Robust Hydrophobic Rare Earth Oxide Composite Electrodeposits
Bibliographic record
Abstract
Abstract Inspired by the lotus leaf, nonwetting surfaces have drawn widespread attention in the field of surface engineering due to their remarkable water repelling characteristics. There are many applications for these surfaces, for instance, self‐cleaning walls and windows, anti‐icing surfaces, or low drag microfluidic channels. However, the adoption of nonwetting surfaces in large scale industrial applications has been hampered by synthesis techniques that are not easily scalable and the limited long term stability and wear robustness of these surfaces in service. This study demonstrates a simple, low cost, and scalable electrochemical technique to produce robust composite coatings with tunable nonwetting properties. The composite coatings are composed of an ultrafine grain nickel matrix with embedded hydrophobic cerium oxide ceramic particles. A comprehensive characterization is performed, including wetting property measurements, electron microscopy, focused ion beam analysis, hardness measurements, and abrasive wear testing to establish the structure–property relationships for these materials. The ultrafine grain structure of the nickel matrix contributes to the high hardness of the composites. As a result of the bimodal CeO2 particle size, hierarchical roughness is present on the surface of the composite, leading to remarkable nonwetting properties, even after 720 m of abrasive wear.
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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".