Hierarchically Engineered Triple‐Defensive Antifouling Coating with Well‐Regulated Structure for Enhanced Wastewater Treatment
Bibliographic record
Abstract
Abstract Intergrading traditional hydrophilic coating with tailored functionalities is critical to enhance its antifouling capabilities. However, designing robust functional antifouling coatings with well‐regulated structures to boost its antifouling performance remains a significant challenge. By mimicking the unique structure of shark skin with super‐hydrophilic “denticle layer” and low‐surface‐energy “mucus layer”, for the first time, a facile strategy is presented to hierarchically engineer a triple‐defensive antifouling coating, which consists of a hydrophilic mineralized catalytic nanoparticle (NP) layer, overlaid with hydrophobic perfluoro‐silane domains (F@NPs), synergistically optimizing its fouling resistance, fouling release, and fouling degradation properties. Force measurements and dynamic simulations demonstrate that molecular‐scale incorporation of perfluoroalkyl chains on coating surface significantly reduces foulant adhesion while preserving hydrophilicity, thereby effectively preventing over 98% of oil contamination, protein adsorption, and bio‐fluid fouling. Its antifouling properties are further enhanced by the unique catalytic self‐cleaning ability, enabling rapid degradation of adsorbed organic compounds and bacteria contact killing. Moreover, F@NPs‐coated membrane achieves a water flux over 4200 L m −2 h −1 bar −1 with flux recovery ratio exceeding 95% for separation of oil‐in‐water emulsions containing bio‐foulants. This study presents an innovative strategy for fabricating robust functional coating with superior antifouling performance for environmental engineering applications.
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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) | 0.001 | 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 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".