Amplified EQCM-D detection of extracellular vesicles using 2D gold nanostructured arrays fabricated by block copolymer self-assemblyElectronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d2nh00424k

Extracellular vesicles (EVs) are routinely released from nearly all cell types as transport vehicles and for cell communication. Crucially, they contain biomolecular content for the identification of health and disease states that can be detected from readily accessible physiological fluids, including urine, plasma, or saliva. Despite their clinical utility within noninvasive diagnostic platforms such as liquid biopsies, the currently available portfolio of analytical approaches are challenged by EV heterogeneity in size and composition, as well as the complexity of native biofluids. Quartz crystal microbalance with dissipation monitoring (QCM-D) has recently emerged as a powerful alternative for the phenotypic detection of EVs, offering multiple modes of analyte discrimination by frequency and dissipation. While providing rich data for sensor development, further progress is required to reduce detection limits and fully exploit the technique's potential within biosensing. Herein, we investigate the impact of nanostructuring the sensor electrode surface for enhancing its detection capabilities. We employ self-assembly of the block copolymer polystyrene-block-poly(4-vinylpyridine) to create well defined 2D gold islands viaselective impregnation of the pyridine domain with gold precursors and subsequent removal of the template. When matched to the EV length scale, we find a 4-fold improvement in sensitivity despite a 4-fold reduction in area for analyte and ligand anchoring in comparison to a flat sensor surface. Creation of tailored and confined sensing regions interspersed by non-binding silica provides optimal spatial orientation for EV capture with reduced steric effects and negative cooperativity of grafted antibodies, offering a promising route for facilitated binding and enhanced performance of sensor platforms.