Robust Antifogging and Antifouling Coating Tailored with Zwitterionic Nanocellulose for Multi‐Functional Applications
Bibliographic record
Abstract
Biofouling often occurs simultaneously with fogging, presenting significant challenges to visibility, safety, and operational efficiency. The development of biocompatible coatings that offer both antifouling performance and stability under fogging conditions is highly sought after. A method to form multifunctional coatings is presented, utilizing a zwitterionic nanocellulose composite material that demonstrates both antifogging and antifouling properties, suitable for application on various surfaces. Cellulose nanofibers are modified with lysine to impart zwitterionic characteristics, enhancing hydrophilicity and antifouling performance. Polyacrylic acid is incorporated as a binder, ensuring tight integration of the zwitterionic cellulose nanofibers, which further improves coating uniformity, stability, and adhesion. The resulting coating exhibits superior antifogging properties, excellent adhesion resistance, and mechanical strength while remaining nontoxic to biological organisms. This coating effectively addresses practical needs in applications such as medical endoscopy, food preservation, and marine antifouling. The research offers new insights and approaches for the development of eco-friendly and transparent protective coatings.
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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) | 0.000 | 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 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".