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Towards engineering molecularly imprinted polymer-based sensors: linking design strategies to analytical and operational performance.

Sep 2026 · Analytical and Bioanalytical Chemistry · 0 citations · 39 references
Medicine

Abstract

The use of molecularly imprinted polymers (MIPs) as synthetic receptors in sensing has expanded rapidly across diagnostic, environmental, food, and pharmaceutical applications, owing to their ability to mimic biological recognition while offering chemical robustness and cost-effective synthesis. However, the analytical performance of MIP-based sensors is not determined by molecular recognition alone, but by how effectively the imprinted material is engineered and integrated within the sensing interface. Against this background, this review provides a performance-oriented analysis of strategies developed to improve MIP-based sensors by correlating specific material and device-design approaches with measurable sensing outcomes. After outlining the analytical and operational criteria used to evaluate sensor performance, the review examines how different engineering strategies address distinct performance-limiting factors, including restricted analyte accessibility, slow mass transport, binding-site heterogeneity, weak signal generation, and limited regeneration. Nanoscale and ultrathin MIP formats are discussed as approaches to improve binding-site accessibility and response dynamics, while oriented imprinting, template engineering, and solid-phase synthesis are examined as routes to enhance binding-site homogeneity and recognition reproducibility. Stimuli-responsive materials are considered for their ability to introduce active functionality into the recognition layer, enabling signal amplification, reversible target binding, and interface regeneration. By linking design strategies to sensor-level performance benefits and remaining operational limitations, this review provides a framework for evaluating how MIP engineering can contribute to more sensitive, reproducible, and functionally robust sensing platforms.

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