Zhu Xiongfeng, Man Tianxing, Tan Xing Haw Marvin, Chung Pei-Shan, Teitell Michael A, Chiou Pei-Yu
Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, California, USA.
Lab Chip. 2021 Mar 7;21(5):942-950. doi: 10.1039/d0lc00960a. Epub 2021 Jan 18.
We demonstrate a novel platform for mapping the pressure distribution of complex microfluidics networks with high spatial resolution. Our approach utilizes colorimetric interferometers enabled by lossy optical resonant cavities embedded in a silicon substrate. Detection of local pressures in real-time within a fluid network occurs by monitoring a reflected color emanating from each optical cavity. Pressure distribution measurements spanning a 1 cm area with a spatial resolution of 50 μm have been achieved. We applied a machine-learning-assisted sensor calibration method to generate a dynamic measurement range from 0 to 5.0 psi, with 0.2 psi accuracy. Adjustments to this dynamic measurement range are possible to meet different application needs for monitoring flow conditions in complex microfluidics networks, for the timely detection of anomalies such as clogging or leakage at their occurring locations.
我们展示了一种用于以高空间分辨率绘制复杂微流体网络压力分布的新型平台。我们的方法利用了嵌入硅基板中的有损光学谐振腔实现的比色干涉仪。通过监测每个光学腔发出的反射颜色来实时检测流体网络中的局部压力。已经实现了在1平方厘米区域内的压力分布测量,空间分辨率为50微米。我们应用了一种机器学习辅助的传感器校准方法,以生成0至5.0 psi的动态测量范围,精度为0.2 psi。可以根据不同的应用需求调整此动态测量范围,以监测复杂微流体网络中的流动状况,及时检测诸如堵塞或泄漏等异常情况的发生位置。