Literature DB >> 21207991

High-throughput large-area automated identification and quality control of graphene and few-layer graphene films.

Craig M Nolen1, Giovanni Denina, Desalegne Teweldebrhan, Bir Bhanu, Alexander A Balandin.   

Abstract

Practical applications of graphene require a reliable high-throughput method of graphene identification and quality control, which can be used for large-scale substrates and wafers. We have proposed and experimentally tested a fast and fully automated approach for determining the number of atomic planes in graphene samples. The procedure allows for in situ identification of the borders of the regions with the same number of atomic planes. It is based on an original image processing algorithm, which utilizes micro-Raman calibration, light background subtraction, lighting nonuniformity correction, and the color and grayscale image processing for each pixel. The outcome of the developed procedure is a pseudo color map, which marks the single-layer and few-layer graphene regions on the substrate of any size that can be captured by an optical microscope. Our approach works for various substrates and can be applied to mechanically exfoliated, chemically derived, deposited or epitaxial graphene on an industrial scale.

Entities:  

Year:  2011        PMID: 21207991     DOI: 10.1021/nn102107b

Source DB:  PubMed          Journal:  ACS Nano        ISSN: 1936-0851            Impact factor:   15.881


  9 in total

1.  Graphene quilts for thermal management of high-power GaN transistors.

Authors:  Zhong Yan; Guanxiong Liu; Javed M Khan; Alexander A Balandin
Journal:  Nat Commun       Date:  2012-05-08       Impact factor: 14.919

2.  Highly sensitive transient absorption imaging of graphene and graphene oxide in living cells and circulating blood.

Authors:  Junjie Li; Weixia Zhang; Ting-Fung Chung; Mikhail N Slipchenko; Yong P Chen; Ji-Xin Cheng; Chen Yang
Journal:  Sci Rep       Date:  2015-07-23       Impact factor: 4.379

3.  Quantitative optical mapping of two-dimensional materials.

Authors:  Bjarke S Jessen; Patrick R Whelan; David M A Mackenzie; Birong Luo; Joachim D Thomsen; Lene Gammelgaard; Timothy J Booth; Peter Bøggild
Journal:  Sci Rep       Date:  2018-04-23       Impact factor: 4.379

4.  Autonomous robotic searching and assembly of two-dimensional crystals to build van der Waals superlattices.

Authors:  Satoru Masubuchi; Masataka Morimoto; Sei Morikawa; Momoko Onodera; Yuta Asakawa; Kenji Watanabe; Takashi Taniguchi; Tomoki Machida
Journal:  Nat Commun       Date:  2018-04-12       Impact factor: 14.919

5.  Machine Learning Analysis of Raman Spectra of MoS2.

Authors:  Yu Mao; Ningning Dong; Lei Wang; Xin Chen; Hongqiang Wang; Zixin Wang; Ivan M Kislyakov; Jun Wang
Journal:  Nanomaterials (Basel)       Date:  2020-11-09       Impact factor: 5.076

6.  Selective syntheses of thick and thin nanosheets based on correlation between thickness and lateral-size distribution.

Authors:  Yuri Haraguchi; Hiroaki Imai; Yuya Oaki
Journal:  iScience       Date:  2022-08-24

7.  The selective transfer of patterned graphene.

Authors:  Xu-Dong Chen; Zhi-Bo Liu; Wen-Shuai Jiang; Xiao-Qing Yan; Fei Xing; Peng Wang; Yongsheng Chen; Jian-Guo Tian
Journal:  Sci Rep       Date:  2013-11-14       Impact factor: 4.379

8.  Intelligent Identification of MoS2 Nanostructures with Hyperspectral Imaging by 3D-CNN.

Authors:  Kai-Chun Li; Ming-Yen Lu; Hong Thai Nguyen; Shih-Wei Feng; Sofya B Artemkina; Vladimir E Fedorov; Hsiang-Chen Wang
Journal:  Nanomaterials (Basel)       Date:  2020-06-13       Impact factor: 5.076

9.  Automatic Micro-Robotic Identification and Electrical Characterization of Graphene.

Authors:  Sergio A Garnica B; Marius Knaust; Sergej Fatikow
Journal:  Micromachines (Basel)       Date:  2019-12-11       Impact factor: 2.891

  9 in total

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