Development of an intelligent droplet acoustofluidic platform for automated liquid-liquid handing and monitoring in biomedical research

About This Grant

Project Summary Over the past several decades, advancements in automated technologies for liquid handling and monitoring have significantly accelerated research across various fields of chemistry, biology, and medicine. These innovations have facilitated breakthroughs in numerous applications, including sequencing library preparation, clinical diagnostics, large-scale compound screening, and high-throughput chemical reactions. Usually, these biological, biochemical, and physical reactions/processes occurring within tiny droplets (with a volume of picolitre) may exhibit heterogeneous, complex, and dynamic characteristics, requiring high-throughput and high-precision handling, real-time mentoring, and dynamic feedback control of massive liquids. Current liquid handling methods using robotics, micro-droplets, pneumatic valves, electrowetting, acoustics, hydrodynamics, or magnetics have achieved fluid processing, but some may face significant issues including cross-contamination, low throughput, limited precision, and reproducibility. Moreover, current automated liquid handling technologies lack real-time monitoring and dynamic feedback control, limiting their ability to handle various reagents and manipulate complex conditions. To address these challenges, we propose an ‘Intelligent Droplet Acoustofluidics’ platform. Our recent invention of ‘Digital Acoustofluidics’ integrates acoustics and microfluidics for manipulating droplets in oil, showing promising potential to overcome the drawbacks of the existing liquid-handling technologies. Additionally, our engineering advances in ‘Intelligent Acoustofluidics’ leverage artificial intelligence (AI) for dynamic regulation of acoustofluidic actuators via an imaging-based closed-loop feedback control system. By combining two unique systems together, we expect the platform can achieve on-demand generation of droplets with controlled volumes, transportation of multiple droplets in parallel, fusion and mixing of multiple droplets, real- time monitoring of each droplet, and automated operation of liquid-liquid interaction using an AI-guided closed- loop controller. After optimizing the proposed platform, we will validate the performance of the proposed platform across two distinct biomedical applications: protein crystal chemistry and high-throughput drug screening to address the limitations that have hindered in using of traditional liquid-handling tools.