Comparative study on protocols to prepare slippery liquid-like PDMS coatings - pitfalls and time-savers
Bibliographic record
Abstract
Slippery covalently attached liquid surfaces (SCALS, also called quasi-liquid surfaces, liquid-like surfaces, and slippery omniphobic covalently attached surfaces) have recently emerged as a new family of materials with many useful properties --- such as droplet-shedding, anti-icing, and anti-fouling --- and can serve as model systems in studies of wetting, evaporation, and self-assembly phenomena. They are made of nano-thin layers of polymers or oligomers that are liquid at ambient temperature and covalently attached to a smooth substrate. Herein we lay out protocols for preparation of the most common SCALS system: polydimethylsiloxane (PDMS) bound to silica surfaces via silane chemistry. The apparent simplicity of these layers and their methods of preparation is misleading, and obtaining reproducible results requires careful control of the reaction parameters. Exact details of the synthetic methods for SCALS determine the observed results, and reporting them is required for reproducibility and to advance understanding of the field. Here a range of synthetic methods used in the literature were reproduced in three different laboratories across the world, their comparative advantages and disadvantages discussed, and resulting SCALS characterised. For each synthetic method the key parameters that contribute to their performance and ease of reproducibility were identified and optimised.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".