Algorithm-improved high speed and non-invasive confocal Raman imaging of two-dimensional materials

Sachin Nair, Jun Gao*, Qirong Yao, Michael H.G. Duits, Cees Otto, Frieder Mugele*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
23 Downloads (Pure)

Abstract

Confocal Raman microscopy is important for characterizing two dimensional (2D) materials, but its low throughput significantly hinders its applications. For metastable materials such as graphene oxide (GO), the low throughput is aggravated by the requirement of extremely low laser dose to avoid sample damage. Here we introduce algorithm-improved Confocal Raman Microscopy (ai-CRM), which increases the Raman scanning rate by one to two orders of magnitude with respect to state-of-the-art works for a variety of 2D materials. Meanwhile, GO can be imaged at a laser dose that is 2 to 3 orders of magnitude lower than previously reported, such that laser-induced variations of the material properties can be avoided. ai-CRM also enables fast and spatially resolved quantitative analysis, and is readily extended to three-dimensional mapping of composite materials. Since ai-CRM is based on general mathematical principles, it is cost-effective, facile-to-implement and universally applicable to other hyperspectral imaging methods.
Original languageEnglish
Pages (from-to)620-628
JournalNational Science Review
Volume7
Issue number3
Early online date13 Nov 2019
DOIs
Publication statusPublished - Mar 2020

Keywords

  • 2D materials
  • Graphene
  • Graphene oxide
  • Confocal Raman microscopy
  • Algorithm

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