Multidimensional particle fractioning using surface acoustic waves

使用表面声波进行多维颗粒分级

• 批准号: 444806275
• 负责人: Professor Dr.-Ing. Christian Cierpka
• 金额: --
• 依托单位: Institut für Thermo- und Fluiddynamik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/444806275?language=en
• 关键词: Multidimensional; particle; fractioning; using; surface

For many industrial applications, in process engineering and food chemistry as well as pharmaceutical production, particulate systems are of vital importance. In microfluidics, forces acting on particles scale differently due to the square cube law. Here passive and active methods can be applied for fractionation. Passive methods such as inertial flow or flow focusing have the benefit to be very robust and do not require external energy. However, active methods applying external fields (such as magnetic, electric, and acoustic fields) offer a greater flexibility at the expense of more complex systems. A combination of both types opens the possibility to build a flexible and controllable system that can be adapted to the different parameters of varying particle suspensions. Therefore, these systems are well suited to fractionate particle suspensions by different properties. The aim of the research project is to setup a system using surface acoustic waves to manipulate and sort particles due to their size, shape and acoustic contrast (density and compressibility). In order to be able to characterize such systems, a deep neural network should be applied to learn the particle position based on astigmatically deformed particle images. In addition, the network can be trained to extend the applicability of this measurement technique to multi dispersed particles and to detect the corresponding size and shape of the particles. Finally, microfluidics fractionation systems shall be produced at the technology center (Center of Micro- and Nanotechnologies) at TU Ilmenau and characterized in detail, to draw conclusions on the design of technical relevant systems for the customizable fractioning of suspensions of very fine particles (< 500 nm).

对于许多工业应用，在工艺工程和食品化学以及制药生产中，颗粒系统至关重要。在微流体中，由于平方立方定律，作用在颗粒上的力的比例不同。在这里，可以应用被动和主动方法进行分馏。诸如惯性流或流聚焦的被动方法具有非常稳健并且不需要外部能量的益处。然而，施加外部场（例如磁场、电场和声场）的主动方法以更复杂的系统为代价提供了更大的灵活性。这两种类型的组合打开了建立一个灵活和可控的系统，可以适应不同的参数变化的颗粒悬浮液的可能性。因此，这些系统很好地适用于不同性质的压裂颗粒悬浮液。该研究项目的目的是建立一个系统，使用表面声波来操纵和分类颗粒，因为它们的大小，形状和声学对比度（密度和可压缩性）。为了能够表征这样的系统，应该应用深度神经网络来基于变形的粒子图像学习粒子位置。此外，可以训练网络以将这种测量技术的适用性扩展到多分散颗粒，并检测颗粒的相应尺寸和形状。最后，微流控分馏系统将在TU Ilmenau的技术中心（微纳米技术中心）生产，并进行详细表征，以得出关于设计技术相关系统的结论，用于定制极细颗粒（< 500 nm）悬浮液的分馏。