Battery electrodes, essential for energy storage, possess pores that heavily influence their mechanical properties based on the level of porosity and the nature of the pores. The irregularities in pore shape, size, and distribution complicate the accurate determination of these properties. While stress-strain measurements can shed light on a material’s mechanical behavior and predict compression limits, the complex structure of the pores poses significant challenges for accurate measurements. In this research, we introduce a simulation-driven approach to derive stress-strain data that considers porosity. By calculating relative density and the rate of volume change under compression based on porosity, and applying pressure, we conducted a parametric study to identify the elastic modulus (E) in relation to the rate of volume change. This information was utilized within a material modeling equation, generating stress-strain (S-S) curves that were further analyzed to replicate the compression behavior of the electrode material. The outcomes of this study are expected to improve the prediction accuracy of mechanical properties for porous electrode materials, potentially enhancing battery performance and refining manufacturing processes.

With the growth of silicon-based semiconductor sensors in the global sensor market, advancements in body motion detection for wearable devices and sustainable health monitoring have accelerated. This has led to a significant attention on various sensors with excellent flexibility and stretchability, such as PDMS, in numerous applications. In this study to adjust the sensitivity of conventional conductive pressure sensors, a porous sponge structure was initially created using a sugar template method. The polymer was prepared with four different ratios (5:1, 10:1, 20:1, 30:1) to achieve varying flexibilities. To ensure conductivity, the sponge was coated using a dip-coating method with a 3wt% CNT solution. The conductive sponges with various ratios were tested for sensitivity, demonstrating characteristics suitable for a wide range of pressure sensing applications.

In this study, we have developed a movable defect detection system based on a vision module with machine-learning algorithm for distinguishing product quality. Machine-learning model determined the results in good or no good through images acquired from the vision module consisting of a camera, processor unit, and lighting. To ensure versatility for use in a variety of settings, we have integrated a robot arm and cart for the movable defect detection system, and the robot arm that adjusts the focus length is made to be able to rotate in all directions. The type of defect was divided into eccentricity defect and printing defect. As a result, it was confirmed that classification accuracy showed 0.9901 in our developed device.

This paper developed a wind triboelectric nanogenerator(TENG) using cubic PTFE model. When the wind is injected, the cube PTFE is scattered inside the cylinder TENG structure and energy is harvested. The TENG structure was designed as a cylinder that allows independent dielectric to rotate well inside. In addition, an inlet and an outlet were made to allow good wind flow. Unlike wind harvesters, where one end is mostly fixed and energy is harvested, the dielectric's motion is freed using independent mode. The electrodes and dielectric materials used Aluminum(Al) and Polytetrafluoroethylene(PTFE). The cube PTFE dielectric contacts/separates the electrode attached to the inner wall of the cylinder along the inner wall of the cylinder. At this time, electricity is generated by the kinetic energy generated by the wind. In this study, the efficiency by the number of Cube PTFE inside the cylinder was compared. The experiment confirmed that as the number of Cube PTFE increases, the power increases, but if the number of Cube PTFE exceeds an appropriate number, the density inside the cylinder increases, interrupting the flow of wind, and thus decreasing the power.

In this study, controlling pattern gap was induced by deformation of the stretchable substrate. The pre-fabricated pattern was made by depositing an Au(gold) on the Si substrate, and PDMS(Polydimethylsiloxane) was used for the stretchable substrate in consideration of surface energy, formability, and viscoelastic characteristics. For uniform deformation of PDMS, it is manufactured in the form of a tensile specimen using a molding method. An external force is applied to the deformable substrate in the uni-axial direction using a self-manufactured JIG, and the Au thin film pattern is transferred to the substrate under tensile state. After that, the external force is removed, and the PDMS specimen is recovered to its initial state. At this time, it can be seen that the pre-fabricated pattern gap is shortened from the initial size due to the viscoelastic properties of the PDMS. As a result, it was characterized to deformation rate of the pattern gap according to elongation rate of PDMS.

This paper is a study to improve the energy harvesting output of a TENG(Triboelectric nanogenerator) driven by wind power using fine PTFE(Polytetrafluoroethylene) flakes. The structure of the nanogenerator was manufactured in the cylindrical structure, Al(Aluminium) was attached to the inner wall of the cylinder and the PTFE flakes were rotated by the wind inside the cylinder. The number of contact and separation motions was increased as there are multiple PTFE flakes, resulting in improvement of the harvesting output. Through this, it was evaluated to the energy harvesting output characteristics according to the change in the number of PTFE flakes. Up to the optimum, the energy collection efficiency shows the linear correlation with the increase in PTFE flakes and decreases after that. As the PTFE flakes are more than the optimum, the lowering in the harvesting output is induced by obstructing the flow of wind inside the cylinder.