Microfluidic Particle Separation and Detection System Based on Standing Surface Acoustic Wave and Lensless Imaging

Objective: Separation and detection of micro-particles or cells from bio-samples by point-of-care (POC) systems are critical for biomedical and healthcare diagnostic applications. Among various microfluidic separation techniques, acoustophoresis-based technique has the advantages of label-free and good biocompatibility. However, most of the existing separation techniques are bulky and require additional equipment for analysis. Methods: We proposed a platform, which integrates an acoustophoresis-based separation device and a lensless imaging sensor into a compact standalone system to tackle this challenge. Standing Surface Acoustic Wave (SSAW) is utilized for label-free particle separation, while lensless imaging is employed for seamless particle detection and counting using self-developed dual-threshold motion detection algorithms. In particular, we specially optimized the design of microfluidic channel and interdigital transducers (IDTs) for higher performance bioparticle separation, designed a heat dissipation system for the suppression of fluid temperature, and proposed a novel frequency-temperature-curve based method to determine the appropriate signal driving frequency for IDTs. Results: At 2 mu L/min flow rate, separation efficiency of 93.52% and purity of 94.29% for 15 mu m microbead were achieved in mixed 5 mu m and 15 mu m microbead solution at a 25 dBm RF driving power, and similar results for mixed 10 mu m and 15 mu m microbead solution. Conclusions: The results showed that the integrated platform has an excellent capability to seamlessly separate, distinguish, and count microbeads of different sizes. Significance: Such a platform and the design methodologies offer a promising POC solution for label-free cell separation and detection in biomedical diagnostics.

Automated summary

The study presents an integrated platform that combines acoustophoresis-based separator and lensless imaging sensor to separate, distinguish, and count microbeads of different sizes label-free. Experimental results demonstrate the excellent performance of the platform, offering a promising solution for label-free cell separation and detection in biomedical diagnostics.