Normal and Lateral Interactions between Thermosensitive Nanoparticle Monolayers in Water
Bibliographic record
Abstract
Static and dynamic interaction forces between two thermosensitive polymeric nanoparticle monolayers grafted onto mica surfaces and immersed in water were studied using a surface forces apparatus. The polymeric nanoparticles (NPs) were made of N,N-diethylacrylamide and had a hydrodynamic diameter of ca. 780 nm at 20 degrees C in aqueous suspension. They were irreversibly grafted onto chemically modified mica surfaces at a constant surface coverage of 2.6 NPs/mum(2). The measured normal forces between two opposing NP monolayers were found to be strongly dependent on the temperature. At temperatures lower than the lower critical solution temperature (LCST), the grafted NPs were swollen, and the normal interaction forces between the two NP monolayers were repulsive. Above the LCST, the NPs collapsed, and attractive forces between the NP layers were measured. The swollen NPs were found to exhibit very low friction forces compared to the collapsed ones. The effect of the sliding velocity on the shear stress was investigated, and the results are in agreement with the so-called adhesive friction model developed for rubber friction. Our results suggest that the water content in the contact area and the interdiffusion of polymer chains are important parameters in determining the friction between polymer-bearing surfaces.
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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) | 0.000 | 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 | 0.001 |
| 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".