Enrichment of metal particles in lubricating oil is a crucial pretreatment for wear debris analyses in applications of condition-based machinery maintenance. Current techniques using physical filter cleaning and magnetic attachment to enrich metal particles have limitations in terms of efficiency and selectivity. This work presents an innovative acoustic manipulation chip for efficiently enriching metallic particles from lubricating oil. The platform utilizes the hybrid acoustic forces to perform high throughput particle enrichment in microchannels, even in an intensive flow environment. Regarding the viscosity effect of lubricating oil, the temperature dependence upon the particle enrichment is explored, and the figure of merit is employed to quantify the enrichment performance from the captured microscopic images. Experimental results demonstrate the proposed platform shows great nonselectivity for enriching both magnetic and nonmagnetic particles. This method opens a new door for developing automatic filter-free pretreatment tools to perform efficient particle enrichment in lubricating oil, which have great potential in many application scenarios, such as advanced wear debris analyses, oil quality monitoring, etc.

中文翻译：

---

基于无滤波声学操纵芯片的润滑油中金属颗粒的高效富集


在基于状态的机械维护应用中，金属颗粒在润滑油中的富集是磨损碎屑分析的关键预处理。目前使用物理过滤器清洁和磁性附件来富集金属颗粒的技术在效率和选择性方面存在局限性。这项工作提出了一种创新的声学操纵芯片，用于有效地从润滑油中富集金属颗粒。该平台利用混合声力在微通道中进行高通量颗粒富集，即使在密集的流动环境中也是如此。关于润滑油的粘度效应，探讨了温度对颗粒富集的依赖性，并采用品质因数从捕获的显微图像中量化富集性能。实验结果表明，所提出的平台对富集磁性和非磁性颗粒都显示出很好的非选择性。该方法为开发自动无过滤器预处理工具打开了一扇新的大门，以在润滑油中进行高效的颗粒富集，在许多应用场景中具有很大的潜力，例如高级磨损碎片分析、油质监测等。