Self‐Powered Triboelectric Sensor for Real‐Time, Intelligent Occlusal Force Monitoring
Abstract Occlusal force is a critical biomechanical parameter in orthodontics, implant restoration, and temporomandibular joint evaluation. However, its multidimensional dynamics and the complex intraoral environment pose significant challenges to existing sensing technologies, which often lack the capacity for real‐time, high‐resolution, and non‐invasive monitoring. Here, a self‐powered triboelectric sensor (STS) is introduced for autonomous, 3D occlusal force monitoring. The device features a multilayer soft‐contact architecture comprising a Poly(vinylidene fluoride‐co‐hexafluoropropylene (PVDF‐HFP)/Barium titanate (BaTiO 3 ) composite dielectric layer, flexible polyurethane (PU) foam, and carbon–silicone composite electrodes, enabling conformal integration and precise biomechanical signal acquisition. This configuration enables fully self‐powered, real‐time acquisition of occlusal force magnitude, frequency, and spatial distribution. The conformal soft interface facilitates adaptive sensing of multidimensional force vectors across complex intraoral geometries, allowing high‐fidelity reconstruction of dynamic occlusal mechanics. An optimized multichannel architecture improves spatial resolution and surface conformity, enabling accurate tracking of contact trajectories and force distributions during occlusion. Coupled with artificial intelligence, the system performs real‐time signal analysis and abnormal pattern recognition, achieving classification accuracies of 98.75% for occlusal frequency and 98.71% for bite force magnitude. By integrating triboelectric sensing into a compliant occlusal interface, this platform redefines digital occlusal monitoring and offers a scalable path toward intelligent, data‐driven occlusal healthcare.
Authors
- Tian‐Ling Ren (ORCID: https://orcid.org/0000-0002-7330-0544)
- Di Wei (ORCID: https://orcid.org/0000-0003-2670-6362)
- Bing Han (ORCID: https://orcid.org/0000-0002-5170-8709)
- Xuliang Deng (ORCID: https://orcid.org/0000-0003-1838-6274)
- Zhong Wang (ORCID: https://orcid.org/0000-0002-1454-0601)
- Yan Du (ORCID: https://orcid.org/0009-0007-2290-6873)
- Houfang Liu (ORCID: https://orcid.org/0000-0002-3941-8936)
- Jingkai Song
- Zhiwei Zhang (ORCID: https://orcid.org/0009-0003-5488-8180)
Institutions
- Chinese Academy of Sciences (CN)
- Peking University (CN)
- University of Cambridge (GB)
- National Clinical Research (US)
- Beijing Institute of Nanoenergy and Nanosystems (CN)
- University of Chinese Academy of Sciences (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1002/adfm.202519646
- Citations
- 2
- Primary Topic
- Advanced Sensor and Energy Harvesting Materials
- Type
- article
- Field-Weighted Citation Impact
- 1.35