South China University of Technology

About: South China University of Technology is a education organization based out in Guangzhou, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 62343 authors who have published 69468 publications receiving 1251592 citations. The organization is also known as: SCUT & Huánán Lǐgōng Dàxué.

Bioinspired Graphene Nanopores with Voltage-Tunable Ion Selectivity for Na+ and K+

Abstract: Biological protein channels have many remarkable properties such as gating, high permeability, and selectivity, which have motivated researchers to mimic their functions for practical applications Herein, using molecular dynamics simulations, we design bioinspired nanopores in graphene sheets that can discriminate between Na+ and K+, two ions with very similar properties The simulation results show that, under transmembrane voltage bias, a nanopore containing four carbonyl groups to mimic the selectivity filter of the KcsA K+ channel preferentially conducts K+ over Na+ A nanopore functionalized by four negatively charged carboxylate groups to mimic the selectivity filter of the NavAb Na+ channel selectively binds Na+ but transports K+ over Na+ Surprisingly, the ion selectivity of the smaller diameter pore containing three carboxylate groups can be tuned by changing the magnitude of the applied voltage bias Under lower voltage bias, it transports ions in a single-file manner and exhibits Na+ selectivi

Intelligent Fault Diagnosis for Rotary Machinery Using Transferable Convolutional Neural Network

Abstract: Deep neural networks present very competitive results in mechanical fault diagnosis. However, training deep models require high computing power while the performance of deep architectures in extracting discriminative features for decision making often suffers from the lack of sufficient training data. In this paper, a transferable convolutional neural network (CNN) is proposed to improve the learning of target tasks. First, a one-dimensional CNN is constructed and pretrained based on large source task datasets. Then a transfer learning strategy is adopted to train a deep model on target tasks by reusing the pretrained network. Thus, the proposed method not only utilizes the learning power of deep network but also leverages the prior knowledge from the source task. Four case studies are considered and the effects of transfer layers and training sample size on classification effectiveness are investigated. Results show that the proposed method exhibits better performance compared with other algorithms.