Document Type

Article

Publication Date

6-2025

Keywords

3D printing, Additive manufacturing, Carbon nanotubes, Foam TPU printing, Triboelectric nanogenerator, Smart Knee Implant, Pressure sensor

Abstract

This study presents the development and characterization of a novel triboelectric nanogenerator (TENG) designed as a self-powered sensor for load monitoring in total knee replacement (TKR) implants. The triboelectric layers comprise a 3D-printed thermoplastic polyurethane (TPU) matrix with carbon nanotube (CNT) nanoparticles and kapton tape, sandwiched between two copper electrodes. To optimize sensor performance, the proposed CNT/TPU TENG sensor is fabricated with varying CNT concentrations and thicknesses, enabling a comprehensive analysis of how material composition and structural parameters influence energy harvesting efficiency. The 1% CNT/TPU composite demonstrates the highest power output among the tested samples. The solid CNT/TPU-based TENG generated the apparent output power of 4.1 μW under a cyclic compressive load of 2100 N, measured across a 1.6 GΩ load resistance and over a nominal contact area of 15.9 cm², while the foam CNT/TPU film achieved a higher apparent output power of 6.9 μW measured across a 0.9 GΩ load resistance with the same nominal area. The generated power is sufficient to operate a power management and ADC circuit based on our earlier work. The sensors exhibit a stable open-circuit voltage of 320 V for the foam layer and 275 V for the solid one. Sensitivities are 80.50 mV/N (≤ 1600 N) and 24.60 mV/N (> 1600 N) for foam CNT/TPU film, demonstrating the integrated sensor capability f or wide-range force sensing on TKR implants. The foam CNT/TPU-based TENG maintained stable performance over 16,000 load cycles, confirming its potential f or long-term use inside the TKR. Additionally, the dielectric constant of the CNT/TPU composite was found to increase with increasing CNT concentration. The proposed CNT/TPU TENG sensor offers a broad working range and robust energy-harvesting efficiency, making it appropriate for self-powered load sensing in biomedical applications.

Publisher Attribution

This is the accepted manuscript version of the article to be published in Smart Materials and Structures. DOI: 10.1088/1361-665X/ade1ba

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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