Mechanical structure design: A survey on modern triboelectric nanogenerators
Bibliographic record
Abstract
Recent advancements in the utilization of triboelectric nanogenerators (TENGs), which convert kinetic energy into electrical energy, present a suitable solution for efficient energy harvesting and self-powered sensing applications. Despite the continuous development of various materials for triboelectric applications, a significant challenge persists in selecting an appropriate mechanical structure and designing a mechanism for effectively capturing this renewable energy resource. This review paper explores the critical role of mechanical components and structures in the performance of TENGs. A thorough review of recent literature indicates a concerted effort to explore different structural designs and their impact on energy harvesting and self-powered sensing capabilities. The mechanical structure emerges as a key factor in the triboelectric generation process, significantly influencing efficiency by facilitating optimal material separation and friction. Moreover, force transmission mechanisms within the mechanical structure are important for maximizing energy extraction. The impact of design on structural reliability is essential for adapting to diverse environmental conditions, ensuring the necessary flexibility for practical real-world applications. A robust mechanical structure ensures stability, essential for sustainable and reliable nanogenerator operation. This systematic review emphasizes the pivotal role of mechanical structure design in advancing the field of TENGs, providing insights into key factors influencing their performance and paving the way for future developments in energy harvesting technologies. • Offering comprehensive review on triboelectric mechanical structures and components. • Exploring efficient triboelectric series and geometries for versatile TENG uses. • Conducting in-depth analysis of motion mechanisms impacting TENG performance and durability. • Reviewing recent TENG advancements in energy harvesting and sensing applications. • Elaborating on different issues and challenges in TENG designs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".