From Synthesis to Intelligence: Machine Learning-Enabled Pattern Recognition in Carbon Quantum Dot Fluorescent Sensor Arrays

Carbon quantum dot (CQD) fluorescent sensor arrays have emerged as versatile platforms for multiplexed chemical and biological analysis by generating high-dimensional spectral fingerprints from diverse photophysical interactions. However, translating these complex signals into reliable analytical decisions requires integration between rational nanomaterial design and advanced computational strategies. This review presents a synthesis-to-classification framework that systematically links CQD precursor selection, surface-state engineering, and array architecture with fluorescence feature generation, machine-learning-based pattern recognition, and analytical validation. Engineering strategies for constructing ML-ready CQD sensor arrays are critically examined, including bottom-up/top-down synthesis approaches, functionalization mechanisms, and spectral information encoding. The ML workflow is subsequently analyzed through dimensionality reduction, supervised classification, and performance evaluation, with emphasis on how material-derived feature spaces influence algorithm selection and model reliability. Unlike previous reviews that primarily focus on CQD synthesis optimization, quantum-dot computation, or broad ML applications, this work provides a materials-to-decision perspective by evaluating algorithmic rigor, dataset limitations, validation practices, and real-world generalization of CQD sensing platforms. Critical assessment of reported systems for antibiotic detection, bacterial identification, and emerging analytical targets reveals persistent challenges associated with reproducibility, data scarcity, and laboratory-to-field translation. Future directions toward physics-informed ML, active learning, and miniaturized autonomous sensing systems are discussed to guide the development of robust and deployable intelligent CQD platforms.

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