B. T. Sayed

Machine learning-enhanced fluorescence signal processing of carbon quantum dots for high-accuracy chemical sensing

A comprehensive analysis of ML-driven methodologies for denoising, spectral decomposition, feature extraction, and high-accuracy classification in CQD fluorescence systems shows how ML enables ultra-low-level analyte detection, interpretable photophysical modeling, and real-time intelligent sensing across chemical and biological environments.