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Your search keyword '"Hu, Xiangquan"' showing total 36 results
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36 results on '"Hu, Xiangquan"'

7. Based Big Data Analysis of Fraud Detection for Online Transaction Orders

Author

Yang, Qinghong, Hu, Xiangquan, Cheng, Zhichao, Miao, Kang, Zheng, Xiaohong, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Coulson, Geoffrey, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert, Series editor, Leung, Victor C.M., editor, Lai, Roy Xiaorong, editor, Chen, Min, editor, and Wan, Jiafu, editor

Published
2015
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17. Standard of payment for ecosystem services in a watershed based on InVEST model under different standpoints: a case study of the Weihe River in Gansu Province

Author

吴娜 Wu Na, 刘玉卿 Liu Yuqing, 康文慧 Kang Wenhui, 石培基 Shi Peiji, 宋晓谕 Song Xiaoyu, 胡想全 Hu Xiangquan, and 邓晓红 Deng Xiaohong

Subjects
Watershed, Geography, 010504 meteorology & atmospheric sciences, Ecology, 010501 environmental sciences, Water resource management, 01 natural sciences, Payment for ecosystem services, Ecology, Evolution, Behavior and Systematics, 0105 earth and related environmental sciences
Published
2018

25. Machine Learning Assisted Synthesis of Metal–Organic Nanocapsules

Author

Xie, Yunchao, Zhang, Chen, Hu, Xiangquan, Zhang, Chi, Kelley, Steven P., Atwood, Jerry L., and Lin, Jian

Abstract

Herein, we report machine learning algorithms by training data sets from a set of both successful and failed experiments for studying the crystallization propensity of metal–organic nanocapsules (MONCs). Among a variety of studied machine learning algorithms, XGBoost affords the highest prediction accuracy of >90%. The derived chemical feature scores that determine importance of reaction parameters from the XGBoost model assist to identify synthesis parameters for successfully synthesizing new hierarchical structures of MONCs, showing superior performance to a well-trained chemist. This work demonstrates that the machine learning algorithms can assist the chemists to faster search for the optimal reaction parameters from many experimental variables, whose features are usually hidden in the high-dimensional space.

Published
2020
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34. Geometry and binding properties of different multiple‐state glycine–Fe+/Fe2+complexes

Author

Ai, Hongqi, Bu, Yuxiang, Li, Ping, Li, Zhiqiang, Hu, Xiangquan, and Chen, Zhida

Abstract

Several biologically relevant glycine–Fe+/Fe2+complexes with three different multiplicities were studied for the first time by using the hybrid three‐parameter B3LYP density functional method with different basis sets. Single‐point calculations were also carried out at the BHLYP level with a larger basis set to refine and calibrate these energy values. The results show that the most stable glycine–Fe+isomer is the C1‐symmetric sextet NO‐16, in which Fe+is interacted with both the amino nitrogen and carbonyl oxygen of the glycine ligand. The ground‐state structure of glycine–Fe2+is the 5A′′ state 2O‐25, which generates from the interaction of Fe2+with the two oxygen terminus of the zwitterionic glycine. The calculations indicate that the binding energies mainly derive from the contributions of electrostatic effects, for both the monovalent and divalent metal cation‐chelated glycine complexes. The differences in binding energies between these different multiple‐state glycine–Fe+isomers with same combination modes mainly derive from their different electrostatic and polarized effects, and those between the isomers of different combination modes with the same multiple states mainly stem from their different deformation effects. The differences in the relative stabilities of these glycine–Fe+isomers with different multiple states mainly come from the fact that the more electrostatic contribution of the lower spin complex cannot compensate for the loss of energy enhancement of its corresponding metal cation relative to that of the higher spin counterpart. The same is true for the glycine–Fe2+complexes. Copyright © 2004 John Wiley & Sons, Ltd.

Published
2005
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35. Self-Assembly of a Semiconductive and Photoactive Heterobimetallic Metal-Organic Capsule.

Author

Hu X, Han M, Shao L, Zhang C, Zhang L, Kelley SP, Zhang C, Lin J, Dalgarno SJ, Atwood DA, Feng S, and Atwood JL

Abstract

We report the synthesis of a novel metal-organic capsule constructed from six pyrogallol[4]arene macrocycles, which are switched together by 16 Fe III and 16 Co II ions. This supramolecular structure is the first instance of a spheroidal heterometallic nanocage assembled through a one-step metal-ligand coordination approach. This new assembly also demonstrates an important proof of concept through the formation of multiple heterometallic metal-metal interactions within the capsule framework. Photophysical and electrochemical studies of self-assembled capsule films indicate their potential as semiconductors. These materials display unexpected photoelectric conversion properties, thus representing an emergent phenomenon in discrete metal-organic supramolecular assemblies., (© 2021 Wiley-VCH GmbH.)

Published
2021
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36. Biomimetic Self-Assembly of Co II -Seamed Hexameric Metal-Organic Nanocapsules.

Author

Hu X, Chai J, Zhang C, Lang J, Kelley SP, Feng S, Liu B, Atwood DA, and Atwood JL

Subjects
Models, Molecular, Molecular Structure, Biomimetics, Chemical Engineering, Cobalt chemistry, Metal Nanoparticles chemistry
Abstract

A Co II 18 L 6 hexameric metal-organic nanocapsule (MONC) has been prepared and characterized using biomimetic self-assembly as the synthetic methodology. Akin to the biological behavior of zinc-finger proteins' release, uptake, and electrophilic substitution of Zn 2+ ions, the assembly of this novel MONC has been accomplished by employing three sequential processes: assembly of the framework, metal ion insertion, and metal exchange, resulting in the formation of the Co II 18 L 6 hexameric MONC. In this work, inspired by the biological behavior of metalloproteins, rational control of multiple complex supramolecular self-assembly has been achieved.

Published
2019
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