Autophobic polydimethylsiloxane nanodroplets enable abrasion-tolerant omniphobic surfaces
Bibliographic record
Abstract
• Grafted PDMS chains exhibit a liquid component in QCM-D measurements with prolonged polymerization. • Autophobic dewetting is observed and leads to cluster formation on grafted PDMS. • Cyclic PDMS grafting method effectively controls cluster growth. • Omniphobic properties of grafted PDMS persist after extensive abrasion due to clusters. Polydimethylsiloxane (PDMS) brushes showcase unparalleled omniphobic properties, repelling a diverse spectrum of liquid droplets. However, PDMS brushes exhibit limited physical durability due to their unique chain conformation, especially when subjected to mechanical abrasion. Here we report the unexpected repellency of PDMS brushes towards their own PDMS chains, i.e., like-vs-like repulsion or autophobicity, and show how the interplay between PDMS brush structure and autophobic behavior can be tuned to design damage-tolerant omniphobic surfaces. A detailed examination of grafted PDMS chain growth using difunctional chlorosilanes from the vapor phase revealed autophobic PDMS chain dewetting during synthesis. Architecturally designed PDMS brushes that favor incremental chain cluster growth demonstrate tolerance towards abrasion, where fractured chains can re-bond to the surface, resulting in extended resistance to abrasion. Tribology experiments confirm a decreasing coefficient of friction and lower contact angle hysteresis after mechanical wear. The tailored autophobicity of PDMS brushes results in surfaces that durably maintain omniphobicity, repelling low surface tension liquids after one thousand cycles of abrasion.
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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) | 0.001 | 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".