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

To achieve high-precision intelligent tactile recognition and hyperfine operation tasks, tactile sensors need to possess the ability to discriminate minute pressures within the range of human perception. However, due to the lack of methodologies for noise suppression, existing tactile sensing mechanisms are inferior in pressure resolution. In this work, we emulate the structure of biological fingertip Merkel cells to develop a quasi-2D vertical tunneling tactile sensor based on conformal graphene nanowalls-hexagonal boron nitride-graphene (CGNWs-hBN-Gr) van der Waals (vdWs) heterojunctions. Tunneling channel modulation of this heterojunction simulates the ion gating mechanism of piezo (PZ) proteins and greatly reduces the noise power spectral density (PSD) to 2.22 × 10^-24 A²/Hz at 10 Hz, which is 3 orders of magnitude lower than that of the sensor without an hBN layer. The noise equivalent pressure (<i>NEPr</i>) was as low as 7.96 × 10^-3 Pa. Multiscale conformal micro- and nanostructured CGNWs further promote an ultrahigh sensitivity of 1.99 × 10⁶ kPa^-1, and the sensor demonstrates a high signal-to-noise ratio (SNR) of 68.76 dB and a resolution of 1/10,000. The minimum identifiable loading of 2 Pa at a pressure of 20 kPa is less than the sensing threshold value of human skin. An ultraresolution sensor could be used to evaluate different liquid properties by detecting complex hydrodynamic changes during artificial touching of liquids via a fingertip. Combined with the TacAtNet model, this sensor distinguishes between different liquids with a resolution accuracy of 98.1% across five distinct alcohol concentrations.