Self‐Powered Wireless Monitoring of Obstacle Position and State in Gas Pipe via Flow‐Driven Triboelectric Nanogenerators
Bibliographic record
Abstract
Abstract Triboelectric nanogenerator (TENG) has attracted increasingly attention in a new energy field. However, it is still a huge challenge for TENG to transmit electricity or be self‐powered sensing without hard wires, which can reduce the efficiency of power generation and further cause inconvenience for connecting device. Here, a flow‐driven wireless TENG for efficient power transmission, ultrasensitive distance sensing, and obstacle monitoring in the gas pipe is reported. The wireless TENG can generate a voltage/current signal of about 121.0 V/4.4 µA at the receiving distance of 1.5 cm with a wind speed of 18.0 m s−1. When the distance is increased to 10.0 cm, the output voltage can be decreased to 8.0 V with a sensitivity of 92% owing to the decrease of induced charges on receiving electrodes. The relationship that the output voltage decreases with increasing the receiving distance has been confirmed, suggesting the possibility of the wireless TENG as a self‐powered distance sensor. Moreover, by installing several wireless TENGs uniformly in a gas pipe, the position and specific placement state of an obstacle in gas pipe can be effectively monitored with the self‐powered way.
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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) | 0.000 | 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".