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Modular Centrifugal Microfluidics for Sample Preparation

化学 微流控 模块化设计 纳米技术 样品(材料) 样品制备 色谱法 程序设计语言 材料科学 计算机科学
作者
Ali Gholizadeh,Gabriel Mazzucchelli,Ana Amoroso,Tristan Gilet
出处
期刊:Analytical Chemistry [American Chemical Society]
被引量:1
标识
DOI:10.1021/acs.analchem.5c00076
摘要

Sample preparation is often a critical and labor-intensive step in molecular biology and analytical chemistry. It bottlenecks biological assays, where liquid-handling speed and technique influence the outcome. While automation improves efficiency, traditional systems such as robotic platforms remain costly, complex, and resource-intensive to manufacture. Centrifugal microfluidic devices provide liquid-handling operations at the microliter scale by using microfluidic channels and chambers engraved on disks (Lab-On-A-Disk, LOAD). However, their monolithic design limits flexibility and demands microfluidic expertise, thereby increasing prototyping time and costs, while discouraging broader adoption. To address these limitations, we introduce modular microfluidic chips that are integrable and functional on both LOAD platforms and commercial centrifuges, enabling broad laboratory use without additional equipment. These interchangeable modules perform specific functions─dispensing, metering, mixing, pooling, and collection─without requiring extra components for leak-proof interconnection. Their detachability from the rotating support allows fluid control through "flipping" relative to the centrifugal force. Additionally, they are compatible with multiwell plates and stackable in swinging-bucket centrifuges, enabling high-throughput sample preparation. As a proof of concept, an enzymatic assay was performed by using several assemblies of modules in parallel. After the reagents were mixed and transferred into a well plate, absorbance was measured at three antibiotic concentrations, confirming accurate volume control and reproducible measurements. This modular approach enhances miniaturization, compatibility, and affordability while reducing the reliance on expensive and bulky robotic systems. By simplifying workflows and improving flexibility, this provides an efficient alternative for rapid and scalable sample preparation.
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