A Triboelectric Nanogenerator (TENG) for Pipeline Monitoring
Bibliographic record
Abstract
In this study, we present triboelectric nanogenerators (TENGs) for vibrational energy harvesting in oil pipelines. The generators are designed to replenish the batteries of leak detection sensor, thereby increasing their lifespan and reducing the need for maintenance. The TENGs were designed to harvest energy from a 12-inch diameter pipeline, vibrating with at 32 Hz. Three alternative materials were used for the upper plate of a 4 × 4 cm TENG, namely Polytetrafluoroethylene (PTFE), unstructured polydimethylsiloxane (PDMS) and structured PDMS. Tests revealed that the unstructured PDMS TENG outperformed the PTFE TENG and generated 47.6 μW of power. Structuring the PDMS by patterning open channels on half of the surface increased the output power to 200.0 μW. When the spring constant of the structured PDMS TENG was optimized, the output power was further increased to 297.7 μW. These results demonstrate that structured PDMS shows promise in triboelectric energy harvesting, specifically because it can be surface-modified using inexpensive techniques that do not require a clean room.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".