactions

Photoinduced shape morphing has implications in fields ranging from soft robotics to biomedical devices. Despite considerable effort in this area, it remains a challenge to design materials that can be both rapidly deployed and reconfigured into multiple different three-dimensional forms, particularly in aqueous environments. In this work, we present a simple method to program and rewrite spatial variations in swelling and, therefore, Gaussian curvature in thin sheets of hydrogels using photoswitchable supramolecular complexation of azobenzene pendent groups with dissolved α-cyclodextrin. We show that the extent of swelling can be programmed via the proportion of azobenzene isomers, with a 60% decrease in areal swelling from the all trans to the predominantly cis state near room temperature. The use of thin gel sheets provides fast response times in the range of a few tens of seconds, while the shape change is persistent in the absence of light thanks to the slow rate of thermal cis–trans isomerization. Finally, we demonstrate that a single gel sheet can be programmed with a first swelling pattern via spatially defined illumination with ultraviolet light, then erased with white light, and finally redeployed with a different swelling pattern

A Dimensional Structure based Knowledge Distillation Method for Cross-Modal Learning

Due to limitations in data quality, some essential visual tasks are difficult to perform independently. Introducing previously unavailable information to transfer informative dark knowledge has been a common way to solve such hard tasks. However, research on why transferred knowledge works has not been extensively explored. To address this issue, in this paper, we discover the correlation between feature discriminability and dimensional structure (DS) by analyzing and observing features extracted from simple and hard tasks. On this basis, we express DS using deep channel-wise correlation and intermediate spatial distribution, and propose a novel cross-modal knowledge distillation (CMKD) method for better supervised cross-modal learning (CML) performance. The proposed method enforces output features to be channel-wise independent and intermediate ones to be uniformly distributed, thereby learning semantically irrelevant features from the hard task to boost its accuracy. This is especially useful in specific applications where the performance gap between dual modalities is relatively large. Furthermore, we collect a real-world CML dataset to promote community development. The dataset contains more than 10,000 paired optical and radar images and is continuously being updated. Experimental results on real-world and benchmark datasets validate the effectiveness of the proposed method