Triboelectric Nanogenerators Enable Multifunctional Ice Accretion, Melting, and Interfacial Fracture Detection
Bibliographic record
Abstract
Triboelectric nanogenerators (TENGs) have significant potential to perform as sensors or compact electric power generators through the production of electrical charge during the frictional interactions between two dissimilar materials, such as liquids impacting solids. However, whether phase transitions generate a triboelectric response is not known. This study investigates the occurrence of triboelectrification during the water-ice phase transition using TENGs for real-time ice detection on critical engineering surfaces such as aircraft, wind turbine blades, and vehicles. TENGs are fabricated using aluminum electrodes and either polyethylene, silicone, or polytetrafluoroethylene as the dielectric. The freezing of water and the melting of ice are found to generate triboelectric current only during motion of the contact line, and the presence of ice can lessen additional charge transfer during continuous ice accretion. Further, ice type (rime versus glaze) can be differentiated during accretion by the initial transferred charge and how quickly the signal plateaus. It is observed that mechanical de-icing generates triboelectric charges that are proportional to the de-icing force, and this allows for the identification and quantification interfacial fracture mechanisms such as stress-controlled, toughness-controlled, and cavitation-controlled de-bonding. A prototype ice sensor is validated on a flying drone exposed to simulated rain under icing conditions, where it is able to detect both icing and de-icing in flight. The TENGs exhibited a signal-to-noise ratio as high as 83 dB, highlighting triboelectricity as a novel, real-time, and energy-efficient solution for ice detection and protection systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".