Molecularly Imprinted Polymers for Chemical Sensors: From Molecular Recognition to Advanced Architectures and Practical Sensing Platforms

Molecularly imprinted polymers (MIPs) are synthetic recognition materials that combine molecular selectivity with chemical and structural stability, making them attractive for chemical sensing. This review examines the development of MIP-based sensors, from conventional imprinting strategies to advanced architectures and integrated sensing platforms, with emphasis on the relationship between material structure, molecular recognition, and signal transduction. Classical preparation methods, including bulk, precipitation, emulsion, suspension, and sol–gel polymerization, are compared in terms of morphology, recognition-site accessibility, mass transfer, and processing requirements. Advanced architectures, including magnetic MIPs, carbon-based hybrids, hollow and restricted-access systems, nanoMIPs, nanogels, and molecularly imprinted hybrid materials, are discussed in relation to their integration into electrochemical and optical transducers. Particular attention is given to electropolymerization, epitope imprinting, solid-phase nanoMIP synthesis, computationally guided MIP design, and portable, microfluidic, paper-based, and wearable sensing platforms. The effects of MIP architectures and preparation strategies on response time, regeneration requirements and efficiency, and operational stability are also examined in the context of practical sensor performance. A bibliometric analysis based on Scopus and Web of Science is presented to contextualize the growth and diversification of the field. Beyond analytical sensitivity and low detection limits, this review emphasizes the importance of validation in realistic matrices, reproducibility, stability, manufacturing control, and appropriate benchmarking. Overall, the practical development of MIP-based sensors depends on the effective integration of molecular recognition with the transducer and on reproducible control of the entire recognition–transduction interface.