3D printed CNT/TPU triboelectric nanogenerator for load monitoring of total knee replacement
Bibliographic record
Abstract
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 µ W 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 cm 2 , while the foam CNT/TPU film achieved a higher apparent output power of 6.9 µ W 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 −1 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mtext>⩽</mml:mtext> <mml:mstyle scriptlevel="0"/> <mml:mn>1600</mml:mn> </mml:mrow> </mml:math> N) and 24.60 mV N −1 (> 1600 N) for foam CNT/TPU film, demonstrating the integrated sensor capability for wide-range force sensing on TKR implants. The foam CNT/TPU-based TENG maintained stable performance over 16 000 load cycles, confirming its potential for 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".