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Search Results: 1 - 10 of 46932 matches for " 宋力昕 "
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镍薄膜对化学气相沉积法生长石墨烯的影响
香莲,,张涛
硅酸盐学报 , 2015, DOI: 10.14062/j.issn.0454-5648.2015.12.20
Abstract: 通过电子束蒸发法在石英玻璃表面沉积得到镍薄膜,研究了镍薄膜厚度及保温时间对化学气相沉积(CVD)石墨烯生长的影响。结果表明,镍薄膜的厚度及生长时间均对石墨烯的生长有重要影响。镍薄膜厚度小于70nm时,容易在石英基材表面发生去湿现象及Ostwald熟化,使得镍在石英表面凝聚成小颗粒;而适当增加镍薄膜的厚度,能够减少退火后薄膜表面的孔洞及缺陷,保证石墨烯薄膜的质量。当CVD过程中保温时间小于5min时,石墨烯在镍表面难以形成完整的膜;而保温时间超过15min后,则容易生长为石墨,而非石墨烯。适当厚度的镍薄膜及保温时间是制备大尺寸高质量石墨烯薄膜的前提。
高温处理对CNx薄膜晶化的影响
肖兴成,江伟辉,田静芬,,胡行方
物理学报 , 2000,
Abstract: 利用直流磁控溅射制得非晶态氮化碳膜,然后在高温下、常压N气氛中进行热处理,利用DTA,XRD和Auger研究晶化前后氮化碳成分、结构以及键态的变化.实验结果表明:在1186℃附近出现了晶化现象,高温晶化处理可以促进无定形氮化碳向晶态转变,在XRD图谱上出现αC3N4衍射峰.Auger实验结果表明膜中出现富C,Si,N的区域
介孔氧化钨电色薄膜的制备与性能研究
袁嘉国,章俞之,乐军,,胡行方<>
化学学报 , 2005,
Abstract: 采用一种新的非离子型gemini表面活性剂结构导向模板,成功制备了介孔氧化钨薄膜.通过SAXRD,TEM和N2吸附-脱附等方法考察薄膜的制备和微结构特性,发现获得的产物具有三维蠕虫介孔结构,比表面积可达145.5m2?g-1.测定了该薄膜在无水高氯酸锂/碳酸丙烯酯电解质溶液中的循环伏安和电致变色性能,并与无模板薄膜进行了对比研究.研究表明,由于具有更大的电化学活性比表面,纳米介孔氧化钨薄膜表现出增强的电色性能,在633nm波长处的透过率调制幅度可达60%以上,着色效率为51.7cm2?C-1.
二氧化钛超滤膜的制备及其渗透性能
章俞之,于云,,何超,胡行方,é
硅酸盐学报 , 2005,
Abstract:
α-SiC∶H薄膜的热行为研究
彭晓峰,孟佳,陈杰锋,,胡行方
硅酸盐学报 , 2003,
Abstract:
氯醇盐溶胶-凝胶法纳米结构氧化钨薄膜的光谱学特性
袁嘉国,章俞之,乐军,,胡行方
物理化学学报 , 2009,
Abstract:
黄河上游重点河段特征有机污染物现状调查分析
,张曙光,渠康,,王霞,徐建
人民黄河 , 2006,
Abstract: ?以黄河的水污染重点研究河段为调查对象,开展了典型特征有毒有机污染物的专项筛选和重点识别工作.首次采用半透膜采样技术(spmd)和其他先进测试手段对7个地表水和底质断面样品进行了分析.结果表明,研究河段有毒有机污染物的种类繁多,并确定了壬基酚类和多环芳烃类等作为典型特征有毒有机物.
微等离子体氧化al2o3陶瓷膜的组织结构与形成过程
辛世刚?,姜兆华?,赵荣根?,,胡行方?
