Superlow-Noise Quasi-2D Vertical Tunneling Tactile Sensor for Fine Liquid Dynamic Recognition

Area of Science:

• Materials Science
• Nanotechnology
• Biomimetics

Background:

• Existing tactile sensors lack sufficient pressure resolution due to noise limitations.
• High-precision tactile recognition requires sensors capable of discriminating pressures within the human perception range.

Purpose of the Study:

• To develop a quasi-2D vertical tunneling tactile sensor with enhanced noise suppression and pressure resolution.
• To emulate the structure of biological fingertip Merkel cells for improved tactile sensing.

Main Methods:

• Fabrication of a van der Waals heterojunction sensor using conformal graphene nanowalls-hexagonal boron nitride-graphene (CGNWs-hBN-Gr).
• Utilizing tunneling channel modulation to simulate biological ion gating mechanisms for noise reduction.
• Incorporating multiscale conformal micro- and nanostructured CGNWs for enhanced sensitivity.

Main Results:

• Achieved a noise power spectral density (PSD) of 2.22 × 10^-24 A²/Hz, 3 orders of magnitude lower than sensors without hBN.
• Demonstrated a noise equivalent pressure (NEPr) as low as 7.96 × 10^-3 Pa and ultrahigh sensitivity of 1.99 × 10⁶ kPa^-1.
• The sensor achieved a minimum identifiable loading of 2 Pa, a high signal-to-noise ratio (SNR) of 68.76 dB, and 98.1% accuracy in distinguishing liquid concentrations.

Conclusions:

• The developed tactile sensor overcomes limitations in pressure resolution and noise suppression.
• The biomimetic sensor exhibits performance exceeding human skin's sensing threshold, enabling precise tactile recognition and liquid property evaluation.