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.
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 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) | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".