Xi'an Jiaotong University

About: Xi'an Jiaotong University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Heat transfer & Dielectric. The organization has 85440 authors who have published 99682 publications receiving 1579683 citations. The organization is also known as: '''Xi'an Jiaotong University''' & Xi'an Jiao Tong University.

Citrate-Assisted Growth of NiCo2O4 Nanosheets on Reduced Graphene Oxide for Highly Reversible Lithium Storage

Abstract: Hybrid nanostructures based on graphene and transition metal oxides hold great promise as high-performance electrode materials for next-generation lithium-ion batteries. In this work, the rational design and fabrication of NiCo2O4 nanosheets supported on reduced graphene oxide (denoted as rGO/NiCo2O4) is presented as a novel anode material for highly efficient and reversible lithium storage. A solution method is applied to grow Ni-Co precursor nanosheets on rGO, in which the addition of trisodium citrate is found crucial to guide the formation of uniform Ni-Co precursor nanosheets. Subsequent thermal treatment results in formation of crystalline NiCo2O4 nanosheets on rGO without damaging the morphology. The interconnected NiCo2O4 nanosheets form hierarchically porous films on both sides of rGO. Such a hybrid nanostructure would effectively promote the charge transport and withstand volume variation upon prolonged charge/discharge cycling. As a result, the rGO/NiCo2O4 nanocomposite demonstrates high reversible capacities of 954.3 and 656.5 mAh g–1 over 50 cycles at current densities of 200 and 500 mA g–1 respectively, and remarkable capacity retention at increased current densities.

The Nanostructured Origin of Deformation Twinning

Abstract: We have revealed the fundamental embryonic structure of deformation twins using in situ mechanical testing of magnesium single crystals in a transmission electron microscope. This structure consists of an array of twin-related laths on the scale of several nanometers. A computational model demonstrates that this structure should be a generic feature at the incipient stage of deformation twinning when there are correlated nucleation events. Our results shed light on the origin of twinning-induced plasticity and transformation toughening, critical to the development of advanced structural alloys with high strength, ductility, and toughness.

High-Performance Non-Fullerene Polymer Solar Cells Based on a Pair of Donor-Acceptor Materials with Complementary Absorption Properties.

Abstract: A 7.3% efficiency non-fullerene polymer solar cell is realized by combining a large-bandgap polymer PffT2-FTAZ-2DT with a small-bandgap acceptor IEIC. The complementary absorption of donor polymer and small-molecule acceptor is responsible for the high-performance of the solar-cell device. This work provides important guidance to improve the performance of non-fullerene polymer solar cells.

Multispectral Images Denoising by Intrinsic Tensor Sparsity Regularization

Abstract: Multispectral images (MSI) can help deliver more faithful representation for real scenes than the traditional image system, and enhance the performance of many computer vision tasks. In real cases, however, an MSI is always corrupted by various noises. In this paper, we propose a new tensor-based denoising approach by fully considering two intrinsic characteristics underlying an MSI, i.e., the global correlation along spectrum (GCS) and nonlocal self-similarity across space (NSS). In specific, we construct a new tensor sparsity measure, called intrinsic tensor sparsity (ITS) measure, which encodes both sparsity insights delivered by the most typical Tucker and CANDECOMP/ PARAFAC (CP) low-rank decomposition for a general tensor. Then we build a new MSI denoising model by applying the proposed ITS measure on tensors formed by non-local similar patches within the MSI. The intrinsic GCS and NSS knowledge can then be efficiently explored under the regularization of this tensor sparsity measure to finely rectify the recovery of a MSI from its corruption. A series of experiments on simulated and real MSI denoising problems show that our method outperforms all state-of-the-arts under comprehensive quantitative performance measures.