Artificial Tactile Receptor System for Sensitive Pressure-Neural Spike Conversion.

An artificial tactile receptor is crucial for e-skin in next-generation robots, mimicking the mechanical sensing, signal encoding, and preprocessing functionalities of human skin. In the neural network, pressure signals are encoded in spike patterns and efficiently transmitted, exhibiting low power consumption and robust tolerance for bit error rates. Here, we introduce a highly sensitive artificial tactile receptor system integrating a pressure sensor, axon-hillock circuit, and neurotransmitter release device to achieve pressure signal coding with patterned spikes and controlled neurotransmitter release. Owing to the heightened sensitivity of the axon-hillock circuit to pressure-mediated current signals, the artificial tactile receptor achieves a detection limit of 10 Pa that surpasses the human tactile receptors, with a wide response range from 10 to 5 × 105 Pa. Benefiting from the appreciable pressure-responsive performance, the potential application of an artificial tactile receptor in robotic tactile perception has been demonstrated, encompassing tasks such as finger touch and human pulse detection.

Highly efficient recognition of similar objects based on ionic robotic tactile sensors.

Tactile sensing provides robots the ability of object recognition, fine operation, natural interaction, etc. However, in the actual scenario, robotic tactile recognition of similar objects still faces difficulties such as low efficiency and accuracy, resulting from a lack of high-performance sensors and intelligent recognition algorithms. In this paper, a flexible sensor combining a pyramidal microstructure with a gradient conformal ionic gel coating was demonstrated, exhibiting excellent signal-to-noise ratio (48 dB), low detection limit (1 Pa), high sensitivity (92.96 kPa-1), fast response time (55 ms), and outstanding stability over 15,000 compression-release cycles. Furthermore, a Pressure-Slip Dual-Branch Convolutional Neural Network (PSNet) architecture was proposed to separately extract hardness and texture features and perform feature fusion. In tactile experiments on different kinds of leaves, a recognition rate of 97.16% was achieved, and surpassed that of human hands recognition (72.5%). These researches showed the great potential in a broad application in bionic robots, intelligent prostheses, and precise human-computer interaction.

Flexible Wearable Capacitive Sensors Based on Ionic Gel with Full-Pressure Ranges.

Flexible positive pressure sensors have been studied extensively and have been used in a lot of scenarios. However, negative pressure detection is also in demand in some scenarios, such as fluid mechanics analysis, air pressure sensing, and so on. Flexible wearable sensors that can detect both positive and negative pressures will greatly broaden the application field. In this paper, we report a flexible highly sensitive ionic gel (IG) pressure sensor, which is simple and of low cost to prepare and can reliably detect a large pressure range from -98 to 100 kPa under an atmospheric pressure of about 982 hPa. The IG dielectric layer is composed of polyvinyl alcohol and phosphoric acid with a random microstructure of sandpaper inversion. The sensor exhibits flexibility, cycling stability, and high sensitivity under both negative and positive pressures (S = 84.45 nF/kPa for the negative pressure section, S = 25.61 nF/kPa for the positive pressure section). These sensors could be worn on the body not only to test breathing and pulse but also to measure air pressure for estimating the altitude, showing that the flexible full-pressure sensors have a wider application range in wearable electronics.