Droplet Motion on Designed Microtextured Superhydrophobic Surfaces with Tunable Wettability
Bibliographic record
Abstract
Superhydrophobic surfaces have shown promising applications in microfluidic systems as a result of their water-repellent and low-friction properties over the past decade. Recently, designed microstructures have been experimentally applied to construct wettability gradients and direct the droplet motion. However, thermodynamic mechanisms responsible for the droplet motion on such regular rough surfaces have not been well understood such that at present specific guidelines for the design of tunable superhydrophobic surfaces are not available. In this study, we propose a simple but robust thermodynamic methodology to gain thorough insight into the physical nature for the controllable motion of droplets. On the basis of the thermodynamic calculations of free energy (FE) and the free-energy barrier (FEB), the effects of surface geometry of a pillar microtexture are systematically investigated. It is found that decreasing the pillar width and spacing simultaneously is required to lower the advancing and receding FEBs to effectively direct droplets on the roughness gradient surface. Furthermore, the external energy plays a role in the actuation of spontaneous droplet motion with the cooperation of the roughness gradient. In addition, it is suggested that the so-called "virtual wall" used to confine the liquid flow along the undesired directions could be achieved by constructing highly advancing FEB areas around the microchannels, which is promising for the design of microfluidic systems.
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) | 0.000 | 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".