Clear Antismudge Unimolecular Coatings of Diblock Copolymers on Glass Plates
Bibliographic record
Abstract
Two poly[3-(triisopropyloxysilyl)propyl methacrylate]-block-poly[2-(perfluorooctyl)ethyl methacrylate] (PIPSMA-b-PFOEMA) samples and one poly(perfluoropropylene oxide)-block-poly-[3-(triisopropyloxysilyl)propyl methacrylate] (PFPO-b-PIPSMA) sample were synthesized, characterized, and used to coat glass plates. These coatings were formed by evaporating a dilute polymer solution containing HCl, which catalyzed PIPSMA's sol-gel chemistry. Polymer usage was minimized by targeting at diblock copolymer unimolecular (brush) layers that consisted of a sol-gelled grafted PIPSMA layer and an oil- and water-repellant fluorinated surface layer. Investigated is the effect of varying the catalyst amount, polymer amount, as well as block copolymer type and composition on the structure, morphology, and oil- and water-repellency of the coatings. Under optimized conditions, the prepared coatings were optically clear and resistant to writing by a permanent marker. The marker's trace was the faintest on PFPO-b-PIPSMA coatings. In addition, the PFPO-b-PIPSMA coatings were far more wear-resistant than the PIPSMA-b-PFOEMA coatings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 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".