CNT Based Artificial Hair Sensors for Predictable Boundary Layer Air Flow Sensing (Postscript)
AIR FORCE RESEARCH LAB WRIGHT-PATTERSON AFB OH WRIGHT-PATTERSON AFB United States
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While numerous flow sensor architectures mimic the natural cilia of crickets, locusts, bats, and fish, the prediction of sensor output for given flow conditions based on the sensor properties has not been achieved. Challenges include difficulty in determining the electromechanical properties of the sensors, limited working knowledge of the boundary layer, low sensitivity to small hair deflections, and lack of models for large deflections. Within this work, hair sensors are fabricated using piezoresistive arrays of carbon nanotubes CNTs without traditional microelectromechanical processing. While correlating the CNT array electromechanical properties to synthesis conditions remains a challenge, a consistent, proportional, and predictable response to steady, boundary-determined air flow is obtained using theory and measurement for various lengths of hairs. The moment sensitivity is shown to scale inversely with the CNT length and stiffness to a typical maximum of 1.3 0.4 resistance change nN1 m1. The normalized CNT piezoresistivity is constant 1.1 0.2 for a majority of the more than two dozen sensors examined despite the orders-of-magnitude variability in both sensitivity and CNT compressive modulus.
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