Molecularly Imprinted Polymer (MIP) Based Electrochemical Sensors for Amino Acid Detection Towards Wearable Sensing

Continuous, non-invasive monitoring of physiological health is a significant objective in modern medicine and personalized wellness. To efficiently capture physiological data and diagnostic metrics, the ideal biofluid/medium for health monitoring should be non-invasive, easy to access, and contains a variety of physiologically significant analytes. Sweat is a particularly promising biofluid for real-time monitoring due to its ease of usage and rich milieu of metabolic and potentially diagnostic biomarkers. Within sweat, amino acids are of particular diagnostic interest due to their direct correlation with a spectrum of metabolic disorders, chronic illnesses, and overall health. Molecularly Imprinted Polymers (MIPs) have emerged as a promising class of synthetic, antibody-mimetic receptors that offer a robust, stable, and cost-effective alternative to traditional biological recognition elements. This review provides a comprehensive examination of MIP-based sensors for the detection of amino acids in sweat. It commences with an overview of the current literature around wearable sensing and real-time health monitoring. Then it presents a dissection of sweat composition and its utility as a diagnostic fluid with a following exploration of the role of amino acids as pivotal biomarkers in sweat. The review then discusses the fundamental principles of molecular imprinting, detailing various synthesis methodologies and the underlying physicochemical binding mechanisms governing MIP-analyte interactions. Subsequently, the specifics of MIP-based sensor construction for sweat analysis are investigated such as substrate selection, various signal transduction modalities with an emphasis on electrochemical techniques, nanomaterial integration, and current methods for sweat induction in wearables. This review concludes with analyzing current research trajectories, addressing the scientific and technical challenges such as sensor regeneration, physiological differences in amino acid concentrations, and explores the opportunities for future applications like point-of-care diagnostics, management of chronic diseases, and integration with artificial intelligence for personalized health analytics and health monitoring.