Nanjing University

About: Nanjing University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 85961 authors who have published 105504 publications receiving 2289036 citations. The organization is also known as: NJU & Nanking University.

Conducting Polymer Nanostructures: Template Synthesis and Applications in Energy Storage

Abstract: Conducting polymer nanostructures have received increasing attention in both fundamental research and various application fields in recent decades. Compared with bulk conducting polymers, conducting polymer nanostructures are expected to display improved performance in energy storage because of the unique properties arising from their nanoscaled size: high electrical conductivity, large surface area, short path lengths for the transport of ions, and high electrochemical activity. Template methods are emerging for a sort of facile, efficient, and highly controllable synthesis of conducting polymer nanostructures. This paper reviews template synthesis routes for conducting polymer nanostructures, including soft and hard template methods, as well as its mechanisms. The application of conducting polymer mesostructures in energy storage devices, such as supercapacitors and rechargeable batteries, are discussed.

Intracontinental subduction: a possible mechanism for the Early Palaeozoic Orogen of SE China

Abstract: The Early Palaeozoic Orogen of SE China consists of three lithotectonic elements, from top to bottom: a sedimentary Upper Unit, a metamorphic Lower Unit and a gneissic basement. The boundaries between these units are flat lying, south directed, ductile decollements. The lower one is coeval with an amphibolite facies metamorphism (M1). The belt is reworked by migmatite‐granite domes, high-temperature metamorphism (M2) and granitic plutons related to post-orogenic crustal melting. We date here the syn-M1 ductile shearing at 453 ± 7 Ma by U-Th ⁄Pb method on monazite. Previous ages and our new 40 Ar ⁄ 39 Ar ages of biotites and muscovites show that the metamorphic rocks experienced syn-M2 exhumation from 440 to 400 Ma. The Early Palaeozoic Orogen of SE China is an intracontinental belt in which decollements accommodated the north-directed subduction of the Cathaysian continent. This orogen is an example of intracontinental subduction that was not preceded by oceanic subduction.

Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization

Abstract: The matrix separation problem aims to separate a low-rank matrix and a sparse matrix from their sum. This problem has recently attracted considerable research attention due to its wide range of potential applications. Nuclear-norm minimization models have been proposed for matrix separation and proved to yield exact separations under suitable conditions. These models, however, typically require the calculation of a full or partial singular value decomposition at every iteration that can become increasingly costly as matrix dimensions and rank grow. To improve scalability, in this paper, we propose and investigate an alternative approach based on solving a non-convex, low-rank factorization model by an augmented Lagrangian alternating direction method. Numerical studies indicate that the effectiveness of the proposed model is limited to problems where the sparse matrix does not dominate the low-rank one in magnitude, though this limitation can be alleviated by certain data pre-processing techniques. On the other hand, extensive numerical results show that, within its applicability range, the proposed method in general has a much faster solution speed than nuclear-norm minimization algorithms and often provides better recoverability.

A projection neural network and its application to constrained optimization problems

Abstract: In this paper, we present a recurrent neural network for solving the nonlinear projection formulation. It is shown here that the proposed neural network is stable in the sense of Lyapunov and globally convergent, globally asymptotically stable, and globally exponentially stable, respectively under different conditions. Compared with the existing neural network for solving the projection formulation, the proposed neural network has a single-layer structure and is amenable to parallel implementation. Moreover, the proposed neural network has no Lipschitz condition, and, thus can be applied to solve a very broad class of constrained optimization problems that are special cases of the nonlinear projection formulation. Simulation shows that the proposed neural network is effective in solving these constrained optimization problems.