Tribological Characteristics of Diamond-Like Carbon Based Double-Layers Film Prepared by DC-Plasma Ionization Deposition

Mano, Tsuyoshsi; Tanaka, Shogo; Takagi, Makoto; Sugiyama, Osamau; Takai, O.

doi:10.2494/photopolymer.24.463

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…11 For the PLCH training set, 0.5 M HCl was the electrolyte, producing only carbon samples that contained more than 35 at.% of hydrogen. 11 The testing dataset for HOPG, GC, 30,44,52-74 and DLC 15,[30][31][32][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89][90][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 À1 from the literature data.…”

Section: Samplesmentioning

confidence: 99%

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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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Section: Samplesmentioning

confidence: 99%

“…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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Analysis Services of Thin Films in Shizuoka Prefectural Industrial Research Institute

Mano¹,

Takagi²,

Tanaka³

2013

Journal of The Surface Finishing Society of Japan

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Tribological Characteristics of Diamond-Like Carbon Based Double-Layers Film Prepared by DC-Plasma Ionization Deposition

Cited by 2 publications

References 14 publications

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

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

Analysis Services of Thin Films in Shizuoka Prefectural Industrial Research Institute

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Tribological Characteristics of Diamond-Like Carbon Based Double-Layers Film Prepared by DC-Plasma Ionization Deposition

Cited by 2 publications

References 14 publications

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

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

Analysis Services of Thin Films in Shizuoka Prefectural Industrial Research Institute

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Product

Resources

About

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

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