Investigating the effect of heat treatment on the diffusion behaviour of xenon implanted in glassy carbon

Ismail, M.Y.A.; Malherbe, J.B.; Odutemowo, O.S.; Njoroge, E.G.; Hlatshwayo, Thulani Thokozani; Mlambo, Mbuso; Wendler, E.

doi:10.1016/j.vacuum.2017.12.021

Cited by 13 publications

(12 citation statements)

References 9 publications

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“…3(d), the peaks at about 1341 cm −1 and 1589 cm −1 responsible for the D and G bands, respectively, for the carbon originate from the vibrations of the sp 2 and sp 3 bonds of graphite. 32 The peaks corresponding to both MoSe 2 and WSe 2 are present as shown in the selected area plot, which confirm the heterogeneous deposition on the CC substrate. XPS analysis has been employed to probe the electronic state and binding energy of the heterostructure.…”

Section: Resultsmentioning

confidence: 61%

Synergistic 2D MoSe₂@WSe₂ nanohybrid heterostructure toward superior hydrogen evolution and flexible supercapacitor

et al. 2022

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Section: Resultsmentioning

confidence: 61%

Synergistic 2D MoSe₂@WSe₂ nanohybrid heterostructure toward superior hydrogen evolution and flexible supercapacitor

et al. 2022

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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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“…A key property for a nuclear waste storage material is low diffusivity for the radioactive materials. Although there had been a few reports of diffusion of fission product elements in glassy carbon [17][18][19][21][22][23][24][25][26], this is the prime reason for this study, i.e. to determine the diffusivity of the radiological important fission product, strontium, in glassy carbon.…”

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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Investigating the effect of heat treatment on the diffusion behaviour of xenon implanted in glassy carbon

Cited by 13 publications

References 9 publications

Synergistic 2D MoSe₂@WSe₂ nanohybrid heterostructure toward superior hydrogen evolution and flexible supercapacitor

Synergistic 2D MoSe₂@WSe₂ nanohybrid heterostructure toward superior hydrogen evolution and flexible supercapacitor

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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Investigating the effect of heat treatment on the diffusion behaviour of xenon implanted in glassy carbon

Cited by 13 publications

References 9 publications

Synergistic 2D MoSe2@WSe2 nanohybrid heterostructure toward superior hydrogen evolution and flexible supercapacitor

Synergistic 2D MoSe2@WSe2 nanohybrid heterostructure toward superior hydrogen evolution and flexible supercapacitor

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

Resources

About

Synergistic 2D MoSe₂@WSe₂ nanohybrid heterostructure toward superior hydrogen evolution and flexible supercapacitor

Synergistic 2D MoSe₂@WSe₂ nanohybrid heterostructure toward superior hydrogen evolution and flexible supercapacitor

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