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Updated: May 11, 2026

Ratiometric Imaging of Extracellular pH in Dental Biofilms
Published on: March 9, 2016
Multi-technique computational assessment of fluoride uptake in enamel using PIGE, NEXAFS, and Raman spectroscopy
Sofia Pessanha1, António Fortes1, Marta B Lopes2,3
1LIBPhys, LA-REAL, Faculdade de Ciências e Tecnologia, NOVA FCT, Universidade NOVA de Lisboa, 2829-516, Caparica, Portugal. sofia.pessanha(at)fct.unl.pt.
Abstract:
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.
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