Molecularly Capped Omniphobic Polydimethylsiloxane Brushes with Ultra‐Fast Contact Line Dynamics
Bibliographic record
Abstract
Droplet friction is common and significant in any field where liquids interact with solid surfaces. This study explores the molecular capping of surface-tethered, liquid-like polydimethylsiloxane (PDMS) brushes and its substantial effect on droplet friction and liquid repellency. By exchanging polymer chain terminal silanol groups for methyls using a single-step vapor phase reaction, the contact line relaxation time is decreased by three orders of magnitude-from seconds to milliseconds. This leads to a substantial reduction in the static and kinetic friction of both high- and low-surface tension fluids. Vertical droplet oscillatory imaging confirms the ultra-fast contact line dynamics of capped PDMS brushes, which is corroborated by live contact angle monitoring during fluid flow. This study proposes that truly omniphobic surfaces should not only have very small contact angle hysteresis, but their contact line relaxation time should be significantly shorter than the timescale of their useful application, i.e., a Deborah number less than unity. Capped PDMS brushes that meet these criteria demonstrate complete suppression of the coffee ring effect, excellent anti-fouling behavior, directional droplet transport, increased water harvesting performance, and transparency retention following the evaporation of non-Newtonian fluids.
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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) | 0.000 | 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".