The Direct Cause of Amplified Wettability: Roughness or Surface Chemistry?
Bibliographic record
Abstract
Higher contact angles or amplified wettability observed on surfaces of rough solid materials are typically expressed as a function of a physical dimension (roughness factor). Herein, we present a simple experimental approach that demonstrates that roughness may only magnify the inherent surface chemistry that seems to have direct influence on surface wettability. We investigate gradual change in surface chemistry (hydrophobisation) of rough and smooth glass surfaces, from a very low concentration (10−7 M) of dichlorodimethylsilane, DCDMS through various intermediate hydrophilic/hydrophobic states to when the surfaces are maximally hydrophobised with DCDMS at 0.1 M. The wettability of the modified glasses was studied by water contact angle measurements using drop shape analysis system (DSA). The data obtained indicate a deviation from Wenzel model, with the functionalized rough glass surfaces showing higher reactivity towards DCDMS when compared to the smooth glass surfaces, indicating that the two surfaces are not chemically identical. Our study reveals that just like transforming a solid material to powder, a well-divided glass (rough) surface may not only exhibit a greater surface area than the smooth counterpart as rightly predicted by the Wenzel model, but seems to be bloated with functional groups (–OH or –CH3) that can amplify surface interaction when such functional species dominate the solid surface.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".