Du Ryan Shijie, Liu Lily, Ng Simon, Sambandam Sneha, Hernandez Adame Bernardo, Perez Hansell, Ha Kyung, Falcon Claudia, de Rutte Joseph, Di Carlo Dino, Bertozzi Andrea L
Department of Mathematics, University of California, Los Angeles, California, USA.
Department of Mathematics, University of Chicago, Chicago, Illinois, USA.
Phys Rev E. 2021 Jul;104(1-2):015109. doi: 10.1103/PhysRevE.104.015109.
Drop-carrier particles (DCPs) are solid microparticles designed to capture uniform microscale drops of a target solution without using costly microfluidic equipment and techniques. DCPs are useful for automated and high-throughput biological assays and reactions, as well as single-cell analyses. Surface energy minimization provides a theoretical prediction for the volume distribution in pairwise droplet splitting, showing good agreement with macroscale experiments. We develop a probabilistic pairwise interaction model for a system of such DCPs exchanging fluid volume to minimize surface energy. This leads to a theory for the number of pairwise interactions of DCPs needed to reach a uniform volume distribution. Heterogeneous mixtures of DCPs with different sized particles require fewer interactions to reach a minimum energy distribution for the system. We optimize the DCP geometry for minimal required target solution and uniformity in droplet volume.
液滴载体颗粒（DCPs）是一种固体微粒，旨在无需使用昂贵的微流控设备和技术就能捕获目标溶液的均匀微尺度液滴。DCPs可用于自动化高通量生物检测和反应以及单细胞分析。表面能最小化对成对液滴分裂中的体积分布提供了理论预测，与宏观实验结果吻合良好。我们针对这样一个交换流体体积以最小化表面能的DCPs系统开发了一个概率成对相互作用模型。这引出了一个关于DCPs达到均匀体积分布所需成对相互作用数量的理论。具有不同尺寸颗粒的DCPs的异质混合物达到系统最小能量分布所需的相互作用更少。我们优化了DCP的几何形状，以实现所需目标溶液的最小化和液滴体积的均匀性。
