Ion bombardment of glassy carbon

Malherbe, J.B.; Odutemowo, O.S.; Njoroge, E.G.; Langa, D.F.; Hlatshwayo, Thulani Thokozani; Theron, C.C.

doi:10.1016/j.vacuum.2017.11.006

Cited by 24 publications

(16 citation statements)

References 23 publications

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“…The energy in channel number in the RBS Se profile was converted into depth using Ziegler, Biersack and Littmark (ZBL) stopping powers [14], and density of SiC (3.21 g cm -3 ). Se profiles in the depth scale were fitted to a Gaussian function to obtain the range straggling (ΔRp) and projected ranges (Rp) [15]. The surface morphology before and after each annealing cycle was monitored by field emission scanning electron microscopy (FEG-SEM) using a Zeiss Ultra 55 with an in-lens detector at 2 kV.…”

Section: Methodsmentioning

confidence: 99%

Isothermal annealing of selenium (Se)-implanted silicon carbide: Structural evolution and migration behavior of implanted Se

Abdalla

Njoroge

Mlambo

et al. 2022

Materials Chemistry and Physics

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Section: Methodsmentioning

confidence: 99%

Isothermal annealing of selenium (Se)-implanted silicon carbide: Structural evolution and migration behavior of implanted Se

Abdalla

Njoroge

Mlambo

et al. 2022

Materials Chemistry and Physics

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“…The testing dataset for HOPG, 30–51 GC, 30,44,52–74 and DLC 15,30–32,75–91 were obtained from the literature. A web tool (WebPlotDigitizer) was used to extract the data points from the Raman spectra in the interval between 1050 and 1800 cm −1 from the literature data 92…”

Section: Experimental Partmentioning

confidence: 99%

A machine learning approach for the automated classification of bulk sp² and sp³ carbon materials

daFonseca,

Thind,

Booth

et al. 2023

J Raman Spectroscopy

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Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to classify different types of carbon material based on their Raman spectra. The selected reference materials were highly oriented pyrolytic graphite (HOPG), diamond‐like carbon (DLC), glassy carbon (GC), hydrogenated graphite‐like carbon (GLCH), and hydrogenated polymer‐like carbon (PLCH). These materials vary in crystallinity, predominant carbon hybridization, and hydrogen content. The training dataset was Raman spectra collected from commercial samples (HOPG, DLC, GC) and samples synthesized in our laboratory (GLCH, PLCH). The Raman spectra were collected using 532 nm laser excitation. The classification model revealed that the first principal component (PC1) was the determinant source of information to separate the crystalline from the amorphous carbon samples. PC2 allowed the separation of amorphous material with different levels of hybridization (sp2 and sp3). Finally, both PC2 and PC3 contributed to separate materials with different levels of hydrogenation. The classification model was tested using a library of Raman spectra of carbon materials reported in the literature, and the results showed a high accuracy prediction (97%). The model presented here provides an avenue for automated classification of carbon materials using Raman spectroscopy and machine learning.

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“…It also has a few proposed applications in nuclear reactor technology, viz. as a protective layer on the graphite reactor core surface in molten salt breeder reactors [9] and nuclear waste storage for encapsulation of nuclear fuel elements [10,11] and recently as a new container material for the long-term storage of high-level nuclear waste [12]. Two key factors for a material to be used in the nuclear industry are radiation hardness and diffusion properties.…”

Section: Introductionmentioning

confidence: 99%

“…Two key factors for a material to be used in the nuclear industry are radiation hardness and diffusion properties. The radiation resistance of glassy carbon is largely unexplored with some Raman studies, see [12][13][14][15][16][17][18][19] and also with thermal conductivity measurements [20]. A key property for a nuclear waste storage material is low diffusivity for the radioactive materials.…”

Section: Introductionmentioning

confidence: 99%

Diffusion of strontium implanted in glassy carbon

et al. 2021

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Glassy carbon has properties making it attractive as a containment material for radioactive waste. In this study, the diffusivity of the radiological important fission product, strontium, is measured. Two hundred kiloelectronvolt strontium ions were implanted at room temperature. The implanted samples were either annealed isochronally for 1 h up to 900°C or by increasing the temperature linearly up to 648°C. The Sr profiles were determined by Rutherford backscattering spectrometry (RBS). The isochronally annealed samples showed Fickian diffusion at 300°C ( D = 6.7 × 10 −19 m 2 s −1 ) with segregation occurring at the higher temperatures. Raman spectroscopy indicated a link between damage annealing and the onset of segregation. During increasing temperature, the system is dynamic with diffusion taking place. An equation to determine diffusion coefficients for ramped heating was derived. Fickian diffusion occurred with an activation energy of 1.97 eV for 414–454°C, and from 458 to 558°C, it was 0.184 eV. At higher temperatures, a segregation of the strontium towards the surface was observed. A calculation of the heat of segregation of Sr in carbon supports this segregation. The results show that the in situ RBS measurements with a linear increase in temperature give additional information compared with the conventional isochronal annealing.

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Ion bombardment of glassy carbon

Cited by 24 publications

References 23 publications

Isothermal annealing of selenium (Se)-implanted silicon carbide: Structural evolution and migration behavior of implanted Se

Isothermal annealing of selenium (Se)-implanted silicon carbide: Structural evolution and migration behavior of implanted Se

A machine learning approach for the automated classification of bulk sp² and sp³ carbon materials

Diffusion of strontium implanted in glassy carbon

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Ion bombardment of glassy carbon

Cited by 24 publications

References 23 publications

Isothermal annealing of selenium (Se)-implanted silicon carbide: Structural evolution and migration behavior of implanted Se

Isothermal annealing of selenium (Se)-implanted silicon carbide: Structural evolution and migration behavior of implanted Se

A machine learning approach for the automated classification of bulk sp2 and sp3 carbon materials

Diffusion of strontium implanted in glassy carbon

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Product

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A machine learning approach for the automated classification of bulk sp² and sp³ carbon materials