Abstract

Evolving over millions of years, hair-like natural flow sensors called cilia, which are found in fish, crickets, spiders, and inner ear cochlea, have achieved high resolution and sensitivity in flow sensing. In the pursuit of achieving such exceptional flow sensing performance in artificial sensors, researchers in the past have attempted to mimic the material, morphological, and functional properties of biological cilia sensors, to develop MEMS-based artificial cilia flow sensors. However, the fabrication of bio-inspired artificial cilia sensors involves complex and cumbersome micromachining techniques that lay constraints on the choice of materials, and prolongs the time taken to research, design, and fabricate new and novel designs, subsequently increasing the time-to-market. In this work, we establish a novel process flow for fabricating inexpensive, yet highly sensitive, cilia-inspired flow sensors. The artificial cilia flow sensor presented here, features a cilia-inspired high-aspect-ratio titanium pillar on an electrospun carbon nanofiber (CNF) sensing membrane. Tip displacement response calibration experiments conducted on the artificial cilia flow sensor demonstrated a lower detection threshold of 50 µm. Furthermore, flow calibration experiments conducted on the sensor revealed a steady-state airflow sensitivity of 6.16 mV/(m s−1) and an oscillatory flow sensitivity of 26 mV/(m s−1), with a lower detection threshold limit of 12.1 mm/s in the case of oscillatory flows. The flow sensing calibration experiments establish the feasibility of the proposed method for developing inexpensive, yet sensitive, flow sensors; which will be useful for applications involving precise flow monitoring in microfluidic devices, precise air/oxygen intake monitoring for hypoxic patients, and other biomedical devices tailored for intravenous drip/urine flow monitoring. In addition, this work also establishes the applicability of CNFs as novel sensing elements in MEMS devices and flexible sensors.

Highlights

  • Flow sensing is of utmost importance in fields related to medicine, diagnostics, pharmaceuticals, process control, weather/environmental monitoring, and forecasting systems

  • Bio-inspiration has played a vital role in the development of some of the most accurate and sensitive flow sensors, inspired by cilia commonly found in many aquatic creatures, including fish

  • The biological cilia sensors in fish are known for their superior sensitivity, as demonstrated by their excellent steady-state flow velocity detection threshold, which can be as low as 10 mm/s [3]

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Summary

Introduction

Flow sensing is of utmost importance in fields related to medicine, diagnostics, pharmaceuticals, process control, weather/environmental monitoring, and forecasting systems. In most of the cases, the sensors comprised of high-aspect-ratio polymeric pillars mimicking cilia, with piezoresistive or piezoelectric sensing membranes at the bases. The sensor demonstrated a high sensitivity of 22 mV/(mm s−1) and a very low detection threshold limit of 8.2 μm s−1 for oscillatory flows generated using a dipole vibrating at 35 Hz. Most of the works reported in the past involved complex and cumbersome microfabrication techniques involving photolithography, deposition, etching, and so forth. Our group reported electrospun carbon nanofiber (CNF) bundle-based piezoresistive sensing elements for flexible sensor applications [19]. We report a facile method of developing an artificial cilia flow sensor, featuring a high-aspect-ratio titanium pillar mimicking the cilia, which is attached at the base to a circular CNF membrane that acts as the sensing element. The facile fabrication method reported in this work will circumvent the complex fabrication methods and time budget associated with similar MEMS-based devices, which will subsequently allow for the development and commercialization of a future class of inexpensive, yet accurate and sensitive, sensors

Electrospinning of Polyacrylonitrile Nanofiber Bundles
Synthesis of Electrospun Carbon Nanofibers
Characterization of CNF Bundle-Based Flexible Sensors

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