Reversible Self-Assembled Monolayers with Tunable Surface Dynamics for Controlling Cell Adhesion Behavior
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Cells adhering onto surfaces sense and respond to chemical and physical surface features. The control over cell adhesion behavior influences cell migration, proliferation, and differentiation, which are important considerations in biomaterial design for cell culture, tissue engineering, and regenerative medicine. Here, we report on a supramolecular-based approach to prepare reversible self-assembled monolayers (rSAMs) with tunable lateral mobility and dynamic control over surface composition to regulate cell adhesion behavior. These layers were prepared by incubating oxoacid-terminated thiol SAMs on gold in a pH 8 HEPES buffer solution containing different mole fractions of ω-(ethylene glycol) 2-4 - and ω-(GRGDS)-, α-benzamidino bolaamphiphiles. Cell shape and morphology were influenced by the strength of the interactions between the amidine-functionalized amphiphiles and the oxoacid of the underlying SAMs. Dynamic control over surface composition, achieved by the addition of inert filler amphiphiles to the RGD-functionalized rSAMs, reversed the cell adhesion process. In summary, rSAMs featuring mobile bioactive ligands offer unique capabilities to influence and control cell adhesion behavior, suggesting a broad use in biomaterial design, tissue engineering, and regenerative medicine.
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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 | 0.000 |
| 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".