Preparation of Silver Nanowires via a Rapid,Scalable and Green Pathway

Rapid synthesis of silver nanowires(Ag NWs) with high quality and a broad processing window is challenging because of the low selectivity of the formation of multiply twinned particles at the nucleation stage for subsequent Ag NWs growth.Herein we report a systematic study of the water-involved heterogeneous nucleation of Ag NWs with high rate(less than 20 min) in a simple and scalable preparation method.Using glycerol as a reducing agent and a solvent with a high boiling point,the reaction is rapidly heated to 210 ℃ in air to synthesize Ag NWs with a very high yield in gram level.It is noted that the addition of a small dose of water plays a key role for obtaining highly pure Ag NWs in high yield,and the optimal water/glycerol ratio is0.25%.After investigating a series of forming factors including reaction temperature and dose of catalysts,the formation kinetics and mechanism of the Ag NWs are proposed.Compared to other preparation methods,our strategy is simple and reproducible.These Ag NWs show a strong Raman enhancement effect for organic molecules on their surface.     

海底电缆交叉鬼波化双检合并技术改进及应用

双检合并技术在海底电缆地震资料处理中被广泛应用,可衰减电缆鬼波的影响。传统方式以水、陆检记录的波场记录相同为假设,电缆鬼波压制效果差,合并效果并不理想。在分析交叉鬼波化双检合并技术基本原理的基础上,综合利用水检资料与陆检鬼波褶积的结果和陆检资料与水检鬼波褶积的结果求取刻度算子,同时在地震资料处理中利用陆检资料标定水检资料,从而优化了交叉鬼波化双检合并技术。实际资料处理结果表明,改进后的技术可有效衰减电缆鬼波,分离出上行波场,大幅度拓宽地震资料频带,提高地震资料分辨率,从而提供更丰富的构造细节信息。     

GOS+MU:一种查询对象选择新方法

在分析单一MU(Most Uncertainty)采样缺陷的基础上,提出一种"全局最优搜寻"方法 GOS(Global Optimal Search),并结合MU共同完成查询选择。GOS+MU方法中,GOS着眼全局寻找目标,在应用环境能提供的训练样本数量有限、分类器受训不充分时,该方法选择的对象学习价值高,能快速推进分类器学习进程;MU则能够在GOS采样失效情形下,利用分类器当前训练成果,选择查询不确定性最强的样本补充训练集。通过对网络商品的用户评论进行分类仿真,并比较其他采样学习方法的效果,证明了GOS+MU方法在压缩学习成本、提高训练效率方面的有效性。