China Jiliang University

About: China Jiliang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Fiber optic sensor & Optical fiber. The organization has 9291 authors who have published 8932 publications receiving 95279 citations. The organization is also known as: China Institute of Metrology.

MoS2 architectures supported on graphene foam/carbon nanotube hybrid films: highly integrated frameworks with ideal contact for superior lithium storage

Abstract: Three-dimensional (3D) nanoworm-like MoS2 architectures have been successfully synthesized and directly supported on graphene foam/carbon nanotubes (GF/CNT) hybrid films. The sp2-hybridized GF/CNT films provide robust frameworks with an ideal contact for the nucleation and subsequent massive growth of the MoS2 architectures, while acting as an efficient current collector with a conductive contact for binder-free electrodes. The as-prepared hierarchical MoS2@GF/CNT electrode shows capacities of 1368 mA h g−1 and 823 mA h g−1 at current densities of 200 mA g−1 and 5000 mA g−1, and can retain 81.3% of the initial reversible capacity up to 120 cycles.

Isolation and crystallographic identification of four isomers of Sm@C90.

Abstract: Four isomers with the composition SmC90 were obtained from carbon soot produced by electric arc vaporization of carbon rods doped with Sm2O3. These were labeled Sm@C90(I), Sm@C90(II), Sm@C90(III), and Sm@C90(IV) in order of their elution times during chromatography on a Buckyprep column with toluene as the eluent. Analysis of the structures by single-crystal X-ray diffraction on cocrystals formed with Ni(octaethylporphyrin) reveals the identities of the individual isomers as follows: I, Sm@C2(40)-C90; II, Sm@C2(42)-C90; III, Sm@C2v(46)-C90 and IV, Sm@C2(45)-C90. This is the most extensive series of isomers of any endohedral fullerene to have their individual structures determined by single-crystal X-ray diffraction. The cage structures of these four isomers can be related pairwise to one another in a formal sense through sequential Stone−Wales transformations.

2D Heterostructure of Amorphous CoFeB Coating Black Phosphorus Nanosheets with Optimal Oxygen Intermediate Absorption for Improved Electrocatalytic Water Oxidation.

Abstract: The oxygen evolution reaction (OER) plays a paramount role in a variety of electrochemical energy conversion devices, and the exploration of highly active, stable, and low-cost electrocatalysts is one of the most important topics in this field. The exfoliated black phosphorus (EBP) nanosheet with a two-dimensional (2D) layered structure has high carrier mobility but is limited by excessive oxygen-containing intermediate absorption and fast deterioration in air. We here report the fabrication of nanohybrids of amorphous CoFeB nanosheets on EBP nanosheets (EBP/CoFeB). The 2D/2D heterostructure, thanks to the electronic interactions and oxygen affinity difference between EBP and CoFeB nanosheets, is capable of balancing the oxygen-containing intermediate absorption to an optimal status for facilitating the OER process. While the crystalline EBP contributes to the improved conductivity, the amorphous coating protects EBP and thus ensures the catalytic stability. The EBP/CoFeB electrocatalyst shows excellent OER performance with an ultralow overpotential of 227 mV at 10 mA cm-2 with an ultrasmall Tafel slope of 36.7 mV dec-1 with excellent stability. This study may inspire more researches to develop heterostructured nanohybrid electrocatalysts for a diversity of electrochemical reactions.

Transformer Winding Fault Diagnosis Using Vibration Image and Deep Learning

Abstract: Winding condition assessment is an essential task for operating transformers, and the vibration method provides a low-cost and non-intrusive approach. In this paper, a novel feature extraction method based on vibration analysis is proposed, which converts the vibration monitoring data with load information into a vibration image. Then, a deep learning approach based on convolutional neural network (CNN) is used to classify the images belong to different classes. In the laboratory experiment, free vibration tests are performed on an on-load winding model, which are used to verify the relationship between the natural frequency and the electromagnetic force under different clamping forces. During the field experiment, transformers are divided into three categories, including normal, degraded and anomalous, and the proposed scheme is trained and tested by using the vibration samples acquired from more than 100 operating transformers. The performance of the CNN classifier under different input sizes is investigated, which achieves 98.3% overall accuracy. Besides, the confusion matrices obtained by other methods are compared, such as artificial neural network (ANN), support vector machine (SVM) and naive Bayes classifier (NBC). The results show that the proposed scheme including the vibration image extraction method and the CNN classifier offers superior performance in winding fault diagnosis.