Recent Advances in Conjugated Polymers for Visible-Light-Driven Water Splitting

With the ambition of solving the challenges of the shortage of fossil fuels and their associated environmental pollution, visible-light-driven splitting of water into hydrogen and oxygen using semiconductor photocatalysts has emerged as a promising technology to provide environmentally friendly energy vectors. Among the current library of developed photocatalysts, organic conjugated polymers present unique advantages of sufficient light-absorption efficiency, excellent stability, tunable electronic properties, and economic applicability. As a class of rising photocatalysts, organic conjugated polymers offer high flexibility in tuning the framework of the backbone and porosity to fulfill the requirements for photocatalytic applications. In the past decade, significant progress has been made in visible-light-driven water splitting employing organic conjugated polymers. The recent development of the structural design principles of organic conjugated polymers (including linear, crosslinked, and supramolecular self-assembled polymers) toward efficient photocatalytic hydrogen evolution, oxygen evolution, and overall water splitting is described, thus providing a comprehensive reference for the field. Finally, current challenges and perspectives are also discussed.     

Novel Bi-Doped Amorphous SnOx Nanoshells for Efficient Electrochemical CO2 Reduction into Formate at Low Overpotentials

Engineering novel Sn-based bimetallic materials could provide intriguing catalytic properties to boost the electrochemical CO₂ reduction. Herein, the first synthesis of homogeneous Sn_1−xBi_x alloy nanoparticles (x up to 0.20) with native Bi-doped amorphous SnO_x shells for efficient CO₂ reduction is reported. The Bi-SnO_x nanoshells boost the production of formate with high Faradaic efficiencies (>90%) over a wide potential window (−0.67 to −0.92 V vs RHE) with low overpotentials, outperforming current tin oxide catalysts. The state-of-the-art Bi-SnO_x nanoshells derived from Sn_0.80Bi_0.20 alloy nanoparticles exhibit a great partial current density of 74.6 mA cm⁻² and high Faradaic efficiency of 95.8%. The detailed electrocatalytic analyses and corresponding density functional theory calculations simultaneously reveal that the incorporation of Bi atoms into Sn species facilitates formate production by suppressing the formation of H₂ and CO.     

高精度薄壁钢套类零件制造工艺

薄壁钢套类零件是机械制造中常碰到的一类难加工零件,由于其不同的功能用途和典型结构特点,其制造有一定难度,在实际生产过程中,经常出现加工制造后的零件尺寸精度、形状精度、形位精度达不到使用及设计要求。本文较系统地阐述了薄壁钢套类零件的典型制造工艺方法以及在制造过程中的变形分析和应对措施。     

我国钻井装备现状及其相关配套技术

结合目前国外钻机现状以及国际石油钻井市场对钻机的配套要求,分析指出我国目前陆地钻机存在的不足与差距,对我国钻机及其配套技术的发展提出建议。     

A practical reliability assessment approach and its application for pile-stabilized slopes using FORM and support vector machine

Bulletin of Engineering Geology and the Environment - Many uncertainties exist in pile-stabilized slopes which make their design substantially complicated. In this paper, a first-order reliability...     

500 kV 开关弹簧机构支架螺栓断裂的原因分析及处理

HPL500B2型弹簧机构支架(BA)螺栓断裂可能导致开关分合闸失败,对其原因进行分析,发现BA螺栓由于预紧固力矩过大,接近其扭断力矩,使得作用在BA螺栓上的力偏高,BA螺栓安全系数仅为12%,安全系数较低。列出BA螺栓更换步骤及注意事项,并详细介绍分闸、合闸弹簧调整步骤,为类似事件的处理提供参考。     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.