On the 15th, the reporter learned from Southern University of Science and Technology that Professor Guo Chuanfei's team from the Department of Materials Science and Engineering at the university has developed a flexible slip sensor that simulates human fingerprint characteristics, which can accurately and accurately distinguish fine texture materials such as cotton, yarn, chemical fiber, wool, etc. This sensor is expected to be used in fields such as humanoid robots, human prosthetics, and tactile virtual reality. The relevant results were published in Nature Communications. There are two different receptors in the human skin that detect static pressure and vibration. These two receptors work together in the human skin, allowing us to perceive various tactile stimuli and respond accordingly. "Guo Chuanfei introduced that through the static pressure receptors in deep skin tissues, humans can sense the hardness and shape of objects, while the vibration receptors in the surface tissue of the skin can quickly sense mechanical stimuli, Then it is converted into neural signals and transmitted to the brain for analysis. It is extremely challenging to imitate the function of the skin, requiring sensors to have both ultra-high sensitivity and ultra-fast response, so that when quickly passing through a large number of small structures, the size, material, and quantity of the structure can be accurately distinguished, "Guo Chuanfei said. Based on this, Guo Chuanfei's team has developed a flexible sliding sensor that simulates human fingerprint features, integrating two types of sensory functions in the skin into a single sensor. Through material and structural innovation, this sensor can distinguish structures of 10 micrometers in size and respond to frequencies up to 400 Hz, which is close to the maximum frequency range that sensory sensors in the skin can sense. Guo Chuanfei introduced that by extracting tactile signals when sensors come into contact with target objects, the research team has constructed a machine learning model and integrated flexible sliding sensors into human hand prostheses. While capturing small tactile signals, prostheses can also recognize 20 common fabrics such as cotton, yarn, synthetic fibers, wool, linen, and seersucker. Through machine learning models, this flexible slip sensor can achieve recognition accuracy of up to 100% at a constant slip speed, even at any stroke speed, reaching around 99%, far higher than the accuracy of 78% of human fingers. Artificial touch has significantly surpassed human touch in some aspects, "Guo Chuanfei said." This flexible sliding sensor may be called an 'electronic skin'. "(Image provided by the interviewee) (Xinhua News Agency)