Hydrophobic and Superhydrophobic Protein-based Materials for Functional Applications
Bibliographic record
Abstract
Next to polysaccharides, proteins are the most abundant natural biomaterials that can be extracted from plant and animal sources. Due to their biocompatibility, sustainability, environmental friendliness and wide range of properties, protein-based materials offer ample scope for the development of new eco-friendly products for various industrial and functional applications. However, proteins are highly hygroscopic and hydrophilic in nature, stemming from the polar functional moieties on the protein structure. The interest in the functionalization or modification of proteins to produce hydrophobic surfaces and interfaces for various engineering applications has increased in recent years. This chapter discusses the functional application of hydrophobic and superhydrophobic protein-based materials and their routes to achieving such properties. A wide range of protein materials derived from various plant and animal resources with different physico-chemical properties that are used to fabricate hydrophobic and superhydrophobic materials are reviewed. Various modification platforms and fabrication methods to obtain superhydrophobic materials are presented. Finally, challenges and future perspectives of protein-based materials for hydrophobic and superhydrophobic applications are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.020 |
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".