Porous triboelectric nanogenerator to enhance self-powered load monitoring of total knee replacement
Bibliographic record
Abstract
Developing self-powered, durable pressure sensors for Total Knee Replacement (TKR) enhances longevity, ensures consistent performance, and provides critical post-operative information. This study presents a triboelectric nanogenerator (TENG) integrated into an instrumented knee implant for energy harvesting and pressure sensing. Operating in vertical contact mode, it utilizes porous silicone rubber (SR) dielectric to improve electrical stability, and mechanical durability. The nanogenerator divides the tibial tray into two compartments for load imbalance detection. Tests simulating human walking showed the device withstands forces up to 2200N, generating a maximum of 18μW at 1Hz under harmonic load and a maximum of 7.5μW at 0.8Hz under gait loading with a VIVO joint simulator. The performance of the TENG was stable over 3000 cycles generating a peak-to-peak voltage of 350V . The porous structure enhances charge trapping, energy storage, and system efficiency. The increased power compared to previous work enhances energy harvesting capability and strengthens its potential for self-powered, real-time load monitoring at the knee joint.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".