Deep-learning-based denoising approach to enhance Raman spectroscopy in mass-produced graphene
Carregando...
Data
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Federal de Minas Gerais
Descrição
Tipo
Artigo de periódico
Título alternativo
Primeiro orientador
Membros da banca
Resumo
The inherently weak signal present in Raman spectroscopy makes spectral resolution susceptible to noise. Hence, efficient denoising techniques for post-processing of spectral data are required. We introduce two efficient approaches to remove noise from graphene Raman spectra, based on deep neural network architectures using supervised and unsupervised learning. We compared the performance of these approaches with three traditional noise removal methods. The experimental results demonstrate the effectiveness of deep-learning models in the denoising task, which is crucial in interpreting characterization data of mass-produced graphene. Overall, our supervised approach outperforms all considered baselines, as well as the unsupervised method, providing significant improvement in noise reduction.
Abstract
Assunto
Aprendizado profundo, Grafeno, Redes neurais, Espectroscopia de Raman
Palavras-chave
Deep-learning, Mass-produced graphene, Neural networks, Spectra denoising
Citação
Curso
Endereço externo
https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/jrs.6317