Fluorinated Raspberry-like Polymer Particles for Superamphiphobic Coatings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Raspberry-like (RB) polymer particles were prepared, fluorinated, and cast onto glass plates to yield highly water- and oil-repellant superamphiphobic particulate coatings. To procure the RB particles, glycidyl-bearing 212 and 332 nm particles (abbreviated as s-GMA and l-GMA, respectively) were first prepared via surfactant-free free radical emulsion polymerization. Reacting the glycidyl groups of the l-GMA particles with 2,2'-(ethylenedioxy)bis(ethylamine) (EDEA) produced large amine-functionalized particles (l-NH2). The l-NH2 particles were then reacted with an excess of the s-GMA particles to create RB particles. For surface fluorination, the residual glycidyl groups of the smaller s-GMA particles surrounding the central l-NH2 core of the RB particles were first converted to amino groups by reaction with EDEA. The purified amino-bearing particles were subsequently reacted with an excess of a statistical copolymer poly(2-(perfluorooctyl)ethyl methacrylate-co-glycidyl methacrylate), P(FOEMA-co-GMA). Casting these particles onto glass plates yielded particulate films that exhibited static contact angles of 165 ± 2°, 155 ± 3°, 152 ± 4°, and 143 ± 1° and droplet rolling angles of <1 °, <1 °, 7 ± 2°, and 13 ± 2° for water, diiodomethane, corn-based cooking oil, and hexadecane droplets, respectively. These results demonstrated that this practical bottom-up approach could be used to produce superamphiphobic coatings.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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