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Introducing the Novel Mixed Gaussian-Lorentzian Lineshape in the Analysis of the Raman Signal of Biochar

In this research, an innovative procedure is proposed to elaborate Raman spectra obtained from nanostructured and disordered solids. As a challenging case study, biochar, a bio-derived carbon based material, was selected. The complex structure of biochar (i.e., channeled surface, inorganic content)...

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Detalles Bibliográficos
Autores principales: Tagliaferro, Alberto, Rovere, Massimo, Padovano, Elisa, Bartoli, Mattia, Giorcelli, Mauro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7558824/
https://www.ncbi.nlm.nih.gov/pubmed/32899319
http://dx.doi.org/10.3390/nano10091748
Descripción
Sumario:In this research, an innovative procedure is proposed to elaborate Raman spectra obtained from nanostructured and disordered solids. As a challenging case study, biochar, a bio-derived carbon based material, was selected. The complex structure of biochar (i.e., channeled surface, inorganic content) represents a serious challenge for Raman characterization. As widely reported, the Raman spectra are closely linked to thermal treatments of carbon material. The individual contributions to the Raman spectra are difficult to identify due to the numerous peaks that contribute to the spectra. To tackle this problem, we propose a brand new approach based on the introduction, on sound theoretical grounds, of a mixed Gaussian-–Lorentzian lineshape. As per the experimental part, biochar samples were carbonized in an inert atmosphere at various temperatures and their respective spectra were successfully decomposed using the new lineshape. The evolution of the structure with carbonization temperature was investigated by Raman and XRD analysis. The results of the two techniques fairly well agree. Compared to other approaches commonly reported in the literature this method (i) gives a sounder basis to the lineshape used in disordered materials, and (ii) appears to reduce the number of components, leading to an easier understanding of their origin.