Molecularly Imprinted Polymeric Sensors for Antibiotic Recognition

Abstract

Molecularly imprinted polymers (MIPs) have emerged as promising recognition materials for antibiotic sensing owing to their high stability, low cost, and tunable selectivity. However, enhanced recognition capability does not necessarily translate into high-performance sensing. A critical yet often overlooked bottleneck lies in the inherently indirect nature of this conversion: molecular binding occurs within an insulating polymer matrix that is spatially and functionally decoupled from the underlying signal transducer, particularly in complex environmental matrices. To address this limitation, this review proposes a unified framework centered on recognition–structure–signal interplay. Within this framework, sensing performance depends not on the simple addition of independently optimized components, but on the synergistic integration of recognition fidelity, structural accessibility, and signal transduction efficiency. Recent advances in electrochemical, optical, photoelectrochemical, and electrochemiluminescent MIP sensors are critically examined through this lens, with emphasis on platform-specific failure modes-including inaccessible recognition sites, insufficient site-transducer integration, weak binding-induced signal modulation, signal amplification-dominated responses, and matrix-induced decoupling. Machine learning is further critically assessed as an emerging data-driven tool for MIP design, signal decoding, experimental optimization, and sensor-level performance prediction, while its broader applicability remains constrained by limited datasets, insufficient external validation, and poor cross-platform transferability. Despite major progress in sensitivity, unresolved challenges persist, including weak intrinsic conversion efficiency, structural trade-offs, limited reliability in real-world samples, a lack of standardized validation protocols, and poor scalability of laboratory fabrication. This review concludes that next-generation MIP-based antibiotic sensors require a paradigm shift from affinity-oriented imprinting and signal amplification toward co-localized recognition-transduction interfaces, matrix-tolerant architectures, interpretable data-driven design, and manufacturable field-deployable systems.