Applications of Raman spectroscopy in graphene‐related materials and the development of parameterized PCA for large‐scale data analysis. (15th September 2017)

• Record Type: Journal Article
• Authors: Campos, João Luiz Elias; Miranda, Hudson; Rabelo, Cassiano; Sandoz‐Rosado, Emil; Pandey, Sugandha; Riikonen, Juha; Cano‐Marquez, Abraham G.; Jorio, Ado
• Other Names: Paillet Matthieu guestEditor.; Parret Romain guestEditor.; Sauvajol Jean‐Louis guestEditor.; Colomban Philippe guestEditor.
• Is Part Of: Journal of Raman spectroscopy. Volume 49:Number 1(2018)
• Page Start: 54
• Page End: 65
• Publication Date: 2017-09-15
• Subjects: graphene -- principal component analysis -- applications -- large‐data analysis -- carbon nanomaterial; Raman spectroscopy -- Periodicals; 535.846
• DOI: 10.1002/jrs.5225
• Languages: English
• ISSNs: 0377-0486

Abstract

Abstract : A methodology for the structural analysis of graphene‐related materials using parameterized principal component analysis (PCA), ideal for large‐scale data treatment, is introduced. First, we review different aspects of Raman spectroscopy for structural and functional characterization of sp 2 ‐bonded carbon materials, which are important for understanding the problem. The parameterized PCA is then introduced and applied to 2 different scenarios: to identify different sp 2 carbon structures and to identify graphene samples with different numbers of layers. Automating these Raman spectroscopy analysis techniques is desired for large‐scale industrial applications. Copyright © 2017 John Wiley & Sons, Ltd. Abstract : Principal component analysis (PCA) is an analytical tool broadly used for automated spectral analysis, with the drawback that the protocol and interpretation depend strongly on sampling data set. Here, we discuss a parameterization procedure for using PCA to analyze large data sets with respect to well‐defined properties. The technique is applied to characterize graphene‐related materials.