Nanotube abundance from non-negative matrix factorization of Raman spectra as an example of chemical purity from open source machine learning

Par Conseil national de recherches du Canada

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DOI	Trouver le DOI : https://doi.org/10.1038/s41598-022-15359-4
Auteur	Rechercher : Flores, Elijah¹; Rechercher : Ouyang, Jianying¹; Rechercher : Lapointe, François¹; Rechercher : Finnie, Paul¹
Affiliation	Conseil national de recherches du Canada. Technologies de sécurité et de rupture
Format	Texte, Article
Sujet	analytical chemistry; carbon nanotubes and fullerenes
Résumé	The chemical purity of materials is important for semiconductors, including the carbon nanotube material system, which is emerging in semiconductor applications. One approach to get statistically meaningful abundances and/or concentrations is to measure a large number of small samples. Automated multivariate classification algorithms can be used to draw conclusions from such large data sets. Here, we use spatially-mapped Raman spectra of mixtures of chirality-sorted single walled carbon nanotubes dispersed sparsely on flat silicon/silicon oxide substrates. We use non-negative matrix factorization (NMF) decomposition in scikit-learn, an open-source, python language “machine learning” package, to extract spectral components and derive weighting factors. We extract the abundance of minority species (7,5) nanotubes in mixtures by testing both synthetic data, and real samples prepared by dilution. We show how noise limits the purity level that can be evaluated. We determine real situations where this approach works well, and identify situations where it fails.
Date de publication	2022-07-08
Maison d’édition	Nature Research
Licence	Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/deed.fr
Dans	Scientific Reports 12, nº 1, 11666 (8 juillet 2022) : 1–10.
Langue	anglais
Publications évaluées par des pairs	Oui
Exporter la notice	Exporter en format RIS
Signaler une correction	Signaler une correction (s'ouvre dans un nouvel onglet)
Identificateur de l’enregistrement	db6740ca-bed1-44c7-95d7-47e4d54098c0
Enregistrement créé	2022-07-18
Enregistrement modifié	2022-07-19

Date de modification :: 2024-10-14