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

Area of Science:

• Materials Science
• Analytical Chemistry
• Data Science

Background:

• Carbon quantum dots (CQDs) display complex photophysical properties like excitation-dependent emission.
• These complexities hinder accurate signal interpretation in fluorescence-based chemical sensing applications.
• Machine learning (ML) offers advanced computational tools to address these challenges.

Purpose of the Study:

• To review ML methodologies for analyzing CQD fluorescence signals.
• To provide a framework for signal reconstruction and generalization using ML.
• To highlight ML's role in advancing CQD-based chemical and biological sensing.

Main Methods:

• Comprehensive analysis of ML techniques for denoising, spectral decomposition, and feature extraction in CQD fluorescence.
• Outlining mathematical foundations of ML paradigms for signal processing.
• Evaluating recent applications of ML in CQD sensing systems.

Main Results:

• ML effectively models complex CQD photophysics, enhancing fluorescence detection performance.
• Demonstrated ML's capability for ultra-low-level analyte detection and interpretable photophysical modeling.
• ML enables real-time intelligent sensing in diverse chemical and biological environments.

Conclusions:

• Integrating ML with CQD photophysics offers a transformative approach to chemical sensing.
• ML facilitates the development of robust, adaptive, and next-generation sensing platforms.
• Emerging trends like physics-informed learning and autonomous sensing will redefine CQD fluorescence analytics.