Issue 22, 2022

Design and fabrication of a novel on-chip pressure sensor for microchannels

Abstract

Pressure is important in virtually all problems in fluid dynamics from macro-scale to micro/nano-scale flows. Although technologies are well developed for its measurement at the macroscopic scale, pressure quantification at the microscopic scale is still not trivial. This study reports the design and fabrication of an on-chip sensor that enables quantification of pressure in microfluidic devices based on a novel technique called astigmatic particle tracking. With this technique, thin membranes that sense pressure variations in the fluid flow can be characterized conveniently by imaging the shapes of the particles embedded in the membranes. This innovative design only relies on the reflected light from the back of the microchannel, rendering the sensor to be separate and noninvasive to the flow of interest. This sensor was then applied to characterize the pressure drop in single-phase flows with an accuracy of ∼70 Pa and good agreement was achieved between the sensor, a commercial pressure transducer and numerical simulation results. Additionally, the sensor successfully measured the capillary pressure across an air–water interface with a 7% deviation from the theoretical value. To the best of our knowledge, this pore-scale capillary pressure quantification is achieved for the first time using an on-chip pressure sensor of this kind. This study provides a novel method for in situ quantification of local pressure and thus opens the door to a renewed understanding of pore-scale physics of local pressure in multi-phase flow in porous media.

Graphical abstract: Design and fabrication of a novel on-chip pressure sensor for microchannels

Supplementary files

Article information

Article type
Paper
Submitted
13 juil. 2022
Accepted
07 sept. 2022
First published
09 sept. 2022

Lab Chip, 2022,22, 4306-4316

Author version available

Design and fabrication of a novel on-chip pressure sensor for microchannels

N. Raventhiran, R. S. Molla, K. Nandishwara, E. Johnson and Y. Li, Lab Chip, 2022, 22, 4306 DOI: 10.1039/D2LC00648K

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