Fabrication of Hydrophobic Lignin-Based Films through Tandem Chemical Modification and Plasma Treatment
Bibliographic record
Abstract
Fully renewable hydrophobic materials offer a promising solution to addressing environmental challenges. Lignin, a relatively underutilized renewable polymer that naturally exhibits hydrophobicity, shows potential as a blend material in various applications. However, current approaches using technical lignin as a primary component or additive in renewable film manufacturing often rely on nonrenewable, external hydrophobic agents. Here, we developed a tandem strategy to create a fully renewable, hydrophobic lignin-based film. First, lignin was esterified by incorporating long-chain palmitic groups to enhance its hydrophobicity. A poly[( R )-3-hydroxybutyrate] (PHB) film containing 20% palmitoylated lignin demonstrated improved hydrophobicity, with the water contact angle (WCA) increasing from 75.4 to 106.7°. To further enhance hydrophobicity, the film underwent oxygen plasma treatment, which introduced macroscopic surface roughness in the form of “nanoforests.” This treatment significantly increased the WCA to 139°, confirming the effectiveness of the tandem strategy for producing hydrophobic 2D materials. Molecular dynamics simulations revealed that the C16 chain in palmitoylated lignin created a more compact complex with PHB through strong van der Waals interactions and optimized hydrogen bonding, suggesting potential for developing high-lignin-content films. This work demonstrates a facile approach for fabricating fully renewable, hydrophobic composite films without the need for external materials.
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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".