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334 results on '"Nie, Fude"'

23. Mesoporous CNT@TiO2-C nanocable with extremely durable high rate capability for lithium-ion battery anodes.

Author

Wang, Bin, Xin, Huolin, Li, Xiaodong, Cheng, Jianli, Yang, Guangcheng, and Nie, Fude

Subjects
Biochemistry and Cell Biology, Other Physical Sciences
Abstract

A well-designed nanostructure CNT@TiO2-C with fine anatase TiO2 particle (< 8 nm), good electronic conducting network (inner CNT core and outer carbon layer), and mesoporous structure was prepared by a simple and green one-pot hydrothermal reaction. The utilization of glucose in the hydrothermal process not only solves the interfacial incompatibility between CNTs and titanate sol and controls the nucleation and growth of TiO2 particles, but also introduces a uniform, glucose-derived, carbon-layer on the TiO2 particles. The nanosized TiO2 particle, high conducting network, and interconnected nanopores of the CNT@TiO2-C nanocable greatly improve its electrochemical performances, especially rate capability. The CNT@TiO2-C nanocables show remarkable rate capability with reversible charge capacity of 297, 240, 210,178 and 127 mAh g(-1) at 1C, 5C, 10C, 20C and 50C, respectively, as well as excellent high rate cycling stability with capacity retention of 87% after 2000 cycles at 50C.

Published
2014

26. High-throughput design of energetic molecules.

Author

Liu, Jian, Zhao, Shicao, Duan, Bowen, He, Xudong, Yang, Chunming, Pu, Xuemei, Zhang, Xinben, Xiao, Yonghao, Nie, Fude, Qian, Wen, Li, Geng, and Zhang, Chaoyang

Abstract

High-throughput design offers a promising way to expedite the de novo design of novel energetic molecules, but achieving this goal necessitates accurate methods for property prediction and efficient schemes for molecular screening. Two approaches for generating energetic molecules are proposed, based on a generative model and a fragment docking scheme, respectively. A high-throughput computation (HTC) workflow based on quantum chemistry is developed for energetic molecule design. Machine learning models are established for predicting crystal density, enthalpy of formation, R–NO2 bond dissociation energy, detonation velocity, detonation pressure, detonation heat, detonation volume and detonation temperature, yielding coefficients of determination (R2) of 0.928, 0.948, 0.984, 0.989, 0.986, 0.986, 0.990 and 0.995, respectively. Thereby, an easy-to-use platform named Energetic Materials Studio (EM-Studio) integrates all the methods and models. Therein, five modules, EM-Generator, EM-QC, EM-DB, EM-ML and EM-Visualizer, work for molecule generation, HTC-aided molecule design, data management, machine learning prediction, and human–computer interaction, respectively. The effectiveness and capabilities of EM-Studio in HTC- and AI-aided molecular design are demonstrated through two cases of fused-ring energetic molecules. [ABSTRACT FROM AUTHOR]

Published
2023
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