Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Nematic liquid crystal materials as a morphology regulator for ternary small molecule solar cells with power conversion efficiency exceeding 10

Abstract: Solution-processed small molecule solar cells (SMSCs) are fabricated based on DRCN5T:PC71BM as active layers, the power conversion efficiency (PCE) is markedly increased from 3.63% to 9.11% for the active layers undergoing up-side-down thermal annealing and solvent vapor annealing post-treatments. The PCE improvement should be attributed to the appropriate phase separation consisting of enhanced crystallinity of donor and purified acceptor domain at nanoscale. The nematic liquid crystal small molecule BTR is selected as the second donor and morphology regulator to prepare ternary SMSCs. The champion PCE of ternary SMSCs was improved to 10.05% by mixing 1.5 wt% BTR, which corresponds to a 10.3% PCE improvement compared with the optimized binary SMSCs. The performance improvement is mainly attributed to the further optimized phase separation and complementary photon harvesting between DRCN5T and BTR, which could be well demonstrated from absorption spectra, two dimensional grazing incidence X-ray diffraction (2D-GIXD) and transmission electron microscopy (TEM).

Shape-Controlled Synthesis of One-Dimensional MnO2 via a Facile Quick-Precipitation Procedure and its Electrochemical Properties

Abstract: MnO2 with different crystallographic forms (α, γ) and morphologies (needles, rods, and spindles) was fabricated via a facile quick-precipitation procedure at a low temperature (about 83 °C) without using any templates or surfactants. The proposed mechanism is focused on the different reaction parameters that affect the behaviors of MnO6 octahedron units (basic structural framework of MnO2), which then exert an influence on the formation and stack of nuclei in the crystal growth process. Specific capacitances (Cs) calculated according to the area of the cyclic voltammograms (CV) curves were 233.5, 83.1 F·g−1 for the needle- and 95.5, 29.3 F·g−1 for the spindle-like products at the scan rate of 5, 100 mV·s−1, respectively. The Cs of needle-like samples calculated by discharge curves at the current density of 2 mA·cm2 is 209.8 F·g−1, close to the value calculated by CV curves at the scan rate of 5 mV·s−1. This methodology facilitates us to synthesize MnO2 with discrepant one-dimensional (1D) nanostructures v...

Anticontrol of chaos in continuous-time systems via time-delay feedback

Abstract: In this paper, a systematic design approach based on time-delay feedback is developed for anticontrol of chaos in a continuous-time system. This anticontrol method can drive a finite-dimensional, continuous-time, autonomous system from nonchaotic to chaotic, and can also enhance the existing chaos of an originally chaotic system. Asymptotic analysis is used to establish an approximate relationship between a time-delay differential equation and a discrete map. Anticontrol of chaos is then accomplished based on this relationship and the differential-geometry control theory. Several examples are given to verify the effectiveness of the methodology and to illustrate the systematic design procedure.

Weakly Supervised Deep Metric Learning for Community-Contributed Image Retrieval

Abstract: Recent years have witnessed the explosive growth of community-contributed images with rich context information, which is beneficial to the task of image retrieval. It can help us to learn a suitable metric to alleviate the semantic gap. In this paper, we propose a new distance metric learning algorithm, namely weakly-supervised deep metric learning (WDML), under the deep learning framework. It utilizes a progressive learning manner to discover knowledge by jointly exploiting the heterogeneous data structures from visual contents and user-provided tags of social images. The semantic structure in the textual space is expected to be well preserved while the problem of the noisy, incomplete or subjective tags is addressed by leveraging the visual structure in the original visual space. Besides, a sparse model with the ${\ell _{2,1}}$ mixed norm is imposed on the transformation matrix of the first layer in the deep architecture to compress the noisy or redundant visual features. The proposed problem is formulated as an optimization problem with a well-defined objective function and a simple yet efficient iterative algorithm is proposed to solve it. Extensive experiments on real-world social image datasets are conducted to verify the effectiveness of the proposed method for image retrieval. Encouraging experimental results are achieved compared with several representative metric learning methods.