Interaction Mechanism between Hydrophobic and Hydrophilic Surfaces: Using Polystyrene and Mica as a Model System
Bibliographic record
Abstract
The interactions between hydrophobic and hydrophilic molecules, particles, or surfaces occur in many biological phenomena and industrial processes. In this work, polystyrene (PS) and mica were chosen as a model system to investigate the interaction mechanism between hydrophilic and hydrophobic surfaces. Using a surface forces apparatus (SFA) coupled with a top-view optical microscope, interaction forces between PS and mica surfaces were directly probed in five different electrolyte solutions (i.e., NaCl, CaCl2, NaOH, HCl, and CH3COOH) of various concentrations. Long-range repulsion was observed in low electrolyte concentration (e.g., 0.001 M) which was mainly due to the presence of microscopic and submicroscopic bubbles on PS surface. A modified Derjaguin-Landau-Verwey-Overbeek (DLVO) theory well fits the interaction forces by taking into account the effect of bubbles on PS surface. The range of the repulsion was dramatically reduced in 1.0 M solutions of NaCl, CaCl2, and NaOH but did not significantly change in 1.0 M HCl and CH3COOH, which was due to ion specificity effect on the formation and stability of bubbles on PS surface. The range of repulsion was also significantly reduced to <20 nm in degassed electrolyte solutions. UV-ozone treatment changed the hydrophobic attraction of the untreated PS-PS system to pure repulsion between untreated PS and treated PS, demonstrating the important role of surface hydrophobicity on the formation and stability of bubbles on substrates. Our results indicate that DLVO forces dominate the interaction between hydrophilic surface (i.e., mica) and hydrophobic polymer (i.e., PS), while the types of electrolytes (ion specificity), electrolyte concentration, degassing, and surface hydrophobicity can significantly affect the formation and stability of bubbles on the interacting surfaces, thus affecting the range and magnitude of the interaction forces.
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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) | 0.001 | 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 | 0.001 | 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".