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High-Performance Transparent, Deformable, and Recoverable Biomimetic Stevia-PVA Hydrogel Triboelectric Nanogenerator
Thien Trung Luu1, Bui Minh Quang1, Toan Minh Pham2
1School of Mechanical Engineering, College of Engineering, Sungkyunkwan University, Suwon, Gyeonggi, South Korea.
Advanced Materials (Deerfield Beach, Fla.)
|April 9, 2026
Summary
A novel stevia/PVA hydrogel enhances flexible sensors for triboelectric nanogenerators (TENGs). This biomimetic material offers superior strength, transparency, and electrical output for self-powered human motion sensing.
Area of Science:
- Materials Science
- Nanotechnology
- Biomedical Engineering
Background:
- Growing demand for portable, self-powered, flexible sensors in IoT and AI.
- Limitations of current triboelectric nanogenerators (TENGs) due to material constraints like 2D fillers affecting transparency and output.
- Need for advanced hydrogels with conductivity, mechanical tunability, and biocompatibility for flexible sensors.
Purpose of the Study:
- To develop a highly transparent, stretchable, and high-output biomimetic hydrogel-based TENG (S-TENG).
- To overcome the limitations of existing TENG materials by utilizing stevia and polyvinyl alcohol (PVA).
- To investigate the potential of this S-TENG as a self-powered sensor for human motion detection.
Main Methods:
- Incorporation of cost-effective biomimetic stevia into PVA hydrogel to enhance cross-linking and crystalline domains via dynamic hydrogen bonding.
- Fabrication of a stevia/PVA hydrogel-based triboelectric nanogenerator (S-TENG).
- Testing of mechanical strength, electrical output, transparency, recyclability, and sensing performance for human motion detection.
- Evaluation of machine learning models, including XGBoost, for sensor data classification.
Main Results:
- The S-hydrogel exhibited 2-5 times greater mechanical strength and 3-8 times higher electrical output compared to existing TENG materials, while maintaining transparency.
- The S-TENG demonstrated excellent recyclability and recovery of voltage output through water-assisted dissolution and re-gelation.
- The sensor showed high sensitivity and a rapid 13-ms reaction time for detecting various human motions.
- The XGBoost machine learning model achieved a classification accuracy of 95.29% for sensor data.
Conclusions:
- The developed stevia/PVA hydrogel-based TENG (S-TENG) offers a promising solution for advanced flexible, self-powered sensors.
- The biomimetic approach enhances material properties, overcoming trade-offs in transparency, output, and sensing capabilities.
- The S-TENG shows significant potential for diverse applications, particularly in human motion monitoring, with high accuracy achieved through machine learning analysis.