无机化学学报 , 2004,
Abstract:
砚状ZnO/石墨烯复合物的制备及其光催化性能
Synthesis and Photocatalytic Performance of Ink Slab-Like ZnO/Graphene Composites

张云龙,章俞之,(),,(),郭云峰,吴岭南,张涛
- , 2017, DOI: 10.3866/PKU.WHXB201705184
Abstract: 采用一步溶液法制备了具有砚状形貌的ZnO/石墨烯复合材料。利用扫描电子显微镜(SEM)、高分辨透射电子显微镜(HRTEM)等研究不同制备条件下ZnO形貌、石墨烯的复合状态和砚状ZnO的生长机理;通过测试300 W氙灯对甲基蓝溶液(MB)的光催化效率,研究制备条件、形貌结构对复合物的光催化性能的影响;通过对复合物光致发光(PL)光谱以及紫外-可见光谱测试,研究石墨烯复合物对光生电子-空穴对的复合以及光吸收效率的影响。研究结果表明,砚状ZnO的生长机理为“掏蚀机理”;复合石墨烯增强了这种ZnO的光吸收效率、降低了ZnO的带隙,并且降低了光生电子-空穴对复合几率,有利于提高光催化性能;砚状ZnO的砚底上表面粗糙,有利于反应面积的增加,砚底的厚度较薄,有利于光生电子-空穴对在较强的内建电场下迅速向相反方向分离,降低其复合几率,从而使其具有优异的光催化性能。
A special ZnO/graphene composite with an ink slab-like shape was synthesized by a facile one-step solution method. The morphology of the ink slab-like ZnO/graphene composites produced under different reaction conditions was studied by scanning electron microscopy (SEM), field emission SEM (FESEM), and high resolution transmission electron microscopy (HRTEM). The photocatalytic properties of the products obtained under different reaction conditions were evaluated to determine the effect of reaction conditions and morphology. Photoluminescence (PL) and UV-visible spectra were measured to study the recombination of electron-hole pairs and absorption of UV-visible light. The results showed that the growth process of the ink slab-like ZnO involves the 'corrosion mechanism'. The combination of graphene greatly enhanced the photocatalytic performance by enhancing light absorption, decreasing the band gap, and reducing the recombination probability of electron-hole pairs. Moreover, the bottom of the ink slab-like ZnO with a rough surface can greatly increase the reaction area. The extremely thin bottom of the ink slab offers a considerable build-in internal electric field that accelerates the separation of electron-hole pairs, thus decreasing the recombination probability and enhancing the photocatalytic performance
基于LDA+kernel-KNNFLC的语音情感识别方法
Speech emotion recognition based on LDA+kernel-KNNFLC

,查诚,徐新洲,,
- , 2015, DOI: 10.3969/j.issn.1001-0505.2015.01.002
Abstract: 结合K近邻、核学习方法、特征线重心法和LDA算法,提出了用于情感识别的LDA+kernel-KNNFLC方法.首先针对先验样本特征造成的计算量庞大问题,采用重心准则学习样本距离,改进了核学习的K近邻方法;然后加入LDA对情感特征向量进行优化,在避免维度冗余的情况下,更好地保证了情感信息识别的稳定性.最后,通过对特征空间再学习,结合LDA的kernel-KNNFLC方法优化了情感特征向量的类间区分度,适合于语音情感识别.对包含120维全局统计特征的语音情感数据库进行仿真实验,对降维方案、情感分类器和维度参数进行了多组对比分析.结果表明,LDA+kernel-KNNFLC方法在同等条件下性能提升效果最显著.
Based on KNN(K-nearest neighbor), kernel learning, FLC(feature line centroid)and LDA(linear discriminant analysis)algorithm, the LDA+kernel-KNNFLC method is put forward for emotion recognition according to the characteristics of the speech emotion features. First, in view of the large amount of calculation caused by the prior sample characteristics, the KNN of kernel learning method is improved by learning sample distances with the FLC. Secondly, by adding LDA to emotional feature vectors, the stability of emotional information recognition is ensured and dimensional redundancy is avoided. Finally, by the relearning of feature spaces, LDA+kernel-KNNFLC can optimize the degree of differentiation between emotional feature vectors, which is suitable for speech emotion recognition(SER). An emotional database is used for simulation tests, which contains 120 dimensional global statistical characteristics. Multiple comparison analysis is conducted through the dimension reduction scheme, emotion classifiers and dimension parameters. The results show that the improvement effect for SER by using LDA+kernel-KNNFLC is remarkable under the same conditions
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