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Unraveling grain growth of metallic tungsten: Investigating the nanoscale realm of hydrogen reduction of tungsten oxides
被引:2
作者:
Estupinan-Donoso, Alvaro A.
[1
]
Gravier, Pauline
[2
]
Ichikawa, Rodrigo U.
[3
]
Michels, Andreas
[1
]
机构:
[1] Univ Luxembourg, Dept Phys & Mat Sci, 162a Ave De La Faiencerie, L-1511 Esch Sur Alzette, Luxembourg
[2] ESRF European Synchrotron Radiat Facil, 71 Ave des Martyrs,CS 40220, F-38043 Grenoble 9, France
[3] Nucl & Energy Res Inst IPEN CNEN, Mat Sci & Technol Ctr CCTM, Ave Lineu Prestes,2242 Cidade Univ, BR-05508000 Sao Paulo, SP, Brazil
关键词:
Tungsten trioxide reduction;
Phase contrast imaging;
In-situ reaction;
Grain size distribution;
Grain growth;
POWDER;
D O I:
10.1016/j.powtec.2024.119672
中图分类号:
TQ [化学工业];
学科分类号:
0817 ;
摘要:
This paper focuses on the experimental observation of tungsten grain evolution during the process of hydrogen reduction of tungsten trioxide. The objective is to gain insights into the grain size distribution (GSD) evolution and morphology changes of metallic tungsten. The study employs an amalgamation of conventional characterization techniques - such as TGA, SEM -EDS, TEM and XRD - alongside cutting-edge techniques like phase contrast nano-tomography from synchrotron sources. Conducted as a quasi in-situ study, this research offers the opportunity to observe the transient evolution of the formed metallic tungsten phase within its precursor WO2 particles. The paper presents and discusses quantitative results concerning the evolution of GSD and morphological changes, encompassing growth rates of both crystallites and grains of the synthesized metallic phase. Additionally, the quasi in-situ study highlights the dependence of grain size on water concentration during the reduction process. The qualitative and quantitative findings contribute to a more nuanced understanding of the kinetics of grain growth of metallic tungsten and offer insights into the intricate interplay between reduction parameters, GSD evolution and morphological changes. Gaining a deeper understanding of the underlying mechanisms driving the changes in GSD establishes a foundation for predictive theory with immediate academic and industrial impact.
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