Probing Single-Molecule Adhesion of a Stimuli Responsive Oligo(ethylene glycol) Methacrylate Copolymer on a Molecularly Smooth Hydrophobic MoS<sub>2</sub> Basal Plane Surface
Bibliographic record
Abstract
Molybdenum disulfide (MoS 2 ) has been receiving increasing attention in scientific research due to its unique properties. Up to now, several techniques have been developed to prepare exfoliated nanosize MoS 2 dispersions to facilitate its applications. To improve its desired performance, as-prepared MoS 2 dispersion needs further appropriate modification by polymers. Thus, understanding polymer–MoS 2 interaction is of great scientific importance and practical interest. Here, we report our results on molecular interactions of a biocompatible stimuli-responsive copolymer with the basal plane surface of MoS 2 determined using single molecule force spectroscopy (SMFS). Under isothermal conditions, the single-molecule adhesion force of oligo(ethylene glycol) methacrylate copolymer was found to increase from 50 to 75 pN with increasing NaCl concentration from 1 mM to 2 M, as a result of increasing hydrophobicity of the polymers. The theoretical analysis demonstrated that single-molecule adhesion force is determined by two contributions: the adhesion energy per monomer and the entropic free energy of the stretched polymer chain. Further data analysis revealed a significant increase in the adhesion energy per monomer with a negligible change in the other contribution with increasing salt concentration. The hydrophobic attraction (HA) was found to be the main contribution for the higher adhesion energy in electrolyte solutions of higher NaCl concentrations where the zero-frequency of van der Waals interaction were effectively screened. The results illustrate that oligo(ethylene glycol) methacrylate copolymer is a promising polymer for functionalizing MoS 2 and that one can simply change the salt concentration to modulate the single-molecule interactions for desired applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".