Active Control over the Wettability from Superhydrophobic to Superhydrophilic by Electrochemically Altering the Oxidation State in a Low Voltage Range
Bibliographic record
Abstract
Fast and reversible active control over the surface wettability of electrodeposited copper is achieved through electrochemical manipulation of the oxidation state. The switchable wettability described in this work allows for facile and precise control over the surface wettability, ranging from superhydrophobic (contact angle about 157°) to superhydrophilic (contact angle less than 10°) with a short response time. The rate of wetting transition and the desired contact angle can be precisely controlled by modulating the magnitude and duration of the applied potential. The wettability alteration is completely reversible when the sample is dried at ambient or heat‐dried at 100 °C. The heat‐drying does not impact the surface composition when compared to the samples dried at room temperature. The surface contains a mixture of CuO and Cu2O as revealed by the surface composition analysis. The mechanism underlying the wetting alteration is based on the Faradaic phase transformation at the surface. An integrated droplet manipulation system is also demonstrated. The single‐step and additive‐free sample preparation is scalable and can be used to design smart surfaces and devices that require control over the wettability (e.g., liquid lenses, microfluidic devices, sensitive particulate matter handling systems, controlled filtration, and biomedical 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 | 0.001 |
| 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".