Modifying Base Metal Substrates to Exhibit Universal Non‐Wettability: Emulating Biology and Going Further
Bibliographic record
Abstract
Abstract The design and fabrication of surfaces that are not wetted either by water or oil holds much significance for the energy infrastructure, particularly where corrosion represents a significant problem and cleaning, maintenance, and repair of containers, pipelines, and processing equipment is difficult or poses safety hazards. Nature has numerous examples of surfaces that resist fouling by repelling liquid (especially water) droplets. Designing a surface that is not wetted by low‐surface‐tension liquids such as hydrocarbons is a considerably more difficult task that is beyond the capabilities of most natural systems since the cohesive forces within such liquids are low and most interfacial interactions induce the spreading of oil droplets. A combination of hierarchical texturation, reentrant curvature, and low surface energy is thought to be necessary to design omniphobic surfaces. However, such surfaces are often constructed from polymers and thus prone to thermal degradation. In this contribution, we illustrate the modular design and development of a biomimetic architecture incorporating micro‐ and nanoscale texturation on etched carbon steel. Etching of the steel substrate endows microscale roughness; the substrate is further coated with ZnO nanotetrapods to define nanoscale texturation and further modified to expose pendant fluorous groups. The highly textured substrates exhibit simultaneous superhydrophobic and superoleophobic behavior. The utilization of ZnO nanotetrapods with protruding arms gives rise to a nanotextured morphology regardless of the specific orientation of the nanostructures and allows for the trapping of air pockets, thereby suspending liquid droplets as per the Cassie–Baxter mode. The textured ceramic/metal surfaces are stable up to high temperatures and are well adhered to the metal substrate upon application of a conformal amorphous SiO 2 coating. The incorporation of multiple design elements—microscale roughness, nanotexturation, a “cementing” layer, and surface modification with low‐energy pendant perfluorinated chains—provides considerable versatility and tunability for specific liquid‐handling conditions. The strategy described here is generalizable to other modes of texturation and surface modification and can be broadly adapted to prevent wettability of a surface by a specific liquid, thereby allowing for protection of components exposed to corrosive fluid environments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".