Algorithm-improved high-speed and non-invasive confocal Raman imaging of 2D materials

作     者：Sachin Nair Jun Gao Qirong Yao Michael H.G.Duits Cees Otto Frieder Mugele Sachin Nair;Jun Gao;Qirong Yao;Michael H.G.Duits;Cees Otto;Frieder Mugele

作者机构：Physics of Complex Fluids MESA+Institute for NanotechnologyUniversity of Twente Physics of Interfaces and NanomaterialsMESA+ Institute for NanotechnologyUniversity of Twente Medical Cell Bio Physics Tech Med Centre University of Twente 

出 版 物：《National Science Review》 (国家科学评论(英文版))

年 卷 期：2020年第7卷第3期

页      面：620-628页

核心收录：

学科分类：07[理学] 08[工学] 080501[工学-材料物理与化学] 070302[理学-分析化学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 

基　　金：part of the research program Rock-on-a-Chip with project number i40 which is partly financed by the Netherlands Organisation for Scientific Research (NWO) and through the Exploratory Research (Explo Re) program of BP plc 

主　　题：2D materials graphene graphene oxide confocal Raman microscopy algorithm 

摘      要：Confocal Raman microscopy is important for characterizing 2 D 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 2 D materials. Meanwhile, GO can be imaged at a laser dose that is two to three 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 3 D 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.