Optimization of Hydrometallurgical Purification for SiO_2 in the Process of Preparing Solar-grade Silicon

考察了Hf质量分数、H2C2O4质量分数、HnO3质量分数、酸浸时间、粒径、液体质量与固体质量的比值(简称液固比,下同)等因素对混酸法提纯SIO2工艺过程的影响,利用电感耦合等离子体发射光谱仪(ICP-OES)、场发射扫描电子显微镜(SEM)进行表征。结果表明,最佳工艺条件为:W(Hf)=2%、W(H2C2O4)=3%、W(HnO3)=30%、酸浸时间4 H、粒径100~120目、液固比4∶1、酸浸温度30℃。fE、Al、CA、P杂质的去除率分别达到99.99%、14.02%、73.27%、60.00%,经混酸法处理后SIO2中杂质总量的质量分数降至1.465x10-4。As a pre-treatment unit for preparing solar-grade silicon,hydrometallurgical route could remove most metallic impurities in silicon dioxide(SiO2) and raise the yield of the final product.Acid leaching of SiO2 could reduce the cost and energy consumption of industrialized development.Combined with high purity of reducing agent,the successor process of pyrometallurgy can also achieve "continuous casting".Factors such as the mass fraction of leaching agent,time,the particle size of SiO2,and the liquid-solid ratio were investigated,and the samples were characterized by means of ICP-OES,SEM,etc.The optimal reaction conditions were as follows:w(HF)=2%,w(H2C2O4)=3%,w(HNO3)=30%,reaction time 4 h,the average size of SiO2 powder particle 100～120 mesh,the liquid-solid ratio 4∶1,and room temperature 30 ℃.It was found that the final removal rates of impurities of Fe,Al,Ca,P could reach 99.99%,14.02%,73.27%,and 60.00% respectively and the mass fraction of total amount of impurities could be reduced to 1.465×10-4

Cu-Ce-Zr基催化剂上CO自持燃烧及动力学实验研究

为了研究Cu-Ce-Zr基催化剂上CO的自持燃烧,采用浸渍法制备了负载型CuCe_(0.75)Zr_(0.25)O_y/ZSM-5、CuCe_(0.75)Zr_(0.25)O_y/TiO_2和溶胶凝胶法制备了复合氧化物CuCe_(0.75)Zr_(0.25)O_y催化剂,结合XRD、BET、SEM、O_2-TPD及CO-TPO等手段对催化剂进行表征与CO自持燃烧反应活性评价.结果表明:CO自持催化燃烧主要分为反应诱导阶段、飞温阶段及自持燃烧3个阶段.催化剂活性CuCe_(0.75)Zr_(0.25)O_y(t_(100)=65℃)&gt;CuCe_(0.75)Zr_(0.25)O_y/TiO_2(t_(100)=150℃) &gt;CuCe_(0.75)Zr_(0.25)O_y/ZSM-5(t_(100)=172℃).表观动力学研究表明,CO催化燃烧均遵循一级反应动力学,反应表观活化能大小顺序为CuCe_(0.75)Zr_(0.25)O_y/ZSM-5(259.7 kJ/mol)&gt; CuCe_(0.75)Zr_(0.25)O_y/TiO_2(69.7 kJ/mol)&gt; CuCe_(0.75)Zr_(0.25)O_y(55.4 kJ/mol).催化剂中活性物种质量分数对催化剂还原能力与储放氧能力的影响显著.</p

30Mn7Al汽车钢的力学性能与微观结构

提出一种成分为0.3C-7Mn-0.5Al (质量分数，%)的新型第三代汽车钢，通过20%～65%冷轧及后续625～675℃退火处理，获得近等比马氏体/亚稳奥氏体双相组织，具有1.0 GPa以上拉伸屈服强度，以及60～75 GPa·%强塑积(抗拉强度与断后伸长率之积)。原位拉伸观测表明，冷轧退火态样品表现出多重非均匀塑性变形特征：在初始塑性应变阶段，显示出屈服降及吕德斯应变平台；在随后的加工硬化硬化阶段，产生动态应变时效诱导应力阶跃。通过变形微结构的EBSD、TEM和3D-APT观测指出，动态微带变形及局部相变传递使样品在更大应变范围内持续应变硬化，是实现其优异力学性能主要微观机制

Research progress and prospects of remote sensing classification of urban vegetation(城市植被遥感分类研究进展与展望)

Urban vegetation is an important part of the urban environment, and remote sensing classification of urban vegetation is an important way to monitor and analyze urban green space. By sorting the research progress of remote sensing classification of urban vegetation at home and abroad, we started from two aspects of remote sensing data sources and classification methods, and analyzed the current problems and development trends in this field, in order to provide references for urban green space research. First, the applications of optical data, light detection and ranging (LiDAR) data and ground sensing data in the remote sensing classification of urban vegetation were summarized, and the advantages and disadvantages of different data sources were analyzed in depth. Second, the characteristics of classification methods applied in the remote sensing classification of urban vegetation were summarized through the study of three classification methods, including threshold segmentation, machine learning, and deep learning. Finally, the existing problems and future development directions in the remote sensing classification of urban vegetation were proposed.(城市植被是城市环境的重要组成部分，城市植被遥感分类是对城市绿度空间监测分析的重要方式。本文通过梳理国内外城市植被遥感分类研究进展，从遥感数据源和分类方法入手，分析该领域目前面临的问题及发展趋势，以期为城市绿度空间研究提供参考。首先，概述了光学数据、激光雷达数据及地面传感数据等数据源在城市植被遥感分类领域的应用，对不同数据源的优势与不足进行了深入分析；其次，基于阈值分割、机器学习和深度学习3种分类方法的研究，总结了应用于城市植被遥感分类领域各方法的特点；最后，提出了城市植被遥感分类研究中现存问题和未来发展方向。