Dewetting Dynamics of a Solid Microsphere by Emulsion Drops
Bibliographic record
Abstract
A novel micropipet technique was developed to quantify the dewetting dynamics of individual microsphere particles by emulsified viscous crude oil drops in aqueous media. This technique allowed dynamic microscale receding contact angles of water to be measured in situ for solid–oil–water systems. System parameters, including modification of glass microspheres and characteristics of oil drops, were varied to study their effect on dewetting dynamics of the systems. Increasing solvent dosage in viscous oil was found to decrease static receding contact angle of water for clean and bitumen-treated glass surfaces, but showed a negligible effect on static receding contact angle for ethyl cellulose (EC)-treated glass surface. Interestingly, dynamic dewetting behavior exhibited a strong dependence on surface modification and the addition of solvent to viscous oil. No dewetting dynamics was observed for clean hydrophilic glass surface. For bitumen- or EC-treated glass surfaces, more rapid dewetting dynamics of water were determined with increasing addition of solvent to viscous oil. Both de Gennes viscous dissipation hydrodynamic and the Blake/Haynes molecular-kinetic models were developed for the current system to understand the observed dynamic dewetting characteristics.
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 | 0.001 |
| 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".