Professor Jong-Woong Kim's research team has proposed a new structure to enhance linearity and sensitivity in pressure sensor design, unveiling two types of stretchable sensors. This research is expected to expand the application range significantly by dramatically improving the properties of the device through a simple approach.

Reliable pressure sensors must possess both high sensitivity and excellent linearity within their detection range. The research team designed the sensor structure to ensure that the initial voltage and current values remain low while allowing for linear changes in response to pressure, considering that the sensitivity is calculated based on the ratio of electrical characteristics during initial deformation. Additionally, to enhance the longevity of the device in light of continuous friction and contact, self-healing materials were introduced, enabling all components of the manufactured sensors to be self-healable repeatedly.

To address potential noise issues in the sensor's operating environment, a one-dimensional convolutional neural network (1D CNN) model was incorporated to differentiate subtle signals. Furthermore, the frictional electric pressure sensor equipped with an artificial intelligence model was attached to a glove, successfully detecting electrical signals that vary based on the user's grip strength and timing when holding a baseball, thereby demonstrating its potential as an intelligent sensor.

These two pressure sensors have been published in the international journals Nano Energy (Impact Factor: 16.8) and Advanced Science (Impact Factor: 14.3) in September 2024, acknowledging the credibility of the research results. The main idea of this research, regarding the nonlinear increase in contact area between sensor materials under pressure, was proposed by Su Bin Choi, a doctoral student and first author of the paper. This concept was validated through experiments and simulations, leading to its publication. Professor Jong-Woong Kim's research team continues to conduct studies that strategically enhance properties through structural design and material selection, actively incorporating artificial intelligence models to further expand the functionality of the devices. This is expected to broaden the possibilities for intelligent sensor applications.