Robust Superamphiphobic Coatings Based on Silica Particles Bearing Bifunctional Random Copolymers
Bibliographic record
Abstract
Reported herein is the growth of bifunctional random copolymer chains from silica particles through a "grafting from" approach and the use of these copolymer-bearing particles to fabricate superamphiphobic coatings. The silica particles had a diameter of 90 ± 7 nm and were prepared through a modified Stöber process before atom transfer radical polymerization (ATRP) initiators were introduced onto their surfaces. Bifunctional copolymer chains bearing low-surface-free-energy fluorinated units and sol-gel-forming units were then grafted from these silica particles by surface-initiated ATRP. Perfluorooctyl ethyl acrylate (FOEA) and 3-(triisopropyloxy)silylpropyl methacrylate (IPSMA) were respectively used as fluorinated and sol-gel-forming monomers in this reaction. Hydrolyzing the IPSMA units in the presence of an acid catalyst yielded silica particles that were adorned with silanol-bearing copolymer chains. Coatings were prepared by spraying these hydrolyzed silica particles onto glass and cotton substrates. A series of four different copolymer-functionalized silica particles samples bearing copolymers with similar FOEA molar fractions (fF) of ~80% but with different copolymer grafting mass ratios (gm) that ranged between 12.3 wt% and 58.8 wt%, relative to silica, were prepared by varying the polymerization protocols. These copolymer-bearing silica particles with a gm exceeding 34.1 wt% were used to coat glass and cotton substrates, yielding superamphiphobic surfaces. More importantly, these particulate-based coatings were robust and resistant to solvent extraction and NaOH etching thanks to the self-cross-linking of the copolymer chains and their covalent attachment to the substrates.
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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".