TY - JOUR
T1 - CNT-Based Artificial Hair Sensors for Predictable Boundary Layer Air Flow Sensing
AU - Slinker, Keith A.
AU - Kondash, Corey
AU - Dickinson, Benjamin T.
AU - Baur, Jeffery W.
N1 - Funding Information:
The authors gratefully acknowledge financial support from the Air Force Office of Scientific Research (AFOSR), Dr. Byung-Lip (Les) Lee, Program Manager, Peter Schuhmann for assistance with data collection in the square tube, and Matthew Maschmann for focused ion beam processing and imaging of the sensor in Figure 1. The reference to the equation in figure 7 was incorrectly presented in the originally published manuscript. This was corrected on December 14, 2016.
Publisher Copyright:
© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
PY - 2016/12
Y1 - 2016/12
N2 - 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 nN−1 m−1. 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. The sensor sensitivity and noise both distinctly change as the flow transitions from steady and laminar to turbulent, suggesting the sensor may be capable of detecting flow transitions.
AB - 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 nN−1 m−1. 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. The sensor sensitivity and noise both distinctly change as the flow transitions from steady and laminar to turbulent, suggesting the sensor may be capable of detecting flow transitions.
KW - biomimetics
KW - carbon nanotubes
KW - microelectromechanical systems
KW - sensors
KW - structure–property relationships
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U2 - 10.1002/admt.201600176
DO - 10.1002/admt.201600176
M3 - Article
AN - SCOPUS:85038404898
SN - 2365-709X
VL - 1
JO - Advanced Materials Technologies
JF - Advanced Materials Technologies
IS - 9
M1 - 1600176
ER -