|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Adsorption and desorption characteristics of diphenylarsenicals in two contrasting soils显示文摘Diphenylarsinic acid(DPAA) is formed during the leakage of aromatic arsenic chemical weapons in soils,is persistent in nature,and results in arsenic contamination in the field.The adsorption and desorption characteristics of DPAA were investigated in two typical Chinese soils,an Acrisol(a variable-charge soil) and a Phaeozem(a constant-charge soil).Their thermodynamics and some of the factors influencing them(i.e.,initial pH value,ionic strength and phosphate) were also evaluated using the batch method in order to understand the environmental fate of DPAA in soils.The results indicate that Acrisol had a stronger adsorption capacity for DPAA than Phaeozem.Soil DPAA adsorption was a spontaneous and endothermic process and the amount of DPAA adsorbed was affected significantly by variation in soil pH and phosphate.In contrast,soil organic matter and ionic strength had no significant effect on adsorption.This suggests that DPAA adsorption may be due to specific adsorption on soil mineral surfaces.Therefore,monitoring the fate of DPAA in soils is recommended in areas contaminated by leakage from chemical weapons. | Anan Wang Shixin Li Ying Teng Wuxin Liu Longhua Wu Haibo Zhang Yujuan Huang Yongming Luo Peter Christie | 2013 | Journal of Environmental Sciences2013，25，6： | 8 |
| 2 | Degradation mechanism analysis of LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2) single crystal cathode materials through machine learning显示文摘LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2)(NCM523)has become one of the most popular cathode materials for current lithium-ion batteries due to its high-energy density and cost performance.However,the rapid capacity fading of NCM severely hinders its development and applications.Here,the single crystal NCM523 materials under different degradation states are characterized using scanning transmission electron microscopy(STEM).Then we developed a neural network model with a two-sequential attention block to recognize the crystal structure and locate defects in STEM images.The number of point defects in NCM523 is observed to experience a trend of increasing first and then decreasing in the degradation process.The space between the transition metal columns shrinks obviously,inducing dramatic capacity decay.This analysis sheds light on the defect evolution and chemical transformation correlated with layered material degradation.It also provides interesting hints for researchers to regenerate the electrochemical capacity and design better battery materials with longer life. | Wuxin Sha Yaqing Guo Danpeng Cheng Qigao Han Ping Lou Minyuan Guan Shun Tang Xinfang Zhang Songfeng Lu Shijie Cheng Yuan-Cheng Cao | 2022 | npj Computational Materials2022，，1： | 1 |
| 3 | Bioremediation of polycyclic aromatic hydrocarbons contaminated soil with Monilinia sp.: degradation and microbial community analysis显示文摘 | Yucheng Wu Yongming Luo Dexun Zou Jinzhi Ni Wuxin Liu Ying Teng Zhengao Li | 2008 | Biodegradation2008，，2： | 1 |
| 4 | A Service Robot with Lightweight Arms and a Trinocular Vision Sensor 显示文摘 | LI Xianhua TAN Shili HUANG Wuxin | 2010 | Key Engineering Materials2010，439，： | 1 |
| 5 | Manganese uptake and interactions with cadmium in the hyperaccumulator— Phytolacca Americana L.显示文摘 | Kejian Peng Chunling Luo Wuxin You Chunlan Lian Xiangdong Li Zhenguo Shen | 2007 | Journal of Hazardous Materials2007，，1： | 1 |
| 6 | Machine learning in polymer informatics显示文摘Polymers have been widely used in energy storage,construction,medicine,aerospace,and so on.However,the complexity of chemical composition and morphology of polymers has brought challenges to their development.Thanks to the integration of machine leaming algorithms and large data resources,the data-driven methods have opened up a new road for the development of poly-mer science and engineering.The emerging polymer informatics attempts to accelerate the performance prediction and process optimization of new poly-mers by using machine learning models based on reliable data.With the grad-ual supplement of currently available databases,the emergence of new databases and the continuous improvement of machine learning algorithms,the research paradigm of polymer informatics will be more efficient and widely used.Based on these points,this paper reviews the development trends of machine learning assisted polymer informatics and provides a simple introduc-tion for researchers in materials,artificial intelligence,and other fields. | Wuxin Sha Yan Li Shun Tang Jie Tian Yuming Zhao Yaqing Guo Weixin Zhang Xinfang Zhang Songfeng Lu Yuan-Cheng Cao Shijie Cheng | 2021 | InfoMat2021，3，4： | 1 |
| 7 | Bioremediation of polycyclic aromatic hydrocarbons contaminated soil with Monilinia sp.: degradation and microbial community analysis显示文摘 | Yucheng Wu Yongming Luo Dexun Zou Jinzhi Ni Wuxin Liu Ying Teng Zhengao Li | 2008 | Biodegradation2008，，2： | 1 |
| 8 | AtomGAN:unsupervised deep learning for fast and accurate defect detection of 2D materials at the atomic scale显示文摘The extraction of atomic-level material features from electron microscope images is crucial for studying structure-property relationships and discovering new materials.However,traditional electron microscope analyses rely on time-consuming and complex human operations;thus,they are only applicable to images with a small number of atoms.In addition,the analysis results vary due to observers’individual deviations.Although efforts to introduce automated methods have been performed previously,many of these methods lack sufficient labeled data or require various conditions in the detection process that can only be applied to the target material.Thus,in this study,we developed AtomGAN,which is a robust,unsupervised learning method,that segments defects in classical 2D material systems and the heterostructures of MoS_(2)/WS_(2)automatically.To solve the data scarcity problem,the proposed model is trained on unpaired simulated data that contain point and line defects for MoS_(2)/WS_(2).The proposed AtomGAN was evaluated on both simulated and real electron microscope images.The results demonstrate that the segmented point defects and line defects are presented perfectly in the resulting figures,with a measurement precision of 96.9%.In addition,the cycled structure of AtomGAN can quickly generate a large number of simulated electron microscope images. | Danpeng CHENG Wuxin SHA Zuo XU Shide LI Zhigao YIN Yuling LANG Shun TANG Yuan-Cheng CAO | 2023 | Science China(Information Sciences)2023，66，6： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员