Mechanochemistry approach to produce in-situ tungsten borides and carbides nanopowders: Experimental study and modeling

Bahrami-Karkevandi, Mojtaba and Nasiri-Tabrizi, Bahman and Wong, K.Y. and Ebrahimi-Kahrizsangi, Reza and Fallahpour, Alireza and Saber-Samandari, Saeed and Baradaran, Saeid and Basirun, Wan Jefrey (2019) Mechanochemistry approach to produce in-situ tungsten borides and carbides nanopowders: Experimental study and modeling. Materials Chemistry and Physics, 224. pp. 47-64. ISSN 0254-0584

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Official URL: https://doi.org/10.1016/j.matchemphys.2018.12.003

Abstract

Mechanically-induced self-sustaining reactions (MSRs) in WO3–B2O3–Mg–C powder mixtures were investigated in terms of reductant content. Also, two different predictive intelligent-based techniques including Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) were developed to estimate the structural features of the mechanosynthesized powders, where different statistical analysis were performed to prove the precision and robustness of proposed models. The phase compositions were changed as the concentration of ductile reductant (Mg) reduced in the system. Accordingly, the fraction of crystalline phases was dramatically altered after the leaching process. The structural assessment showed that the dislocation density significantly varied as the graphite content increased, however, the rate of these alterations was not linear. FESEM observations indicated that the leached product had a typical flower-like cluster configuration, which consisted of loosely organized nano-sheets with a side length and thickness of around 250 and 12 nm, respectively. Meanwhile, the results achieved from the intelligent-based techniques showed that both ANFIS and ANN are very powerful in estimating the structural characteristics of the mechanosynthesized powders. However, ANFIS was more accurate than MLP-ANN.

Item Type: Article
Uncontrolled Keywords: Mechanochemistry; Tungsten borides and carbides; Nanoflowers; MSRs; Predictive intelligent-based techniques
Subjects: Q Science > Q Science (General)
Q Science > QD Chemistry
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Science > Dept of Chemistry
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 23 Jan 2019 02:16
Last Modified: 23 Jan 2019 02:16
URI: http://eprints.um.edu.my/id/eprint/20108

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