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| 1 | Influence of N on precipitation behavior,associated corrosion and mechanical properties of super austenitic stainless steel S32654显示文摘The influence of N on the precipitation behavior,associated corrosion,and mechanical properties of S32654 were investigated by microstructural,electrochemical,and mechanical analyses.Increasing the N content results in several alterations:(1) grain refinement,which promotes intergranular precipitation;(2) a linear increase in the driving force for Cr2 N and Mo activity,which accelerates the precipitation of intergranular Cr2 N and π phase,respectively;(3) a linear decrease in the driving force for σ phase and Cr activity,which suppresses the formation of intragranular σ phase.The total amount of precipitates first decreased and then increased with the N content increasing.Furthermore,the intergranular corrosion susceptibility depended substantially on the total amount of precipitates and also first exhibited a decreasing and then an increasing trend as the N content increased.In addition,aging precipitation caused a considerable decrement in the ultimate tensile strength(UTS) and a remarkable increment in the yield strength(YS).Both the UTS and YS always increased with N content increasing throughout the solution and aging process.Whereas the elongation was considerably sensitive to the aging treatment,it exhibited marginal variation with the N content increasing. | Shucai Zhang Huabing Li Zhouhua Jiang Zhixing Li Jingxi Wu Binbin Zhang Fei Duan Hao Feng Hongchun Zhu | 2020 | Journal of Materials Science & Technology2020，42，7： | 10 |
| 2 | Response of the sediment geochemistry of the Changjiang River(Yangtze River) to the impoundment of the Three Gorges Dam显示文摘Based on the measurement of major and trace elements in suspended sediments in the low reaches of the Changjiang River during throughout a whole hydrologic year, the origins, seasonal variations, and fluxes of multielements and the human impacts on multielements transport processes have been analyzed along with the influence of weathering in the Changjiang River basin. The results show that most element contents were high in both autumn and winter and low in summer, which was largely caused by the dilution of discharge. Weathering detritus in the Changjiang River basin is the main source of most elements in suspended sediments. However, riverine pollution could bring more loadings of Cd, Pb, As and Zn into river water than it did a few decades ago. The annual average fluxes of Cd, Pb and Zn, which are major contamination elements, to the sea were 179 ± 21 tons/year, 7810 ± 675 tons/year, and 12,000 ± 1320 tons/year,respectively, in which approximately 8.7%, 11.9% and 2.7% of their loadings, respectively,were contributed by pollution inputs. Element exports mainly occurred in the summer(44.4%–57.4%) in the lower part of the Changjiang River. A general relationship between sediment retention and element content suggests a positive feedback mechanism for the decreased number of particles, in which element riverine loadings are reduced due to the enhanced trapping effect by the Three Gorges Dam(TGD). Compared to those in 1980,current element shares of the Changjiang River compared to the global budget have declined due to the construction of the TGD. | Hao Wang Xiangbin Ran Jingxi Li Jun Liu Wentao Wu Menglu Li Jiaye Zang | 2019 | Journal of Environmental Sciences2019，31，9： | 6 |
| 3 | Biopsy-free in vivo virtual histology of skin using deep learning显示文摘An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin tumors.The process is cumbersome and time-consuming,often leading to unnecessary biopsies and scars.Emerging noninvasive optical technologies such as reflectance confocal microscopy(RCM)can provide label-free,cellular-level resolution,in vivo images of skin without performing a biopsy.Although RCM is a useful diagnostic tool,it requires specialized training because the acquired images are grayscale,lack nuclear features,and are difficult to correlate with tissue pathology.Here,we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution,enabling visualization of the epidermis,dermal-epidermal junction,and superficial dermis layers.The network was trained under an adversarial learning scheme,which takes ex vivo RCM images of excised unstained/label-free tissue as inputs and uses the microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth.We show that this trained neural network can be used to rapidly perform virtual histology of in vivo,label-free RCM images of normal skin structure,basal cell carcinoma,and melanocytic nevi with pigmented melanocytes,demonstrating similar histological features to traditional histology from the same excised tissue.This application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies. | Jingxi Li Jason Garfinkel Xiaoran Zhang Di Wu Yijie Zhang Kevin de Haan Hongda Wang Tairan Liu Bijie Bai Yair Rivenson Gennady Rubinstein Philip O.Scumpia Aydogan Ozcan | 2021 | Light(Science & Applications)2021，10，12： | 1 |
| 4 | Development and Application of Ti-based Alloy Casting Technologies in the Field of Aerospace显示文摘Ti-based alloys have been widely applied in the aerospace field,owing to their outstanding performance.Precision casting can be used to make integrated near-net-shape components with complex thin-walled structures,which will further promote the engineering application of Ti-based alloys. In this paper,the research progress of Tibased alloys,e. g.,high-temperature Ti-based alloys,high-strength Ti-based alloys,TiAl-based alloys,Ti-based matrix composites,and their precision casting technologies are reviewed. In addition,the development directions of Tibased alloys are presented based on the application status of Ti-based alloys in the aerospace field. | HOU Zhengquan LI Baohui FENG Gangwen WU Jingxi FAN Lipeng GUO Yingfei CHEN Yuyong CHEN Bin ZHANG Xuliang | 2022 | 上海航天(中英文)2022，39，1： | 0 |
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