Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/185419
Título: Multi-technique computational assessment of fluoride uptake in enamel using PIGE, NEXAFS, and Raman spectroscopy
Autor: Pessanha, Sofia
Fortes, António
Lopes, Marta B.
Guilherme Buzanich, Ana
Ortega-Feliu, Inés
Respaldiza, Miguel A.
Gomez Tubio, Blanca
Makarova, Anna
Smirnov, Dmitry
Kumar, Sourabh
Mata, António
Silveira, João
Palavras-chave: Chemistry(all)
Biomedical Engineering
Materials Science(all)
SDG 3 - Good Health and Well-being
Data: 12-Mai-2025
Resumo: The uptake of fluoride in the enamel matrix is an effective strategy to prevent demineralization and caries formation. In this study a comprehensive methodology is developed to evaluate and understand the uptake of fluoride in human enamel. Twenty-six healthy anterior teeth were sectioned in half; one half remained untreated, while the other was treated with 50 mg mL−1 NaF (equivalent to 22.6 mg of fluoride) through three 1-minute applications over a 12-day period, following the manufacturer's guidelines. Fluoride uptake was quantified with particle-induced gamma-ray emission (PIGE), revealing an average increase of 160% in treated samples. The formation of calcium fluoride (CaF2) and fluorapatite-like structures was confirmed through near edge X-ray absorption fine structure (NEXAFS) analysis. Due to the absence of reference spectra for hydroxyapatite, fluorapatite, and calcium fluoride, finite difference method near edge structure (FDMNES) simulations were employed to computationally model the fluorine K-edge and the Ca L-edge spectra. Density functional theory (DFT) and time-dependent DFT (TDDFT) approaches were applied to enhance spectral accuracy, enabling a refined comparison with experimental data. To establish a rapid and laboratory-based screening technique, Raman microscopy was used to analyze fluoride-treated and untreated samples. Spectral data were evaluated using both full-spectrum analysis and specific spectral features, including band intensity, full-width at half maximum (FWHM) of Raman peaks, and phosphate symmetric stretching depolarization ratios. Furthermore, machine learning algorithms were applied to classify treated and untreated enamel samples. The random forest classifier demonstrated strong predictive performance, successfully distinguishing fluoride-treated samples. This methodological approach provides an effective framework for analyzing fluoride uptake in enamel, potentially guiding future preventive dentistry strategies.
Descrição: Funding Information: This work has been financially supported by Fundação para a Ciência e a Tecnologia through the LIBPhys funding UID/FIS/04559/2020 and S. Pessanha contract CEECIND/00278/2018/CP1564/CT0007, https://doi.org/10.54499/CEECIND/00278/2018/CP1564/CT0007. Also, Marta B. Lopes acknowledges FCT for CEECINST/00042/2021, UIDB/00297/2020 and UIDP/00297/2020 (NOVA Math, Center for Mathematics and Applications). UIDB/00667/2020 and UIDP/00667/2020 (UNIDEMI). We thank the Helmholtz-Zentrum Berlin für Materialien und Energie for the allocation of synchrotron radiation beamtime at GELEM Dipole. GELEM-PES Endstation at HZB received funding from the BMBF program ErUM-Pro. Publisher Copyright: © 2025 The Royal Society of Chemistry.
Peer review: yes
URI: http://hdl.handle.net/10362/185419
DOI: https://doi.org/10.1039/d5tb00213c
ISSN: 2050-750X
Aparece nas colecções:Home collection (FCT)

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