Micromixer-controlled nanoparticle size distribution for biomolecular interaction readouts

Official URL: https://dx.doi.org/10.1016/j.biosx.2026.100783

The interaction of nanoparticles within the micromixer-assisted microfluidic platforms offers a powerful strategy for controlled biomarker capture by simultaneously enhancing surface-activated binding and transport-limited interactions under laminar flow conditions. In this study, poly(acrylic acid)-coated superparamagnetic iron oxide nanoparticles (SPION) with a number-based hydrodynamic diameter of 13.2 nm and a strong negative zeta potential (−55.3 mV) were synthesized and successfully functionalized with streptavidin and alpha-fetoprotein (AFP)-specific antibodies using EDC/NHS chemistry, achieving a conjugation efficiency of 98%. Protein conjugation resulted in systematic increases in hydrodynamic size and corresponding reductions in zeta potential, confirming effective surface modification. Streptavidin–biotin interactions (0-12.22 ng/mL) and AFP binding (1 pg/mL to 100 ng/mL) were investigated using dynamic light scattering (DLS) and nanoparticle tracking analyzer (NTA) under both conventional incubation conditions and transition flow element (TFU) mediated micromixing conditions. Microfluidic treatment using a TFU micromixer produced concentration-dependent and reproducible nanoparticle size shifts while maintaining a dominant nanoscale population and preventing uncontrolled aggregation. In contrast, incubation-based assays exhibited broader size distributions, irregular trends, and higher inter-experimental variability. Notably, TFU processing enabled linear and measurable size changes at ∼1 nM concentration increments (Re = 20), demonstrating controlled binding kinetics and improved reproducibility. These findings present micromixer-assisted microfluidic systems as effective TFU for harnessing controllable, binding-induced nanoparticle size shifts as a reproducible readout for biomolecular interaction, supporting their potential as preprocessing platforms in early-stage biomarker detection workflows.