From common natural sweetener to high-performance energy material

Professor Kyungwho Choi’s team (co-first authors: Thien Trung Luu and Bui Minh Quang) of the School of Mechanical Engineering at Sungkyunkwan University(SKKU), in collaboration with Professor Jinsoo Kim’s team in the Department of Chemical Engineering at Kyung Hee University, proposed a strategy that simultaneously overcomes the limitations of conventional hydrogel-based triboelectric nanogenerators (TENGs) — namely low output performance, poor mechanical strength, and insufficient transparency — by utilizing biomimetic stevia.

By incorporating stevia into polyvinyl alcohol (PVA), the abundant hydroxyl groups (-OH) simultaneously reinforced the hydrogen bond-based crosslinking structure and crystalline domains, dramatically improving both mechanical strength and ionic conductivity.

As a result, the stevia-PVA hydrogel TENG (S-TENG) demonstrated approximately 2–5 times greater mechanical strength and 3–8 times higher electrical output compared to conventional TENGs based on 2D materials, biomaterials, and transparent materials, while maintaining over 70% visible light transmittance. The tensile strength exceeded 25 MPa (in the hydrated state) with an elongation at break surpassing 510%.

Furthermore, the research team demonstrated that the S-TENG maintained stable output (~800 V) through 16,000 contact-separation cycles, and confirmed no degradation in electrical output after 30 days of storage at room temperature. The stevia hydrogel can also be recycled via a water-assisted dissolution and re-gelation process, retaining a high output voltage of approximately 600 V after recycling, thus demonstrating its potential as an eco-friendly material. 

In addition, the research team attached the S-TENG to various body parts — including the wrist, elbow, knee, finger, and throat — and utilized it as a self-powered sensor for detecting diverse human body motions. The rise time in response to finger bending was as fast as 13 ms, and among eleven machine learning models evaluated for motion classification, the XGBoost algorithm achieved the highest classification accuracy of 95.29%.

Professor Kyungwho Choi, the corresponding author, stated: "It is highly significant that we successfully developed a hydrogel electrode derived from biomass-based stevia that simultaneously improves transparency, mechanical performance, and electrical output while also securing recyclability. We plan to continue research on applying this technology to a wide range of fields, including IoT-based wearable devices, rehabilitation monitoring, and intelligent human-machine interfaces."

This research was supported by the 4th BK21 Future HRD Education and Research Center for Human-Centered Convergence Mechanical Solution and by the Korea government (MSIT). The results were published in Advanced Materials (IF 26.8, within the top 3% of JCR) in April 2026. In addition, this paper was selected for the inside front cover of Advanced Materials.

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