Controlled Wetting on Electrodeposited Oxide Thin Films: From Hydrophilic to Superhydrophobic
Bibliographic record
Abstract
We have explored how surface morphology and structure affect wetting properties of electrodeposited oxide thin films notwithstanding chemical modifications. Microstripes of self-assembled monolayers (SAMs) on gold were prepared using a microcontact printing (μCP) technique, which served as molecular templates to guide the electrochemical deposition of zirconia in aqueous solution. The wetting properties of the thus-prepared zirconia oxide thin films are shown to be tunable; i.e., a wide range of wettabilities from hydrophilic to superhydrophobic can be obtained by simply varying the SAM template and the electrodeposition conditions (potential scan rate and number of cycles). In particular, a “two-tier” micro/nanoscale roughness was achieved on the gold substrate patterned with alternating stripes of 1-octadecanethiol and 6-mercapto-1-hexanol SAMs, which leads to a superhydrophobic surface (water contact angle ∼150°). Of great significance is the demonstrated ability herein to convert an intrinsically hydrophilic into a hydrophobic surface by changing the conditions for materials fabrication, which does not involve any chemical modifications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".