@inproceedings{03cee99876744a46963a3f099cb113b1,
title = "Geometric property estimation based on raman spetra measurement using machine learning",
abstract = "There have been many efforts to identify the relation between the geometric structure of carbon-nanomaterials and the intensity profile of Raman spectra. As a result, researchers have reported intensity profiles changing with the geometric properties of carbon-nanomaterials, i.e. the length of carbon nanotubes and the thickness of graphene. Based on these measured data, we constructed an autonomous framework that can deduce the geometric property from a Raman spectra pattern using a machine learning algorithm. In this work, we focus on the Raman peak shift recognition using principal component analysis (PCA) to identify the number of graphene layers and this framework can accelerate processes in both measurement and geometric property analysis.",
keywords = "Few-Layer Graphene, Machine Learning, Pattern Recognition, Principal Component Analysis, Raman Spectra",
author = "Jo, {Michael K.} and Umberto Ravaioli",
year = "2019",
month = jan,
day = "8",
doi = "10.1109/NMDC.2018.8605883",
language = "English (US)",
series = "2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018",
address = "United States",
note = "13th IEEE Nanotechnology Materials and Devices Conference, NMDC 2018 ; Conference date: 14-10-2018 Through 17-10-2018",
}