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Novel techniques for characterising graphene nanoplatelets using Raman spectroscopy and machine learning

Vicente Orts Mercadillo1, Happiness Ijije2, Luke Chaplin3, Ian Kinloch1, Mark Bissett1*

1 Henry Royce Institute, National Graphene Institute, and Department of Materials, The University of Manchester, Oxford Road, M13 9PL, UK.

2 Graphene Engineering Innovation Centre, Masdar Building, The University of Manchester, Sackville Street, M1 3BB UK.

3 Centre for Imaging Sciences, Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, M13 9PL, UK.

* Corresponding authors emails: mark.bissett@manchester.ac.uk
DOI10.24435/materialscloud:8q-xh [version v1]

Publication date: Nov 08, 2022

How to cite this record

Vicente Orts Mercadillo, Happiness Ijije, Luke Chaplin, Ian Kinloch, Mark Bissett, Novel techniques for characterising graphene nanoplatelets using Raman spectroscopy and machine learning, Materials Cloud Archive 2022.139 (2022), doi: 10.24435/materialscloud:8q-xh.

Description

A significant challenge for graphene nanoplatelet (GNP) suppliers is the meaningful characterisation of platelet morphology in an industrial environment. This challenge is further exacerbated to platelet surface chemistry when scalable functionalisation processes such as plasma treatment are used to modify the GNPs to improve the filler-matrix interphase in nanocomposites. The costly and complex suite of analytical equipment necessary for a complete material description makes quality control and process optimisation difficult. Raman spectroscopy is a facile and accessible characterisation technique with recent advancements unlocking fast mapping for rapid data collection. In this work we detail several big-data methodologies that extract full value out of Raman spectra so that GNP morphology and surface chemistry can be characterised. A unsupervised peak fitting and processing algorithm was used to extract crystallinity data rapidly and accurately and correlate it with laser-diffraction derived lateral size values for a commercial set of GNPs. Classical machine learning was used to find the most informative Raman features for classifying plasma functionalised GNPs. The initial material properties were found to affect which peak features are most useful for classification. In low-defect density and low specific surface area GNPs, the D peak FWHM is found the most useful, whereas the 2D/G ratio is most useful in the opposite case. Finally, a convolutional neural network was trained to discern between different GNP grades with 86% accuracy. This work demonstrates how computer vision could be deployed for rapid and accurate quality control on the factory floor.

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Keywords

Raman Graphene Nanoplatelets Machine Learning Experimental

Version history:

2022.139 (version v1) [This version] Nov 08, 2022 DOI10.24435/materialscloud:8q-xh