Petal-Inspired Superhydrophobic Paper from Lignin-Cellulose for Sustainable Raindrop Energy Harvesting
Bibliographic record
Abstract
Raindrop kinetic energy represents a sustainable and distributed energy source. However, conventional materials for harvesting rain energy often depend on petroleum-based and fluorinated compounds, which raise environmental concerns and suffer from interfacial instability. This study proposes a fluorine-free, petal-inspired superhydrophobic interface fabricated from cellulose paper and esterified modified lignin. By incorporating long-chain alkyl groups to enhance lignin’s the hydrophobicity, and constructing multiscale rough structures on the paper surface, the interfacial drainage behavior and charge conversion capability are synergistically improved. The resulting paper-based superhydrophobic interface exhibits excellent water repellency (contact angle of 163°), mechanical durability, and self-cleaning properties, effectively resisting droplet retention and particle adhesion under diverse environmental conditions. The developed raindrop-driven triboelectric nanogenerators (R-TENGs) achieves an output voltage of 4.7 V and a current of 470.1 nA under raindrop impact, successfully powering an LED device. This material shows potential for application in rooftop or outdoor environments, where it can sustainably harvest raindrop energy to supply power for low-consumption electronics. Furthermore, this work provides a fluorine-free strategy that integrates the functionalization of natural materials with biomimetic structural design, offering a sustainable and environmentally friendly route for efficient raindrop energy harvesting and the development of next-generation paper-based flexible electronics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".