Volume combustion synthesis of B4C–SiC nanocomposites in tubular and spark plasma furnaces

Roghani, Hamed; Tayebifard, Seyed Ali; Kasraee, Kian; Asl, Mehdi Shahedi

doi:10.1016/j.ceramint.2020.08.060

Cited by 20 publications

(7 citation statements)

References 85 publications

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“…SEM uses physical signals, such as secondary electron and backscattered electron imaging, to observe the surface morphology and composition of the sample and the structure of the cleavage surface. [4,5] SEM sample is easy to make without slicing [4] and can be rotated in 3D space, which is conducive to multiangle observation. [5] However, due to the charge effect and the irregular deflection of the electron beam caused by the electrostatic field, the uneven brightness and darkness of the image, image deformity and image drift will be caused.…”

Section: Imaging Instrumentsmentioning

confidence: 99%

“…Among them, SEM fine electron beam imaging can obtain the surface morphology and composition information of samples. [4,5] The high-energy electron beam of TEM can even get the crystal structure and defects through the sample. [6][7][8][9] In recent years, the latest development of high-end imaging characterization technologies, such as AFM and STEM, makes it possible to observe the microscopic evolution of materials with atomic spatial resolution and subsecond time resolution, [5,[10][11][12] which makes it possible to directly observe the atomic-level dynamic phenomena, including defect evolution, dislocation migration, phase transformation, etc., And accumulated a lot of data.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, SEM fine electron beam imaging can obtain the surface morphology and composition information of samples. [ 4,5 ] The high‐energy electron beam of TEM can even get the crystal structure and defects through the sample. [ 6–9 ]…”

Section: Introductionmentioning

confidence: 99%

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Computer Vision Analysis on Material Characterization Images

Cheng

Sha

Zuo

et al. 2021

Advanced Intelligent Systems

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Material characterization has been proved to be the most intuitive approach to understand the chemical composition, structure, and microstructure of materials, which is the basis of material design. One of the most important steps in material design is to extract the characteristics from an image, and find their associations with the material structure and properties. Therefore, in recent years, with the rapid development of machine vision algorithms, characterization images have attracted attention in the field of material characterization. Researchers use computer vision algorithms, such as image denoising and enhancement, to preprocess the representation image, image segmentation and classification to detect and separate each microstructure from the characterization image, and quantitatively analyze the properties of materials. Herein, the application of computer vision algorithms in material image representation is summarized and discussed. The latest and valuable views for experts and scholars in both computer vision and material grounds are presented. Thus, this review provides guidance for material exploration and promotes the developments of artificial intelligence in the field of materials.

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Section: Imaging Instrumentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computer Vision Analysis on Material Characterization Images

Cheng

Sha

Zuo

et al. 2021

Advanced Intelligent Systems

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“…Nevertheless, the commonly used pre‐synthesis methods, such as carbothermal reduction, molten salt synthesis, and sol‐gel, are complicated and with high cost, and can produce few low‐dimensional phases. Combustion synthesis is a fast and energy‐saving method for the fabrication of micro/nanosized powders 40–43 …”

Section: Introductionmentioning

confidence: 99%

Nanoscale toughening of low carbon Al₂O₃‐C refractories by means of SiC_nw/Al₂O₃ composite reinforcement

et al. 2023

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To improve the thermal shock resistance of low carbon Al2O3‐C refractories, SiC nanowires (SiCnw) containing SiCnw/Al2O3 composite reinforcement were introduced. The specific fracture energy of the Al2O3‐C refractory matrix was obtained by statistical grid nano‐indentation. The reinforcement mechanism of SiCnw/Al2O3 on thermal shock resistance of refractories was investigated. The results revealed that the matrix‐specific fracture energy of A6 (6 wt% SiCnw/Al2O3 added) was 217 N/m, which was 58.4% higher than reference sample A0 (137 N/m) and 18.6% higher than MA6 (183 N/m, 6 wt% SiC/Al2O3 added). A6 showed the highest residual strength ratio of 49.8%, which was 114.7 % higher than A0 (23.2%) and 82.4 % higher than MA6 (27.3%). The components with different morphology in SiCnw/Al2O3 cluster, especially SiC nanowires, promote the generation of microcracks, crack multi‐deflection, and branching, which toughen the matrix and improve the thermal shock resistance of refractories. In comparison to the literature, A6 showed a higher rising in residual strength ratio than those with higher graphite content (4 wt% and 20 wt%), which will greatly reduce the consumption of carbon‐containing refractories and contribute to the reduction of CO2 emission.

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“…The help of mechanical and chemical methods to encourage raw materials to be synthesized at lower temperatures is one of the investigations of researchers to increase the process efficiency. The use of high-energy and fast agitators for the preparation of raw materials [28,29] as well as the stimulants such as microwave energy are among these efforts [30,31]. Attempts have been made to employ materials with high reactivity and high combustion heat such as magnesium to provide the energy needed for synthesis and to reduce the process temperature, which have led to the invention of the magnesiothermal method for the synthesis of B4C [32,33].…”

Section: Introductionmentioning

confidence: 99%

Performance of glucose, sucrose and cellulose as carbonaceous precursors for the synthesis of B4C powders

et al. 2022

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Boron carbide is the third hardest material in the world after diamond and cubic boron nitride, which is one of the most strategic engineering ceramics in various industrial applications. The aim of this research is to synthesize B4C by reacting boric acid as boron source with polymers from the saccharide family as carbon sources, and to determine the best saccharide as precursor. For this purpose, glucose (monosaccharide), sucrose (disaccharide), and cellulose (polysaccharide) were used and examined. The samples were prepared by appropriate mixing of the starting materials, pyrolysis at 700 °C, and synthesis at 1500 °C. The results of Fourier transform infrared (FT-IR) spectroscopy and X-ray diffractometry (XRD) showed that among the studied saccharide polymers, glucose is the best carbon source candidate for the synthesis of B4C. To describe precisely, the specimen prepared with glucose and boric acid had more boron carbide and less hydrocarbon.

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Volume combustion synthesis of B4C–SiC nanocomposites in tubular and spark plasma furnaces

Cited by 20 publications

References 85 publications

Computer Vision Analysis on Material Characterization Images

Computer Vision Analysis on Material Characterization Images

Nanoscale toughening of low carbon Al₂O₃‐C refractories by means of SiC_nw/Al₂O₃ composite reinforcement

Performance of glucose, sucrose and cellulose as carbonaceous precursors for the synthesis of B4C powders

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Volume combustion synthesis of B4C–SiC nanocomposites in tubular and spark plasma furnaces

Cited by 20 publications

References 85 publications

Computer Vision Analysis on Material Characterization Images

Computer Vision Analysis on Material Characterization Images

Nanoscale toughening of low carbon Al2O3‐C refractories by means of SiCnw/Al2O3 composite reinforcement

Performance of glucose, sucrose and cellulose as carbonaceous precursors for the synthesis of B4C powders

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Product

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Nanoscale toughening of low carbon Al₂O₃‐C refractories by means of SiC_nw/Al₂O₃ composite reinforcement