Substrate Engineering for Durable Omniphobic Liquid‐Like Surfaces
Bibliographic record
Abstract
Abstract The critical role of substrates in enhancing the omniphobicity of liquid‐like surfaces (LLSs) is investigated. To smoothen substrate asperity roughness and graft polydimethylsiloxane (PDMS)‐based LLSs, sol–gel silica utilizing 1,2‐bis(triethoxysilyl)ethane (BTESE) and incorporating non‐ionic surfactants is developed, achieving crack‐free thicknesses of 28 µm, a ≈100 × improvement over synthesis using tetraethoxysilane. Substrate surface silanol density significantly impacts the final wetting behavior, and a silanol density ≤ 1.43 nm − 2 is inadequate for achieving minimal contact angle hysteresis (CAH). Oxygen plasma treatment of BTESE silica increases the silanol density to 2.28 nm − 2 , resulting in omniphobicity, including a 2° CAH with water and < 1° CAH for decane, octane, and toluene. The mechanical properties of substrates influence the abrasion resistance of PDMS‐LLSs, with two distinct abrasion damage modes identified: 1) removal or cleavage of PDMS chains, which is independent of the substrate material, and 2) substrate scratching without removing the surrounding PDMS, leading to a rapid loss of liquid‐repellency, particularly on softer silica substrates. A bilayer silica system formed from two different organosilica precursors is then developed to smoothen rough substrates without sacrificing durability. These findings extend the application of PDMS‐LLSs to a range of substrates and offer insights in engineering damage‐tolerant LLSs.
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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".