Nanostructured Conductive Polymers for Advanced Energy Storage

Conductive polymers combine the attractive properties associated with conventional polymers and unique electronic properties of metals or semiconductors. Recently, nanostructured conductive polymers have aroused considerable research interest owing to their unique properties over their bulk counterparts, such as large surface areas and shortened pathways for charge/mass transport, which make them promising candidates for broad applications in energy conversion and storage, sensors, actuators, and biomedical devices. Numerous synthetic strategies have been developed to obtain various conductive polymer nanostructures, and high-performance devices based on these nanostructured conductive polymers have been realized. This Tutorial review describes the synthesis and characteristics of different conductive polymer nanostructures; presents the representative applications of nanostructured conductive polymers as active electrode materials for electrochemical capacitors and lithium-ion batteries and new perspectives of functional materials for next-generation high-energy batteries, meanwhile discusses the general design rules, advantages, and limitations of nanostructured conductive polymers in the energy storage field; and provides new insights into future directions.University of Texas at Austin3M Non-tenured Faculty awardWelch Foundation F-1861Materials Science and Engineerin

A novel object tracking algorithm based on compressed sensing and entropy of information

Acknowledgments This research is supported by (1) the Ph.D. Programs Foundation of Ministry of Education of China under Grant no. 20120061110045, (2) the Science and Technology Development Projects of Jilin Province of China under Grant no. 20150204007G X, and (3) the Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China.Peer reviewedPublisher PD

Greedy Strategy Works for k-Center Clustering with Outliers and Coreset Construction

We study the problem of k-center clustering with outliers in arbitrary metrics and Euclidean space. Though a number of methods have been developed in the past decades, it is still quite challenging to design quality guaranteed algorithm with low complexity for this problem. Our idea is inspired by the greedy method, Gonzalez\u27s algorithm, for solving the problem of ordinary k-center clustering. Based on some novel observations, we show that this greedy strategy actually can handle k-center clustering with outliers efficiently, in terms of clustering quality and time complexity. We further show that the greedy approach yields small coreset for the problem in doubling metrics, so as to reduce the time complexity significantly. Our algorithms are easy to implement in practice. We test our method on both synthetic and real datasets. The experimental results suggest that our algorithms can achieve near optimal solutions and yield lower running times comparing with existing methods