Tsinghua University

About: Tsinghua University is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 129978 authors who have published 200506 publications receiving 4549561 citations. The organization is also known as: Tsinghua & THU.

Graphene-based materials in electrochemistry

Abstract: Graphene, as the fundamental 2D carbon structure with exceptionally high crystal and electronic quality, has emerged as a rapidly rising star in the field of material science. Its sudden discovery in 2004 led to an explosion of interest in the study of graphene with respect to its unique physical, chemical, and mechanical properties, opening up a new research area for materials science and condensed-matter physics, and aiming for wide-ranging and diversified technological applications. In this critical review, we will describe recent advances in the development of graphene-based materials from the standpoint of electrochemistry. To begin with, electron transfer properties of graphene will be discussed, involving its unusual electronic structure, extraordinary electronic properties and fascinating electron transport. The next major section deals with the exciting progress related to graphene-based materials in electrochemistry since 2004, including electrochemical sensing, electrochemiluminescence, electrocatalysis, electrochemical energy conversion and FET devices. Finally, prospects and further developments in this exciting field of graphene-based materials are also suggested (224 references).

Scalable synthesis of hierarchically structured carbon nanotube–graphene fibres for capacitive energy storage

Abstract: Hierarchical hybrid carbon fibres consisting of a network of nitrogen-doped reduced graphene oxide and single-walled carbon nanotubes are synthesized and subsequently used to make a supercapacitor with high volumetric energy density.

Learning Transferable Features with Deep Adaptation Networks

Abstract: Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation Network (DAN) architecture, which generalizes deep convolutional neural network to the domain adaptation scenario. In DAN, hidden representations of all task-specific layers are embedded in a reproducing kernel Hilbert space where the mean embeddings of different domain distributions can be explicitly matched. The domain discrepancy is further reduced using an optimal multikernel selection method for mean embedding matching. DAN can learn transferable features with statistical guarantees, and can scale linearly by unbiased estimate of kernel embedding. Extensive empirical evidence shows that the proposed architecture yields state-of-the-art image classification error rates on standard domain adaptation benchmarks.