Applications of Raman spectroscopy in two-dimensional materials

Yin, Pengkun; Lin, Qingyu

doi:10.1142/s1793545820300104

Cited by 15 publications

(4 citation statements)

References 178 publications

(191 reference statements)

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“…This sample also exhibits significant defects, which are likely the consequences of the amorphous structure. The presence of the 2D peak at ∼2780 cm -1 further characterizes that the sample has two-dimensional characteristics, closely featuring a graphenic phase [26][27][28].…”

Section: Resultsmentioning

confidence: 92%

N-Doped rGO-Like Carbon Prepared from Coconut Shell: Structure and Specific Capacitance

Khambali¹,

Priyanto²,

Asih³

et al. 2023

Journal of Renewable Materials

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An rGO−like carbon compound has been synthesized from biomass, i.e., old coconut shell, by a carbonization process followed by heating at 400°C for 5 h. The nitrogen doping was achieved by adding the urea (CH 4 N 2 O) and stirring at 70°C for 14 h. The morphology and structure of the rGO-like carbon were investigated by electron microscopies and Raman spectroscopy. The presence of C-N functional groups was analyzed by Fourier transform infrared and synchrotron X-ray photoemission spectroscopy, while the particle and the specific capacitance were measured by particle sizer and cyclic voltammetry. The highest specific capacitance of 72.78 F/g is achieved by the sample with 20% urea, having the smallest particles size and the largest surface area. The corresponding sample has shown to be constituted by the appropriate amount of C-N pyrrolic and pyridinic defects.

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Section: Resultsmentioning

confidence: 92%

N-Doped rGO-Like Carbon Prepared from Coconut Shell: Structure and Specific Capacitance

Khambali¹,

Priyanto²,

Asih³

et al. 2023

Journal of Renewable Materials

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“…This has resulted in surface-enhanced Raman spectroscopy (SERS) being developed for a wide range of applications across different fields. This method detects samples in solution and improves selectivity and sensitivity by utilising noble metal nanomaterials to boost the low-concentration single-molecule Raman signal by many orders of magnitude (usually 10 7 to 10 14 ) ( Yin et al, 2020 ). Cowcher et al used SERS to mix silver nanoparticle colloids with bacterial solutions to quickly detect Bacillus and other infections.…”

Section: Detection Of Nanomaterials In Foodmentioning

confidence: 99%

The safety of nanomaterials in food production and packaging

Onyeaka

Passaretti

Miri

et al. 2022

Current Research in Food Science

128

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“…The physical, chemical, thermal, and electrical properties of 2D nanomaterials with different atomic layer thicknesses vary greatly, making both single-layer and few-layer 2D nanomaterials highly valuable for research, and the optical properties of 2D nanomaterials with different layer numbers often vary greatly [2]. Traditionally, the number of layers 2 of 17 in 2D nanomaterials is often measured using optical microscopy (OM) [20], atomic force microscopy (AFM) [21], or by optical methods such as Raman spectroscopy [22]. However, these methods require a large number of researchers and are not only inefficient but also wasteful of resources.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Layer Identification of 2D Nanomaterials

et al. 2022

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Two-dimensional (2D) nanomaterials exhibit unique properties due to their low dimensionality, which has led to great potential for applications in biopharmaceuticals, aerospace, energy storage, mobile communications and other fields. Today, 2D nanomaterials are often prepared and exfoliated by a combination of mechanical and manual methods, which makes the production of 2D nanomaterials inefficient and prevents standardized and industrialized manufacturing. Recent breakthroughs in semantic segmentation techniques based on deep learning have enabled the accurate identification and segmentation of atomic layers of 2D nanomaterials using optical microscopy. In this study, we analyzed in detail sixteen semantic segmentation models that perform well on public datasets and apply them to the layer identification and segmentation of graphene and molybdenum disulfide. Furthermore, we improved the U2-Net† model to obtain the best overall performance, namely 2DU2-Net†. The accuracy of the 2DU2-Net† model was 99.03%, the kappa coefficient was 95.72%, the dice coefficient was 96.97%, and the average cross–merge ratio was 94.18%. Meanwhile, it also had good performance in terms of computation, number of parameters, inference speed and generalization ability. The results show that deep learning-based semantic segmentation methods can greatly improve efficiency and replace most manual operations, and different types of semantic segmentation methods can be adapted to different properties of 2D nanomaterials, thus promoting the research and application of 2D nanomaterials.

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Applications of Raman spectroscopy in two-dimensional materials

Cited by 15 publications

References 178 publications

N-Doped rGO-Like Carbon Prepared from Coconut Shell: Structure and Specific Capacitance

N-Doped rGO-Like Carbon Prepared from Coconut Shell: Structure and Specific Capacitance

The safety of nanomaterials in food production and packaging

Deep Learning-Based Layer Identification of 2D Nanomaterials

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