Self‐Powered Wireless Sensor Node Enabled by a Duck‐Shaped Triboelectric Nanogenerator for Harvesting Water Wave Energy
Bibliographic record
Abstract
This paper presents a fully enclosed duck‐shaped triboelectric nanogenerator (TENG) for effectively scavenging energy from random and low‐frequency water waves. The design of the TENG incorporates the freestanding rolling mode and the pitch motion of a duck‐shaped structure generated by incident waves. By investigating the material and structural features, a unit of the TENG device is successfully designed. Furthermore, a hybrid system is constructed using three units of the TENG device. The hybrid system achieves an instantaneous peak current of 65.5 µA with an instantaneous output power density of up to 1.366 W m −2 . Following the design, a fluid–solid interaction analysis is carried out on one duck‐shaped TENG to understand the dynamic behavior, mechanical efficiency, and stability of the device under various water wave conditions. In addition, the hybrid system is experimentally tested to enable a commercial wireless temperature sensor node. In summary, the unique duck‐shaped TENG shows a simple, cost‐effective, environmentally friendly, light‐weight, and highly stable system. The newly designed TENG is promising for building a network of generators to harvest existing blue energy in oceans, lakes, and rivers.
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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 | 0.000 | 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) | 0.001 | 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".