Biomimetic Plastronic Surfaces for Handling of Viscous Oil
Bibliographic record
Abstract
Unconventional deposits such as extra heavy oil and bitumen represent a steadily increasing proportion of extracted fuels. The rheological properties of viscous crude oil represents a formidable impediment to their extraction, transportation, and processing and have necessitated considerable retooling and changes to process design. In this work, we demonstrate that highly textured inorganic substrates generated by depositing ZnO nanotetrapods onto periodically ordered stainless steel mesh substrates exhibit viscous oil contact angles exceeding 150° as well as enable the facile gliding of viscous oil. Such functionality is derived as a result of multiscale texturation and porosity achieved within these substrates, which are characterized by trapping of plastronic air pockets at the solid/liquid interface. Further reduction of the surface energy has been achieved by constituting a helical highly ordered self-assembled monolayer of a perfluorinated phosphonic acid on the ZnO surfaces. Such structures are strongly ejected upon immersion in water with water contact angles in excess of 160°. The functionalized substrates demonstrate remarkable superoleophobic behavior toward viscous crude oil with contact angles reaching 156° and are furthermore stable to temperatures of 290 °C. The remarkable results evidenced here hold promise for deployment of these constructs in the handling of viscous oil in order to reduce losses associated with transportation from railroad cars, pipelines, and other oil-handling equipment.
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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".