IBM T. J. Watson Research Center, Yorktown Heights, NY 10598;
IBM T. J. Watson Research Center, Yorktown Heights, NY 10598.
Proc Natl Acad Sci U S A. 2017 Jun 27;114(26):E5034-E5041. doi: 10.1073/pnas.1706645114. Epub 2017 Jun 12.
Deterministic lateral displacement (DLD) is a technique for size fractionation of particles in continuous flow that has shown great potential for biological applications. Several theoretical models have been proposed, but experimental evidence has demonstrated that a rich class of intermediate migration behavior exists, which is not predicted. We present a unified theoretical framework to infer the path of particles in the whole array on the basis of trajectories in a unit cell. This framework explains many of the unexpected particle trajectories reported and can be used to design arrays for even nanoscale particle fractionation. We performed experiments that verify these predictions and used our model to develop a condenser array that achieves full particle separation with a single fluidic input.
确定性侧向位移(DLD)是一种连续流中用于颗粒尺寸分级的技术,在生物应用中显示出巨大的潜力。已经提出了几种理论模型,但实验证据表明,存在一类丰富的中间迁移行为,这是无法预测的。我们提出了一个统一的理论框架,根据单元中的轨迹来推断整个阵列中颗粒的路径。该框架解释了许多报道的意外颗粒轨迹,并可用于设计甚至纳米级颗粒分级的阵列。我们进行了验证这些预测的实验,并使用我们的模型开发了一种冷凝器阵列,仅用单个流体输入即可实现完全的颗粒分离。