Long‐Range Hydrophilic Attraction between Water and Polyelectrolyte Surfaces in Oil
Bibliographic record
Abstract
The outstanding water wettability and the capability of polyelectrolyte surfaces to spontaneously clean oil fouling are determined by their wetting mechanism in the surrounding medium. Here, we have quantified the nanomechanics between three types of polyelectrolyte surfaces (i.e. zwitterionic, cationic, and anionic) and water or oil drops using an atomic force microscope (AFM) drop probe technique, and elucidated the intrinsic wetting mechanisms of the polyelectrolyte surfaces in oil and water media. The measured forces between oil drops and polyelectrolyte surfaces in water can be described by the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. Surprisingly, strong long-range attraction was discovered between polyelectrolyte surfaces and water drops in oil, and the strongest interaction was measured for the polyzwitterion. This unexpected long-range "hydrophilic" attraction in oil could be attributed to a strong dipolar interaction because of the large dipole moment of the polyelectrolytes. Our results provide new nanomechanical insights into the development of novel polyelectrolyte-based materials and coatings for a wide range of engineering, bioengineering, and environmental applications.
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) | 0.001 | 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".