Intelligent Tetracycline Sensing With Carbon Quantum Dots: From Photophysical Engineering to Machine Learning‐Enhanced Detection

ABSTRACT The proliferation of tetracycline antibiotics (TCs) in environmental and food matrices necessitates rapid, selective, and field‐deployable analytical methods. Carbon quantum dots (CQDs) have emerged as promising fluorescent nanomaterials for tetracycline (TC) sensing; yet translating laboratory prototypes into intelligent, real‐world platforms remains challenging. This review systematically examines the evolution from materials design to data‐driven detection strategies. We first analyze photophysical engineering approaches, including surface functionalization, heteroatom doping, and bandgap tuning, that govern CQD optical properties and analyte recognition mechanisms. Particular attention is given to selectivity‐enhancing interactions, including hydrogen bonding, electrostatic attraction, metal‐ion‐mediated coordination, molecular imprinting, and fluorescence modulation pathways such as photoinduced electron transfer and inner filter effects that enable discrimination of tetracyclines from competing species. Critical evaluation of analytical performance metrics reveals persistent challenges in matrix interference management and cross‐reactivity mitigation. Across the reviewed studies, CQD‐based sensing platforms have achieved limits of detection ranging from 0.28 nM to 0.158 μM, with linear dynamic ranges extending from the nanomolar domain to as high as 500 μM, depending on sensor architecture, transduction mechanism, and sample matrix. Recent integration of machine learning algorithms, including artificial neural networks, support vector machines, and principal component analysis, has demonstrated enhanced selectivity, interference discrimination, and multianalyte classification capabilities. We further assess emerging portable platforms, including smartphone‐integrated devices and paper‐based sensors, highlighting the synergy between miniaturization and computational intelligence. Translational barriers, including batch‐to‐batch reproducibility, regulatory validation, calibration transferability, and algorithm generalizability, are critically discussed. This review provides a roadmap for developing next‐generation intelligent sensing systems that bridge fundamental nanomaterial science, chemometric validation, artificial intelligence, and practical deployment in environmental monitoring, food safety surveillance, and resource‐limited settings.

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