Hierarchically Textured Oleophobic Internal Coatings that Facilitate Drag Reduction of Viscous Oils in Macroscopic Laminar Flow
Bibliographic record
Abstract
For transportation of hydrocarbon liquids via pipelines, reducing the frictional forces between internal walls and viscous oils through modification of the interfacial surface chemistry and topography represents a key imperative, enabling viscous oil flow at lower temperatures while mitigating the need for diluents. Although drag reduction of aqueous flows in lithographically patterned microchannels has been widely explored, herein drag reduction of oil flows within macroscopic tubing spanning several feet in length is demonstrated. Multiscale texturation is derived from the introduction of micron‐sized pits during electroless deposition of nickel and is augmented by nanoscale texturation derived from the incorporation of polytetrafluoroethylene (PTFE) beads within the coating. Further functionalization with a monolayer of 1 H ,1 H ,2 H ,2 H ‐perfluorooctanephosphonic acid yields a surface that is not wetted by water or viscous oils, yielding 17% drag reduction under laminar flow for castor oil and a slip length that approaches 329 μm. The results demonstrate a promising solution for obtaining robust plastronic architectures embedded within the inner walls of macroscopic tubing. The performance of such coatings is constrained primarily by the robustness of plastrons and molecular properties of the flow liquid with the latter modifying the solid/liquid interface energy as a result of surface adsorption.
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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".