Bioinspired PDMS-graphene cantilever flow sensors using 3D printing and replica moulding

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Abstract

Flow sensors found in animals often feature soft and slender structures (e.g. fish neuromasts, insect hairs, mammalian stereociliary bundles, etc.) that bend in response to the slightest flow disturbances in their surroundings and heighten the animal's vigilance with respect to prey and/or predators. However, fabrication of bioinspired flow sensors that mimic the material properties (e.g. low elastic modulus) and geometries (e.g. high-aspect ratio structures) of their biological counterparts remains a challenge. In this work, we develop a facile and low-cost method of fabricating high-aspect ratio (HAR) cantilever flow sensors inspired by the mechanotransductory flow sensing principles found in nature. The proposed workflow entails high-resolution 3D printing to fabricate the master mould, replica moulding to create HAR polydimethylsiloxane (PDMS) cantilevers (thickness = 0.5 – 1 mm, width = 3 mm, aspect ratio = 20) with microfluidic channel (150 µm wide × 90 µm deep) imprints, and finally graphene nanoplatelet ink drop-casting into the microfluidic channels to create a piezoresistive strain gauge near the cantilever's fixed end. The piezoresistive flow sensors were tested in controlled airflow (0 – 9 m/s) inside a wind tunnel where they displayed high sensitivities of up to 5.8 kΩ/ms-1, low hysteresis (11% of full-scale deflection), and good repeatability. The sensor output showed a second order dependence on airflow velocity and agreed well with analytical and finite element model predictions. Further, the sensor was also excited inside a water tank using an oscillating dipole where it was able to sense oscillatory flow velocities as low as 16 – 30 µm/s at an excitation frequency of 15 Hz. The methods presented in this work can enable facile and rapid prototyping of flexible HAR structures that can find applications as functional biomimetic flow sensors and/or physical models which can be used to explain biological phenomena.
Original languageEnglish
Article number095501
Number of pages11
JournalNanotechnology
Volume32
Issue number9
DOIs
Publication statusPublished - 8-Dec-2020

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