Wearable and flexible electrodes in nanogenerators for energy harvesting, tactile sensors, and electronic textiles: novel materials, recent advances, and future perspectives
Bibliographic record
Abstract
There are numerous drivers in the context of sustainable energy production from ambient mechanical energy sources, such as body motions, due to the increasing world demand for alternative energy. Recent progress has been made in the energy harvesting technologies based on piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs) to convert such ambient biomechanical energy into electricity. The PENGs and TENGs technologies have been successfully utilized to provide sufficient energy for low-power electronic devices, such as biomedical sensors for health monitoring. However, the successful implementations of such technologies, including their electrodes as the critical component of the nanogenerators, require unique properties such as flexibility, wearability, and stretchability. As a result, this review summarizes recent progress on PENGs and TENGs technologies and applications with a focus on new electrode materials that could provide flexibility, wearability, and stretchability capabilities to these types of nanogenerators. This review shed light on the role of wearable electrodes in different applications such as devices with smart tactile sensing mechanisms and electronic textiles . This review also outlines the future prospect and potential direction toward the advancement of such technologies and their performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".