A Self-Marangoni-Propelled Droplet Microarray for Ultrafast Mixing and Point-of-Care Assay of Acute Myocardial Infarction Biomarkers

Abstract

Abstract Droplet microarrays hold great promise for acute myocardial infarction (AMI) diagnosis, yet their biosensing performance is often limited by poor mixing efficiency and nonuniform signal distribution. Here, we report a self-Marangoni-propelled droplet microarray featuring a hydrophobic background, superhydrophilic rings, and hydrophilic cores, enabling solute homogenization and rapid biomarker detection. Continuous water replenishment at the droplet edge creates stable surface tension gradients, driving strong three-dimensional (3D) Marangoni convection and effectively suppressing the coffee-ring effect induced by outward capillary flow. Flow visualization and theoretical analysis reveal that this 3D Marangoni convection not only enhances the internal flow velocity but also fundamentally reconfigures the dominant mass transport direction, achieving an ∼8.7-fold increase in solute mixing efficiency compared with conventional patterned chips. For microRNAs (miRNAs) detection, the platform accelerates hybridization kinetics by ∼17.3-fold, improves sensitivity by ∼500-fold, and lowers the detection limit to 1.0 fM. Further integrated with time-series deep learning algorithms, it achieves accurate identification of AMI patients within 5 min, enabling rapid and precise AMI diagnosis. This convection-generation approach could be readily extended to create versatile droplet-array platforms for combinatorial chemical synthesis, nanomaterial fabrication, and drug combination testing, holding promise for diverse applications beyond biosensing.