Impact of Sub-Nanoscale Surface Topography on Contact Line Profile: Insights from Coherence Scanning Interferometry
Bibliographic record
Abstract
Despite the importance of the effect of subnanoscale roughness on contact line behavior, it is difficult to directly observe the local behavior of contact lines at the micro- and nanoscale, leaving significant gaps in our current understanding. In this research, we investigate contact line motions and their relationship with nanoscale surface topography using coherence scanning interferometry. Our experiments were conducted on the substrates with different wettability without changing nanoscale surface topography. Titanium dioxide was used as a substrate, the wettability of which was varied under UV-light irradiation. A ridge-like structure with a height of approximately 1 nm was observed to cause contact line deformation toward the droplet side, regardless of the direction of the contact line motion. This was explained in terms of an imbalance in the local capillary pressure at the nanoscale contact line. We also found that the deformation becomes larger on the more hydrophilic surface, which was rationalized by theoretical prediction based on analysis of the work done by the force acting on the contact line and the change in surface free energy associated with the deformation of the liquid/gas interface. Furthermore, it was revealed by contact angle measurements that the maximum pinning forces on a hydrophilic surface were less than half of those on a hydrophobic surface. We attributed the weak pinning force on the hydrophilic surface to cascading depinning, where the initial depinning event triggers a chain reaction of subsequent depinning events, driven by the conversion of excess surface energy to kinetic energy. Our experimental works provide new insights of the relationship between the subnanoscale surface roughness and macroscopic contact line motion.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".