Non-fluorinated superomniphobic surfaces
Bibliographic record
Abstract
Superomniphobic surfaces, capable of repelling a wide range of liquids including low-surface-tension oils, rely on a synergy between surface chemistry and texture. For decades, these surfaces have primarily relied on per- and polyfluoroalkyl substances (PFAS) due to their low surface energy and durability. However, the persistence of PFAS in the environment and their toxicological risks have triggered global regulations to phase out their use. This transition presents substantial challenges, especially in sectors such as textiles, food packaging, and electronics, where oil and chemical resistance are essential and fluorine-free alternatives remain limited. While recent research has made progress in developing PFAS-free superhydrophobic surfaces, there remains a significant gap in understanding and designing non-fluorinated superomniphobic systems. This review provides a comprehensive overview of recent strategies for achieving superomniphobicity without fluorinated chemistry. We discuss both texture- and chemistry-based approaches, including coatings made with silica nanoparticles, treated fabrics, and metal oxide nanostructures, as well as coating-free systems that leverage advanced 3D-printing to fabricate doubly and triply re-entrant geometries. Importantly, we highlight limitations in scalability, durability, and liquid-specific performance. By identifying key material and structural design considerations, this review offers a clear perspective on current challenges and emerging opportunities for creating sustainable, high-performance, PFAS-free superomniphobic surfaces.
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 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".