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Author:

Guo, Yuliang (Guo, Yuliang.) | Sun, Xiaobo (Sun, Xiaobo.) | Jiao, Handong (Jiao, Handong.) | Zhang, Liwen (Zhang, Liwen.) | Qin, Wenxuan (Qin, Wenxuan.) | Xi, Xiaoli (Xi, Xiaoli.) | Nie, Zuoren (Nie, Zuoren.)

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EI Scopus SCIE

Abstract:

Tungsten coatings have unique properties such as high melting points and hardness and are widely used in the nuclear fusion and aviation fields. In experiments, compared to pure Na2WO4 molten salt, electrolysis with Na2WO4-WO3 molten salt results in a lower deposition voltage. Herein, an investigation combining experimental and computational approaches was conducted, involving molecular dynamics simulations with deep learning, high-temperature in situ Raman spectroscopy and activation strain model analysis. The results indicated that the molten salt system's behaviour, influenced by migration and polarization effects, led to increased formation of Na2W2O7 in the Na2WO4-WO3 molten salt, which has a lower decomposition voltage and subsequently accelerated the cathodic deposition of tungsten. We analyzed the mechanism of the effect of the electric field on the Na2W2O7 structure based on the bond strength and electron density. This research provides crucial theoretical support for the effect of electric field on tungsten in molten salt and demonstrates the feasibility of using machine learning-based DPMD methods in simulating tungsten-containing molten salt systems. © 2024 The Royal Society of Chemistry.

Keyword:

Sodium compounds Molecular dynamics Chemical activation Deep learning Deposition Fused salts Computational chemistry Tungsten compounds

Author Community:

  • [ 1 ] [Guo, Yuliang]Collaborative Innovation Center of Capital Resource-Recycling Material Technology, College of Materials Science and Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Sun, Xiaobo]Collaborative Innovation Center of Capital Resource-Recycling Material Technology, College of Materials Science and Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Sun, Xiaobo]National Engineering Laboratory for Industrial Big-data Application Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Jiao, Handong]Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing; 100081, China
  • [ 5 ] [Zhang, Liwen]Collaborative Innovation Center of Capital Resource-Recycling Material Technology, College of Materials Science and Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 6 ] [Zhang, Liwen]National Engineering Laboratory for Industrial Big-data Application Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 7 ] [Qin, Wenxuan]School of Chemistry and Materials Engineering, Xinxiang University, Henan, Xinxiang; 453003, China
  • [ 8 ] [Xi, Xiaoli]Collaborative Innovation Center of Capital Resource-Recycling Material Technology, College of Materials Science and Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 9 ] [Xi, Xiaoli]Key Laboratory of Advanced Functional Materials, Ministry of Education, Beijing University of Technology, Beijing; 100124, China
  • [ 10 ] [Nie, Zuoren]Collaborative Innovation Center of Capital Resource-Recycling Material Technology, College of Materials Science and Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 11 ] [Nie, Zuoren]National Engineering Laboratory for Industrial Big-data Application Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 12 ] [Nie, Zuoren]Key Laboratory of Advanced Functional Materials, Ministry of Education, Beijing University of Technology, Beijing; 100124, China

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Source :

Physical Chemistry Chemical Physics

ISSN: 1463-9076

Year: 2024

Issue: 8

Volume: 26

Page: 6590-6599

3 . 3 0 0

JCR@2022

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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