| May 11, 2026 |
Researchers developed a stevia-PVA hydrogel triboelectric nanogenerator with 2-5 times greater mechanical strength and 3-8 times higher electrical output than conventional designs.
(Nanowerk News) A research team from South Korea has built a triboelectric nanogenerator using a stevia-infused hydrogel that surpasses existing designs in mechanical strength, electrical output, and optical transparency.
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The stevia hydrogel triboelectric nanogenerator exploits the natural sweetener’s molecular properties to address several problems that have restricted the practical use of hydrogel-based energy harvesters (Advanced Materials, “High‐Performance Transparent, Deformable, and Recoverable Biomimetic Stevia–PVA Hydrogel Triboelectric Nanogenerator with Machine Learning‐Assisted Motion Recognition”).
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Key Findings
- The stevia-PVA hydrogel triboelectric nanogenerator delivered 2–5 times greater mechanical strength and 3–8 times higher electrical output than conventional devices based on 2D materials, biomaterials, and transparent materials.
- The device held a stable output of approximately 800 V over 16,000 contact-separation cycles with no performance loss after 30 days of storage.
- Paired with the XGBoost machine learning algorithm, the sensor achieved 95.29% accuracy in classifying different human body motions.
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Triboelectric nanogenerators convert mechanical motion into electrical energy through contact and separation between two materials. Hydrogel-based versions are attractive for wearable applications because they can be flexible, stretchable, and transparent. However, conventional hydrogel TENGs have struggled with weak mechanical properties, limited electrical output, and poor transparency, restricting their real-world deployment.
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The team, led by Professor Kyungwho Choi at Sungkyunkwan University’s School of Mechanical Engineering, worked with Professor Jinsoo Kim’s group in the Department of Chemical Engineering at Kyung Hee University. Co-first authors Thien Trung Luu and Bui Minh Quang developed the approach, which mimics biological structures by incorporating stevia, a widely available natural sweetener, into a polymer matrix.
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| Schematic diagram of the structure and motion recognition system of a stevia-enhanced PVA hydrogel-based wearable sensor. (Image: Reproduced from DOI:10.1002/adma.73030, CC BY)
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Stevia’s molecular structure contains abundant hydroxyl groups (-OH). When mixed into polyvinyl alcohol (PVA), these groups strengthen both the hydrogen bond-based crosslinking network and the crystalline domains within the material. This dual reinforcement improves the hydrogel’s mechanical integrity and its ionic conductivity, the property that directly governs electrical output in triboelectric devices.
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The resulting device, called S-TENG, achieved a tensile strength exceeding 25 MPa in the hydrated state and an elongation at break surpassing 510%, while maintaining visible light transmittance above 70%. Compared to conventional TENGs built from 2D materials, biomaterials, and transparent materials, these figures represent a 2–5 fold gain in mechanical strength and a 3–8 fold increase in electrical output.
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Durability testing confirmed that the S-TENG held a stable output of approximately 800 V through 16,000 contact-separation cycles. After 30 days of storage at room temperature, electrical performance showed no degradation. The stevia hydrogel also proved recyclable through a water-assisted dissolution and re-gelation process, retaining an output voltage of roughly 600 V after reprocessing.
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The team demonstrated the S-TENG’s versatility as a self-powered motion sensor by attaching it to the wrist, elbow, knee, finger, and throat. The sensor detected a range of human movements, responding to finger bending in as little as 13 milliseconds. To classify different motion types, the researchers evaluated eleven machine learning models. XGBoost performed best, reaching a classification accuracy of 95.29%.
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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.”
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By combining strong mechanical properties, high electrical output, recyclability, and sensor-grade responsiveness in a single biomass-derived material, the S-TENG addresses key barriers that have kept hydrogel nanogenerators from practical wearable use.
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