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Group 5: Challenge: Nanopore “Defect detection in graphene sheets”

Group 5: Challenge: Nanopore “Defect detection in graphene sheets”
Group 5: Challenge: Nanopore “Defect detection in graphene sheets”
Graphene is a nanomaterial with excellent super conducting properties, is considered to be the strongest material available and is impermeable to gasses (even Helium-the smallest gas atom). Its properties find applications in fabrication of electronic and optoelectronic devices, gas sensors such as chemiresistors for detection of ammonium [1] and nitrogen dioxide [2], biosensors, composite materials and energy storage devices. Graphene has a regular structure- similar to a honey comb, which consists of fused six membered carbon rings into 2D array. This atomic structure is the foundation of the exceptional properties of the material. The occurrence of defects in the lattice which occur during the manufacturing process can cause significant deterioration in the property of the final material produced [3]. These defects can be detected with a number of spectroscopy-microscopic techniques, one of which is Surface Scanning Electron Microscopy (SEM). In the challenge Nanopore “Defect Detection in Graphene Sheets” a complete dataset of 180 SEM images 256x256 pixels (full-stack), from which were derived 2279 images 48x48 pixels of perfect patches (pp) and 32 images 48x48 pixels of defect containing patches (dp) were provided in numpy arrays, see Figure 1. All pixel values had been normalised to values between 0.00 and 1.00. Figure 1 Nanopore “Defect Detection in Graphene Sheets”. The task was to design a classifier for SEM graphene images using the pp dataset. The two classes are as follows: the first is images without defects and the second class, images with defects, where the type of defect or its localisation are not considered. Finally, the classifier had to be evaluated using an appropriate metric on the dp dataset.
AI3SD, Machine Learning, Summer School
3
University of Southampton
Bachs Herra, Anna
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Cisse, Aboulatif
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de la Cruz Nunez Andrade, Emilio Alexis
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Deussen, Philipp
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Yankov, Ivan
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Frey, Jeremy G.
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Niranjan, Mahesan
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Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Bachs Herra, Anna
f0551428-22ef-47c4-ab99-2532a68a41ca
Cisse, Aboulatif
a95dd2b3-a226-46da-9502-44694396ec7f
de la Cruz Nunez Andrade, Emilio Alexis
b8b1a202-cb27-405f-ac49-a8fe8c524e9e
Deussen, Philipp
69a4fcd5-f7f3-4b00-9f1e-75294ec631f5
Yankov, Ivan
495cc568-016c-40ee-a9d7-848a2a74b0c1
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
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Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420

Bachs Herra, Anna, Cisse, Aboulatif, de la Cruz Nunez Andrade, Emilio Alexis, Deussen, Philipp and Yankov, Ivan , Frey, Jeremy G., Niranjan, Mahesan and Kanza, Samantha (eds.) (2022) Group 5: Challenge: Nanopore “Defect detection in graphene sheets” (AI4SD-Machine-Learning-Summer-School, 3) University of Southampton 11pp. (doi:10.5258/SOTON/AI3SD0246).

Record type: Monograph (Project Report)

Abstract

Graphene is a nanomaterial with excellent super conducting properties, is considered to be the strongest material available and is impermeable to gasses (even Helium-the smallest gas atom). Its properties find applications in fabrication of electronic and optoelectronic devices, gas sensors such as chemiresistors for detection of ammonium [1] and nitrogen dioxide [2], biosensors, composite materials and energy storage devices. Graphene has a regular structure- similar to a honey comb, which consists of fused six membered carbon rings into 2D array. This atomic structure is the foundation of the exceptional properties of the material. The occurrence of defects in the lattice which occur during the manufacturing process can cause significant deterioration in the property of the final material produced [3]. These defects can be detected with a number of spectroscopy-microscopic techniques, one of which is Surface Scanning Electron Microscopy (SEM). In the challenge Nanopore “Defect Detection in Graphene Sheets” a complete dataset of 180 SEM images 256x256 pixels (full-stack), from which were derived 2279 images 48x48 pixels of perfect patches (pp) and 32 images 48x48 pixels of defect containing patches (dp) were provided in numpy arrays, see Figure 1. All pixel values had been normalised to values between 0.00 and 1.00. Figure 1 Nanopore “Defect Detection in Graphene Sheets”. The task was to design a classifier for SEM graphene images using the pp dataset. The two classes are as follows: the first is images without defects and the second class, images with defects, where the type of defect or its localisation are not considered. Finally, the classifier had to be evaluated using an appropriate metric on the dp dataset.

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Published date: 8 July 2022
Venue - Dates: AI4SD Machine Learning Summer School, University of Southampton, Southampton, United Kingdom, 2022-06-20 - 2022-06-24
Keywords: AI3SD, Machine Learning, Summer School

Identifiers

Local EPrints ID: 470691
URI: http://eprints.soton.ac.uk/id/eprint/470691
PURE UUID: 0356de81-1dfc-4e11-a879-1157c65c2cfd
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489

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Date deposited: 18 Oct 2022 16:36
Last modified: 21 Oct 2022 01:52

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Contributors

Author: Anna Bachs Herra
Author: Aboulatif Cisse
Author: Emilio Alexis de la Cruz Nunez Andrade
Author: Philipp Deussen
Author: Ivan Yankov
Editor: Jeremy G. Frey ORCID iD
Editor: Mahesan Niranjan ORCID iD
Editor: Samantha Kanza ORCID iD

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