Resistance of Superhydrophobic Surface-Functionalized TiO2 Nanotubes to Corrosion and Intense Cavitation
Bibliographic record
Abstract
The availability of robust superhydrophobic materials with the ability to withstand harsh environments are in high demand for many applications. In this study, we have presented a simple method to fabricate superhydrophobic materials from TiO₂ nanotube arrays (TNTAs) and investigated the resilience of the materials when they are subjected to harsh conditions such as intense cavitation upon ultrasonication, corrosion in saline water, water-jet impact, and abrasion. The TNTAs were prepared by anodization of Ti foil in buffered aqueous electrolyte containing fluoride ions. The hydrophilic TNTAs were functionalized with octadecylphosphonic acid (ODPA) or 1H, 1H', 2H, 2H'-perfluorodecyl phosphonic acid (PFDPA) to form a self-assembled monolayer on the TNTA surface to produce superhydrophobic ODPA@TNTA or PFDPA@TNTA surfaces. The superhydrophobic ODPA@TNTA and PFDPA@TNTA have contact angles of 156.0° ± 1.5° and 168° ± 1.5°, and contact angle hysteresis of 3.0° and 0.8°, respectively. The superhydrophobic ODPA@TNTA and PFDPA@TNTA were subjected to ultrasonication, corrosion in saline water, and water-jet impact and abrasion, and the resilience of the systems was characterized by electrochemical impedance spectroscopy (EIS), contact angle (CA) measurements, diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS), and field-emission scanning electron microscopy (FESEM). The results presented here show that superhydrophobic ODPA@TNTA and PFDPA@TNTA are robust and resilient under the harsh conditions studied in this work, and indicate the potential of these materials to be deployed in practical applications.
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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) | 0.000 | 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".