Hierarchical porous onion-shaped LiMn<Subscript>2</Subscript>O<Subscript>4</Subscript> as ultrahigh-rate cathode material for lithium ion batteries

Spinel LiMn₂O₄ is a widely utilized cathode material for Li-ion batteries. However, its applications are limited by its poor energy density and power density. Herein, a novel hierarchical porous onion-like LiMn₂O₄(LMO) was prepared to shorten the Li⁺ diffusion pathway with the presence of uniform pores and nanosized primary particles. The growth mechanism of the porous onion-like LiMn₂O₄ was analyzed to control the morphology and the crystal structure so that it forms a polyhedral crystal structure with reduced Mn dissolution. In addition, graphene was added to the cathode (LiMn₂O₄/graphene) to enhance the electronic conductivity. The synthesized LiMn₂O₄/graphene exhibited an ultrahigh-rate performance of 110.4 mAh·g^–1 at 50 C and an outstanding energy density at a high power density, maintaining 379.4 Wh·kg^–1 at 25,293 W·kg^–1. Besides, it shows durable stability, with only 0.02% decrease in the capacity per cycle at 10 C. Furthermore, the (LiMn₂O₄/graphene)/graphite full-cell exhibited a high discharge capacity. This work provides a promising method for the preparation of outstanding, integrated cathodes for potential applications in lithium ion batteries.

Open image in new window

     

Aggregation-induced emission luminogen-assisted stimulated emission depletion nanoscopy for super-resolution mitochondrial visualization in live cells

Nano Research - Aggregation-induced emission luminogens (AIEgens) are fluorescent agents that are ideal for bioimaging and have been widely used for organelle targeting, cellular mapping, and...     

云计算环境下信息安全分析

基于互联网的云计算被认为是当今互联网发展的方向,近年来引起人们的广泛关注,如何构建安全的云计算环境成为当前计算机学科研究的热点问题之一.文中从云计算的发展现状人手,介绍了NIST推出的云计算规范、五个本质特征和云计算服务模型,分析了CSA云计算安全参考模型和Jericho Forum的云立方体模型,并从安全边界、数据安全、应用安全三个方面讨论了当前云计算环境下存在的信息安全问题,最后给出了云计算环境下保证信息安全的解决方案.     

一种改进的谱聚类方法及其在文本分析中的应用

随着文本资源的激增,特别是网页文本的迅速增加,针对文本的挖掘分析日益受到重视。谱聚类是文本聚类分析较常用的一种新型方法。该文将非负约束引入到传统的谱聚类算法中,提出了一种基于非负约束的谱聚类方法。文中实验验证了所提出方法在中文文本聚类分析应用中的有效性。     

面向股票新闻的情感分类方法

互联网新闻资讯对证券市场和投资者有举足轻重的影响,新闻进行情感分类后再展示给用户,可以帮助投资者迅速做出投资决定.从文本分类的基本方法出发,实现了基于N-gram 统计模型的新词发现方法,并将所得结果用于构建中文分词词典和情感词典.同时引入评价理论,并用朴素贝叶斯、K 近邻和支持向量机3 种方法进行股票新闻标题的情感分类实验.所用实验数据来自2009 年“新浪财经”共计23 万余条的新闻标题,结果表明二分类的准确率最高可达82.9%.此外,还实现了一个原型系统用于展示股票新闻的分类结果.     

从“三段式”走向综合化教学——论工业设计教学模式的转变

在工业设计的教学模式中,由造型基础、设计基础、专业设计三大块组成的"三段式"的教学方法是至今仍被高校普遍采用的教学模式。而这种按部就班的教学方法显然已经不适应当今社会对设计人才知识结构的需求。本文由此提出以课题设计为中心的综合化教学模式,以期优化工业设计课程的知识结构,在培养设计人才上更为有效合理。     

绘画创作意象论

中国古典美学体系实质上是以"意象"为基础范畴的审美本体体系,绘画美学则侧重于从艺术的角度看待审美,所以"意象"就成为美学本体论的基本范畴。中国古典美学将"象"作为本体之基,是通过对"象"的观察去把握"道",中国绘画创造的思维过程实质上就是意象的思维过程。本文立足从中国画的创作思维的角度探究意象的形成步骤,以达到对情和景二种基本因素构成的"意象",这一中国古典美学的审美本体做出全面的诠释。     

一种互联网海量情报规则优化方法

现在很多部门都需要通过规则处理海量情报数据,规则的数量一旦达到海量级规模,对规则的处理技术的要求将会变得越来越高,海量规则的优化将是一个十分重要的问题.文中展示了一种海量规则的优化方法.若两个规则网最后的计算结果是一样的,则可以认为这两个规则网是等价的.基于该等价规则模块的变换可以将代价小的规则模块替换掉代价大的规则模块,从而以后可以选择一种代价最小的规则模块.本文提出规则网模块等价替换的5种优化方法,并对其做了相应的证明.通过优化,可以使得海量规则网处于计算代价尽量最小状态,以此减少处理机的计算工作量.分析表明,通过等价规则替换,可以大大提高规则处理效率.     

求旅行商问题近似解的碰撞算法

提出通过寻找精确解的边获得旅行商问题(TSP)近似解的思想,并以该思想为指导,设计一种新的碰撞算法。对国际通用的TSPLIB中不同城市规模的数据进行测试表明,该算法可以得到与目前已知最优解或相同或相近的结果。该算法不仅可以计算小规模的TSP,而且同样适用较大规模的TSP。     

A Novel Margin Based Algorithm for Feature Extraction

Margin based feature extraction has become a hot topic in machine learning and pattern recognition. In this paper, we present a novel feature extraction method called Adaptive Margin Maximization (AMM) in which margin is defined to measure the discrimination ability of the features. The motivation comes principally from the iterative weight modification mechanism of the powerful boosting algorithms. In our AMM, the samples are dynamically weighted and features are learned sequentially. After one new feature is learned by maximizing the weighted total margin of data, the weights are updated so that the samples with smaller margins receive larger weights. The feature learned in the next round will thus try to concentrate more on these “hard” samples adaptively. We show that when the data are projected onto the feature space learned by AMM, most examples have large margins, and therefore the nearest neighbor classifier yields small generalization error. This is in contrast to existing margin maximization based feature extraction approaches, in which the goal is to maximize the total margin. Extensive experimental results on benchmark datasets demonstrate the effectiveness of our method.