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 Apex-Time Network for Cross-Dataset Micro-Expression Recognition

The automatic recognition of micro-expression has been boosted ever since the successful introduction of deep learning approaches. As researchers working on such topics are moving to learn from the nature of micro-expression, the practice of using deep learning techniques has evolved from processing the entire video clip of micro-expression to the recognition on apex frame. Using the apex frame is able to get rid of redundant video frames, but the relevant temporal evidence of micro-expression would be thereby left out. This paper proposes a novel Apex-Time Network (ATNet) to recognize micro-expression based on spatial information from the apex frame as well as on temporal information from the respective-adjacent frames. Through extensive experiments on three benchmarks, we demonstrate the improvement achieved by learning such temporal information. Specially, the model with such temporal information is more robust in cross-dataset validations.Comment: 6 pages, 3 figures, 3 tables, code available, accepted in ACII 201

A novel method for searching the Ξc0/+\Xi_c^{0/+}-Ξc′0/+\Xi_c^{\prime 0/+} mixing effect in the angular distribution analysis of a four-body Ξc0/+\Xi_c^{0/+} decay

In this work, we raised a novel method for searching the $\Xi^{0+}_c$-$\Xi_c^{0+\prime}$ mixing effect in an angular distribution analysis of the $\Xi_c\to\Xi^{(\prime)}(\Lambda \pi)\ell^+\nu$ decay, where the mixing effect can be observed by the appearance of the $\Xi^{\prime}$ resonant. Armed with this angular distribution, the decay branching fraction and the forward-backward asymmetry are predicted. We pointed out that the forward-backward asymmetry, as a function of the invariant mass square of $\Xi^{(\prime)}$ and the $\Xi^{0+}_c$-$\Xi_c^{0+\prime}$ mixing angle $\theta_c$, can be used to distinguish the two resonants $\Xi^{(\prime)}$ and even provide a possibility to determine the exact mixing angle.Comment: 7 pages, 3 figure