Ocean-Based Triboelectric Nanogenerators: A Defect Engineering Approach on Efficacious Electrodes and Dielectric Materials
Bibliographic record
Abstract
Marine energies, as renewable resources, offer substantial potential for large-scale energy harvesting. In recent years, triboelectric nanogenerators (TENGs) have emerged as a promising technology for applications in energy harvesting, self-powered sensing, control, and instrumentation. This interest is driven by their lightweight, cost-effective design, reasonable efficiency at low frequencies, and simple structure. TENGs have also shown considerable technical advantages in efficiently harvesting low-frequency ocean wave energy. This paper provides a systematic review of the latest developments in ocean energy harvesting with TENGs, analyzing the principles, structures, efficiencies, and performances of various TENG systems. We highlight current challenges and future trends in TENG applications for ocean energy collection. Our overview discusses the significance and mechanisms of energy harvesting using TENGs and explores the classification of optimal electrode materials, emphasizing composite materials for their advantageous effects on triboelectric properties. Additionally, we introduce new defect engineering approaches to enhance TENG performance and examine hybrid systems that integrate piezoelectric and triboelectric concepts. Finally, we address the significant challenges in selecting triboelectric materials, which must be overcome to advance research and development. This review aims to guide the selection and assembly of innovative electrode materials for emerging clean energy sources, with defect characterization tools clarifying the structure-efficiency relationship to support the development of novel materials for future clean water energy solutions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".