Electrochemical Approach to Evaluate the Wettability of Rough Surfaces
Bibliographic record
Abstract
The wettability is usually determined based on the optical measurement of the contact angle from a droplet placed on the surface. Theoretical models show that this contact angle depends on the solid-liquid interaction under the droplet, which is optically inaccessible. Here, we present an electrochemical method for evaluating the wetted area under the droplet for a rough surface. The method takes advantage of the electrochemical double layer capacitance, which can be quantified using electrochemical approaches such as cyclic voltammetry. This double layer capacitance is proportional to the ion-accessible solid-liquid interfacial area and therefore can be used as a characterization metric. The experimental approach includes simultaneous measurement of the contact angle along with the capacitance. We have shown the capability of this method for a series of carbonaceous surfaces with varying roughness. The experimental results have been correlated to the Wenzel and Cassie-Baxter wettability theories. This approach is applicable for irregular roughness features and therefore can be used to study the wetting behaviour of different surface structures. We believe this work has significant implications as a tool to characterize and understand the wettability on rough surfaces and also facilitates the development of mechanistic and predictable mathematical models for surface wettability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".