Fluorescence sensing with perovskite quantum dots: mechanisms, design strategies, and molecular imprinting approaches

Perovskite quantum dots (PQDs) have emerged as highly promising nanomaterials for fluorescence-based sensing owing to their exceptional optical characteristics, including near-unity photoluminescence quantum yields, narrow emission linewidths, broad absorption cross-sections, and compositional tunability. Despite these advantages, three core challenges continue to limit their practical applications: poor stability in the presence of moisture, oxygen, or heat, and the toxicity of lead-based compositions. Particularly in the context of analytical sensing applications, PQDs show an inherent lack of molecular selectivity toward specific analytes. Although reviews providing a general overview of PQD synthesis or optical properties have been published, a consolidated discussion of sensor designs and mechanisms in PQD-based systems is lacking. Importantly, reviews focusing on design strategies for stability and selectivity, and the emerging role of molecularly imprinted polymers (MIPs) as a route to analyte-specific recognition is lacking. This review addresses that gap by systematically covering the foundations of PQD photophysics, including radiative recombination mechanisms and exciton dynamics, followed by the major sensing mechanisms in PQD-based systems: photoinduced electron transfer (PET), Förster resonance energy transfer (FRET), intramolecular charge transfer (ICT), and excited-state intramolecular proton transfer (ESIPT). Besides, we critically evaluate important design strategies such as surface functionalization, encapsulation, compositional doping, and hybrid material integration. A key focus of this review is a discussion on MIP-PQD hybrid systems, with in-depth discussion of the fluorescence modulation mechanisms that arise from analyte rebinding to imprinted cavities. Finally, representative applications in environmental monitoring, biomedical diagnostics, and food safety are reviewed, and future directions toward lead-free, portable, and AI-integrated sensing platforms are outlined.