Path towards graphene commercialization from lab to market

Kong, Wei; Kum, Hyun; Bae, Sang Hoon; Shim, Jaewoo; Kim, Hyun‐Seok; Kong, Lingping; Meng, Yuan; Wang, Kejia; Kim, Chan-Soo; Kim, Jeehwan

doi:10.1038/s41565-019-0555-2

Cited by 285 publications

(184 citation statements)

References 159 publications

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“…Here, we report an AI tool to quantify the defects in chemical vapor deposition (CVD) graphene films, by classifying the Raman spectroscopy data. Among many graphene synthesis routes, CVD is chosen in this work due to its high-quality, low-cost, and scalability to mass production for industrial applications [22][23][24]. Furthermore, among many characterization methods, Raman analysis is chosen in this work because it is a popular, versatile, high-throughput, very low invasive, and non-destructive methodology for the characterization of graphene [25][26][27][28].…”

Section: T E Dmentioning

confidence: 99%

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

2020

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No characterization method is available to quickly perform quality inspection of 2D materials produced on an industrial scale. This hinders the adoption of 2D materials for product manufacturing in many industries. Here, we report an artificial-intelligence-assisted Raman analysis to quickly probe the quality of centimeter-large graphene samples in a non-destructive manner. Chemical vapor deposition of graphene is devised in this work such that two types of samples were obtained: layer-plus-islands and layer-by-layer graphene films, at centimeter scales. Using these samples, we implemented and integrated an unsupervised learning algorithm with an automated Raman spectroscopy to precisely cluster 20,250 and 18,000 Raman spectra collected from layer-plus-islands and layer-by-layer graphene films, respectively, into five and two clusters. Each cluster represents graphene patches with different layer numbers and stacking orders. For instance, the two clusters detected in layer-by-layer graphene films represent monolayer and bilayer graphene based on their Raman fingerprints. Our intelligent Raman analysis is fully automated, with no human operation involved, is highly reliable (99.95% accuracy), and can be generalized to other 2D materials, paving the way towards industrialization of 2D materials for various applications in the future.

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Section: T E Dmentioning

confidence: 99%

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

2020

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“…was controlled to locate the chemical potential close to the Dirac point in order to enhance the contribution of the SSs in magneto-transport [16,[18][19][20]. The growth was monitored using in-situ reflective high-energy electron diffraction (RHEED).…”

mentioning

confidence: 99%

Unconventional planar Hall effect in exchange-coupled topological insulator–ferromagnetic insulator heterostructures

et al. 2018

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The Dirac electrons occupying the surface states (SSs) of topological insulators (TIs) have been predicted to exhibit many exciting magneto-transport phenomena. Here we report on the first experimental observation of an unconventional planarHall effect (PHE) and an electrically gate-tunable hysteretic planar magnetoresistance (PMR) in EuS/TI heterostructures, in which EuS is a ferromagnetic insulator (FMI) with an in-plane magnetization. In such exchangecoupled FMI/TI heterostructures, we find a significant (suppressed) PHE when the in-plane magnetic field is parallel (perpendicular) to the electric current. This

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“…Generally, the performance of graphene metasurfaces depends on the intrinsic quality of graphene as well as its transfer techniques to a target substrate. [109][110][111] While mechanically exfoliated graphene may be considered to be ideal, epitaxial growth methods, for example, chemical vapor deposition (CVD), have also drawn a lot of interest due to their capability of large-scale graphene production with reasonable a-e) Reproduced with permission. [51] Copyright 2019, Wiley-VCH.…”

Section: Fabrication Issues and Gating Structures For Graphene Metamamentioning

confidence: 99%

Metamaterials for Enhanced Optical Responses and their Application to Active Control of Terahertz Waves

et al. 2020

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Metamaterials, artificially constructed structures that mimic lattices in natural materials, have made numerous contributions to the development of unconventional optical devices. With an increasing demand for more diverse functionalities, terahertz (THz) metamaterials are also expanding their domain, from the realm of mere passive devices to the broader area where functionalized active THz devices are particularly required. A brief review on THz metamaterials is given with a focus on research conducted in the authors' group. The first part is centered on enhanced THz optical responses from tightly coupled meta‐atom structures, such as high refractive index, enhanced optical activity, anomalous wavelength scaling, large phase retardation, and nondispersive polarization rotation. Next, electrically gated graphene metamaterials are reviewed with an emphasis on the functionalization of enhanced THz optical responses. Finally, the linear frequency conversion of THz waves in a rapidly time‐variant THz metamaterial is briefly discussed in the more general context of spatiotemporal control of light.

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Path towards graphene commercialization from lab to market

Cited by 285 publications

References 159 publications

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

Unconventional planar Hall effect in exchange-coupled topological insulator–ferromagnetic insulator heterostructures

Metamaterials for Enhanced Optical Responses and their Application to Active Control of Terahertz Waves

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