Size-based separation of bioparticles/cells is crucial to a variety of biomedical processing steps for applications such as exosomes and DNA isolation. Design and improvement of such microfluidic devices is a challenge to best answer the demand for producing homogeneous end-result for study and use. Deterministic lateral displacement (DLD) exploits a similar principle that has drawn extensive attention over years. However, the lack of predictive understanding of the particle trajectory and its induced mode makes designing a DLD device an iterative procedure. Therefore, this paper investigates a fast versatile design automation platform to address this issue. To do so, convolutional and artificial neural networks were employed to learn velocity fields and critical diameters of a wide range of DLD configurations. Later, these networks were combined with a multi-objective evolutionary algorithm to construct the automation tool. After ensuring the accuracy of the neural networks, the developed tool was tested for 12 critical conditions. Reaching the imposed conditions, the automation components performed reliably with errors of less than 4%. Moreover, this tool is generalizable to other field-based problems and since the neural network is an integral part of this method, it enables transfer learning for similar physics. All the codes generated and used in this study alongside the pre-trained neural network models are available on https://github.com/HoseynAAmiri/DLDNN.
翻译:生物粒子/细胞的大小分离对于各种生物医学处理步骤,例如远洋和DNA隔离等应用,至关重要。设计和改进这种微氟化物装置是一项挑战,无法最好地满足为研究和使用而生产同质最终结果的需求。确定性的横向迁移(DLD)利用了多年来引起广泛关注的类似原则。然而,由于对粒子轨迹及其诱导模式缺乏预测性理解,设计DLD装置是一个迭接程序。因此,本文件调查了一个快速的多功能设计自动化平台,以解决这一问题。为此,使用了神经和人工神经网络,以学习一系列DLD配置的速度字段和关键直径。后来,这些网络与构建自动化工具的多目标进化算法相结合。在确保神经网络的准确性之后,对开发的工具进行了12个关键条件的测试。达到规定条件后,自动化组件的可靠运行率低于4%。此外,这一工具可概括到其他基于实地的问题,而且由于神经神经网络是DLD配置的不可分割部分,因此,这些网络可以与这一系统/运动前的模型一起进行类似的学习。在使用。