Reactant and Waste Minimization during Sample Preparation on Micro-Electrode-Dot-Array Digital Microfluidic Biochips using Splitting Trees

被引:0
作者
Dong, Chen [1 ,3 ]
Chen, Xiao [1 ]
Chen, Zhenyi [2 ]
机构
[1] Fuzhou Univ, Coll Comp & Data Sci, Xueyuan Rd, Fuzhou 350116, Fujian, Peoples R China
[2] Univ S Florida, Dept Comp Sci & Engn, Tampa, FL 33620 USA
[3] Fuzhou Univ, Fujian Key Lab Network Comp & Intelligent Informat, Xueyuan Rd, Fuzhou 350116, Fujian, Peoples R China
来源
JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS | 2024年 / 40卷 / 01期
基金
中国国家自然科学基金;
关键词
Microelectrode-dot-array (MEDA); Biochip; Mixing tree; Dilution; Sample preparation; Reactant minimization; DILUTION; GRADIENTS; DESIGN;
D O I
10.1007/s10836-024-06103-z
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Biological assays around "lab-on-a-chip (LoC)" are required in multiple concentration (or dilution) factors, satisfying specific sample concentrations. Unfortunately, most of them suffer from non-locality and are non-protectable, requiring a large footprint and high purchase cost. A digital geometric technique can generate arbitrary gradient profiles for digital microfluidic biochips (DMFBs). A next- generation DMFB has been proposed based on the microelectrode-dot-array (MEDA) architectures are shown to produce and disperse droplets by channel dispensing and lamination mixing. Prior work in this area must address the problem of reactant and waste minimization and concurrent sample preparation for multiple target concentrations. This paper proposes the first splitting-droplet sharing algorithm for reactant and waste minimization of multiple target concentrations on MEDAs. The proposed algorithm not only minimizes the consumption of reagents but also reduces the number of waste droplets by preparing the target concentrations concurrently. Experimental results on a sequence of exponential gradients are presented in support of the proposed method and demonstrate its effectiveness and efficiency. Compared to prior work, the proposed algorithm can achieve up to a 24.8% reduction in sample usage and reach an average of 50% reduction in waste droplets.
引用
收藏
页码:87 / 99
页数:13
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