Surface engineering strategies to enhance the detection sensitivity of affinity-based biosensors: from ligand density to effective interfacial recognition

Affinity-based biosensors are increasingly expected to detect ultralow-abundance targets in complex matrices while supporting portable, integrated, and scalable analytical platforms. In such settings, sensitivity can be limited not only by signal generation but also by inefficient target capture at the sensing interface. This review critically examines surface-engineering strategies for affinity-based biosensors through the concept of effective interfacial recognition. We compare surface activation, silane monolayers, polymer brushes, dendrimers, branched peptides, adhesive polymers, polyelectrolytes, hydrogels, mesoporous films, nanostructured and graphene-based interfaces, and multivalent aptamer architectures. For each strategy, we assess the interfacial limitation addressed, the mechanism of sensitivity enhancement, key trade-offs, and the sensing contexts in which the approach is most useful. The literature shows that analytical performance is governed not by nominal ligand density alone, but by whether immobilized ligands remain physically accessible and binding-active, whether targets can reach useable sites within the assay window while nonspecific background remains controlled, and whether successful capture remains effectively coupled to the transducer. Persistent weaknesses include overreliance on limits of detection measured in ideal matrices, inadequate quantification of retained ligand activity, limited reproducibility testing, and insufficient attention to trade-offs among loading, transport, and signal coupling. Future progress will depend on hybrid, device-compatible interfaces that jointly optimize ligand presentation, antifouling behavior, mass transport, and transducer compatibility. Therefore, surface modification should be treated as interfacial performance engineering rather than merely an immobilization step.