Machine learning-enhanced fluorescence signal processing of carbon quantum dots for high-accuracy chemical sensing
TL;DR
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.
Abstract
Carbon quantum dots (CQDs) exhibit rich photophysical behaviors, including excitation-dependent emission, surface-state variability, and multimodal fluorescence pathways, which complicate accurate, signal interpretation in chemical sensing. Recent advances in machine learning (ML) offer powerful solutions for modeling these complexities and enhancing fluorescence-based detection performance. This review provides a comprehensive analysis of ML-driven methodologies for denoising, spectral decomposition, feature extraction, and high-accuracy classification in CQD fluorescence systems. Mathematical foundations of key ML paradigms are outlined to establish a rigorous framework for signal reconstruction and generalization. Evaluations of recent applications demonstrate how ML enables ultra-low-level analyte detection, interpretable photophysical modeling, and real-time intelligent sensing across chemical and biological environments. Emerging trends—including physics-informed learning, generative data augmentation, autonomous closed-loop sensing, and distributed multimodal architectures—are examined as frontiers poised to redefine CQD fluorescence analytics. Collectively, the integration of ML with CQD photophysics represents a transformative pathway toward robust, adaptive, and next-generation chemical sensing platforms.