Enhancing Selectivity in Affinity Biosensors through Biorecognition-Driven Suppression of Nonspecific Binding

Despite substantial progress in biosensor development, achieving reliable sensitivity and selectivity under real-world conditions remains challenging, particularly in complex and heterogeneous sample matrices. While sensitivity has historically been the primary focus of biosensor optimization, selectivity often emerges as a limiting factor for practical performance when deployed outside controlled laboratory environments. Poor selectivity can lead to false-positive or false-negative results, thereby undermining the reliability and accuracy of biosensing platforms. A major contributor to this problem is nonspecific binding, the unintended interaction between biosensor and nontarget species. However, the origins, mechanism, and implications of nonspecific binding remain insufficiently understood and are still actively debated within the scientific community. In this review, we trace the conceptual development of nonspecific binding and critically examine its physicochemical origins in substrates and biorecognition elements. We then assess recent progress in recognition elements, such as antibodies, aptamers, and enzymes, emphasizing not only their strengths but also their limitations and vulnerability to off-target interactions. To mitigate nonspecific binding, we summarize a range of emerging strategies, including optimizing the conjugation and orientation and increasing binding site accessibility and density through structural design, removing interfering species, and implementing signal-level strategies. Finally, we outline persisting challenges and future directions for enhancing biosensor selectivity. Collectively, these insights offer a roadmap for designing next-generation biosensors with high accuracy, robust selectivity, and real-world applicability.