| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Bissett, Mark A.
University of Manchester
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (20/20 displayed)
- 2024Synergistic Improvement in the Thermal Conductivity of Hybrid Boron Nitride Nanotube/Nanosheet Epoxy Compositescitations
- 2024High-performance hybrid glass fibre epoxy composites reinforced with amine functionalised graphene oxide for structural applicationscitations
- 2024Structural Health Monitoring of Scarf Bonded Repaired Glass/Epoxy Laminates Interleaved with Carbon Non-woven Veilcitations
- 2023Tribology of Copper Metal Matrix Composites Reinforced with Fluorinated Graphene Oxide Nanosheets: Implications for Solid Lubricants in Mechanical Switchescitations
- 2023Novel techniques for characterising graphene nanoplatelets using Raman spectroscopy and machine learningcitations
- 2022Joule heating and mechanical properties of epoxy/graphene based aerogel compositecitations
- 2021Effect of graphene nanoplatelets on the mechanical and gas barrier properties of woven carbon fibre/epoxy compositescitations
- 2021Fabrication and Mechanical Performance of Graphene Nanoplatelet/Glass Fiber Reinforced Polymer Hybrid Composites
- 2020Sustainable, high barrier polyaleuritate/nanocellulose biocompositescitations
- 2020Multifunctional Biocomposites Based on Polyhydroxyalkanoate and Graphene/Carbon Nanofiber Hybrids for Electrical and Thermal Applicationscitations
- 2018Anodic dissolution growth of metal-organic framework HKUST-1 monitored:Via in situ electrochemical atomic force microscopy
- 2018Anodic dissolution growth of metal-organic framework HKUST-1 monitored via in situ electrochemical atomic force microscopycitations
- 2018Anodic dissolution growth of metal-organic framework HKUST-1 monitored via in situ electrochemical atomic force microscopycitations
- 2017Hydrogen Evolution at Liquid|Liquid Interfaces Catalysed by 2D Materialscitations
- 2016Metal-organic framework templated electrodeposition of functional gold nanostructurescitations
- 2016Asymmetric MoS2-graphene-metal sandwiches: Preparation, characterization and applicationcitations
- 2015Tunable doping of graphene nanoribbon arrays by chemical functionalizationcitations
- 2015Synthesis of Lateral Size-Controlled Monolayer 1H-MoS2@Oleylamine as Supercapacitor Electrodes.citations
- 2012Effect of domain boundaries on the Raman spectra of mechanically strained graphenecitations
- 2011Transition from single to multi-walled carbon nanotubes grown by inductively coupled plasma enhanced chemical vapor depositioncitations
Places of action
| Organizations | Location | People |
|---|
article
Novel techniques for characterising graphene nanoplatelets using Raman spectroscopy and machine learning
Abstract
A significant challenge for graphene nanoplatelet (GNP) suppliers is the characterisation of platelet morphology in industrial environments. 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 study, we develop novel techniques to better characterise GNP morphology and changes in surface chemistry using Raman maps of bulk powders. Providing a bespoke algorithmic framework for the analysis of these advanced materials. An unsupervised peak fitting and processing algorithm was used to extract crystallinity data and correlate it with laser-diffraction-derived lateral size values for a commercial set of GNPs rapidly and accurately. Classical machine learning was used to identify the most informative Raman features for classifying the plasma-functionalised GNPs. The initial material properties were found to affect the peak features that were the most useful for classification. In low defect density and low specific surface area GNPs, the D peak full width at half maximum is found to be the most useful, whereas the I 2D / I G ratio is the 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.