Bibliographic record
Abstract
Liquid drops wetting a surface kept in ambient air has been widely studied over the last few decades due to its manifold applications in technology and industry. However, for a surface kept submerged under-liquid, such wetting processes have been studied to a lesser extent. Understanding how a liquid drop interacts with \na surface in the presence of another liquid medium is pivotal towards growing applications in marine ecosystem, environmental effects of oil-spills, advanced manufacturing techniques like immersion lithography, etc. It also poses the challenging issues of liquid-liquid displacement and contact line dynamics. The present study delineated some fundamentals of under-liquid wettability like coalescence of two sessile drops on an under-liquid substrate, spreading of liquid drops on an underliquid substrate and drop interaction with submerged micro-patterned substrates.Through relevant theoretical analysis as well as experimentation, it was found that the existing theories of drop interaction with a surface in air are inadequate when a surrounding liquid medium is considered, and needs to be modified bringing into effect key parameters of the surrounding medium, such as its density and viscosity. Consideration of a surrounding liquid medium also allows to provide a unifying framework to study such wetting processes. For coalescence and spreading, a universal behavior was observed in terms of the initial fast wetting regime inherent to such processes, and the notion of coalescence-spreading analogy was found acceptable to describe such phenomenon. However, for under-liquid wetting signature of micro-patterned substrates, a non-universal behavior was observed which indicates the need of newer theoretical approach to better understand wetting phenomenon on such under-liquid surfaces.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".