Droplet Triboelectrification on Liquid‐Like Polymer Brushes
Bibliographic record
Abstract
Abstract Solid–liquid triboelectric nanogenerators offer promising energy harvesting and sensing capabilities, yet the role of wettability parameters in governing triboelectrification remains underexplored. Here, the triboelectrification of water droplets on liquid‐like polymer brush‐coated surfaces with varying chemical compositions and contact angle hysteresis is explored. Triboelectrification signals are measured as droplets slide across spatially distributed electrodes; high‐speed imaging correlates the current output with droplet motion. By varying droplet velocity, travel distance, and electrode distribution, an optimal balance is identified for maximizing signal output, with measured currents ranging from ≈2–21 nA for polyethylene glycol, polydimethylsiloxane, and perfluoropolyether brush‐coated surfaces. The effect of changing the contact area is also examined by compressing and decompressing droplets between two polymer brush‐coated surfaces, where the contact area is expanded up to 10 times. This enables precise control over the rate of contact area change, which is found to exhibit the strongest influence on triboelectrification signals, yielding current peaks exceeding 300 nA. As a potential application, electrode patterning is combined with wettability channels to enable mechanical pressure sensing, where increasing pressure triggers contact with multiple electrodes, generating current signals ≈20 nA. This work highlights the role of interfacial dynamics and surface chemistry in shaping triboelectric output.
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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".