Role of surface oxygen-to-metal ratio on the wettability of rare-earth oxides
Bibliographic record
Abstract
Hydrophobic surfaces that are robust can have widespread applications in drop-wise condensation, anti-corrosion, and anti-icing. Recently, it was shown that the class of ceramics comprising the lanthanide series rare-earth oxides (REOs) is intrinsically hydrophobic. The unique electronic structure of the rare-earth metal atom inhibits hydrogen bonding with interfacial water molecules resulting in a hydrophobic hydration structure where the surface oxygen atoms are the only hydrogen bonding sites. Hence, the presence of excess surface oxygen can lead to increased hydrogen bonding and thereby reduce hydrophobicity of REOs. Herein, we demonstrate how surface stoichiometry and surface relaxations can impact wetting properties of REOs. Using X-ray Photoelectron Spectroscopy and wetting measurements, we show that freshly sputtered ceria is hydrophilic due to excess surface oxygen (shown to have an O/Ce ratio of ∼3 and a water contact angle of ∼15°), which when relaxed in a clean, ultra-high vacuum environment isolated from airborne contaminants reaches close to stoichiometric O/Ce ratio (∼2.2) and becomes hydrophobic (contact angle of ∼104°). Further, we show that airborne hydrocarbon contaminants do not exclusively impact the wetting properties of REOs, and that relaxed REOs are intrinsically hydrophobic. This study provides insight into the role of surface relaxation on the wettability of REOs.
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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".