Robust Superamphiphobic Coatings Based on Silica Particles Bearing Bifunctional Random Copolymers
Why this work is in the frame
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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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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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 it