Native Lignin Migration and Clustering in Wood: Superhydrophobic, Antimold, and Tribonegative Layers for Rain‐Driven Electrification
Bibliographic record
Abstract
Abstract The development of wood‐based materials for energy harvesting, particularly triboelectric nanogenerators (TENGs), has recently attracted significant attention. Traditional strategies for wood‐based TENG primarily rely on delignification to enhance tribo‐positivity, overlooking the intrinsic potential of lignin and necessitating the use of fluoropolymers to maintain performance. In this study, the native lignin within the wood matrix is used to create a superhydrophobic, fully wood‐based tribonegative material (referred to as Lig‐wood), functioning as a liquid–solid triboelectric nanogenerator (L–S TENG) upon contact with water. Through a process of pretreatment and in‐situ regeneration, lignin undergoes migration, assembly, and redistribution within the wood's hierarchical architecture. This results in enhanced hydrophobicity (water contact angle 148°) and efficient surface charge transfer. The morphological and chemical changes significantly boost Lig‐wood's tribonegative performance, achieving a 7.5‐fold increase in voltage and a 6‐fold increase in current compared to unmodified wood. The Lig‐wood powers LEDs and digital timers under simulated rainfall, demonstrating its functionality as green energy harvesting material. Importantly, the surface‐localized lignin imparts self‐cleaning and antimold properties, supporting the potential for long‐term, outdoor use. By leveraging the inherent functionalities of lignin, this approach presents a sustainable strategy for rain‐driven energy harvesting, representing a significant advancement in green and renewable energy technologies.
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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".