Hydrogel–colloid interfacial interactions: a study of tailored adhesion using optical tweezers
Bibliographic record
Abstract
Dynamics of colloidal particles adhering to soft, deformable substrates, such as tissues, biofilms, and hydrogels play a key role in many biological and biomimetic processes. These processes, including, but not limited to colloid-based delivery, stitching, and sorting, involve microspheres exploring the vicinity of soft, sticky materials in which the colloidal dynamics are affected by the fluid environment (e.g., viscous coupling), inter-molecular interactions between the colloids and substrates (e.g., Derjaguin-Landau-Verwey-Overbeek (DLVO) theory), and the viscoelastic properties of contact region. To better understand colloidal dynamics at soft interfaces, an optical tweezers back-focal-plane interferometry apparatus was developed to register the transverse Brownian motion of a silica microsphere in the vicinity of polyacrylamide (PA) hydrogel films. The time-dependent mean-squared displacements are well described by a single exponential relaxation, furnishing measures of the transverse interfacial diffusion coefficient and binding stiffness. Substrates with different elasticities were prepared by changing the PA crosslinking density, and the inter-molecular interactions were adjusted by coating the microspheres with fluid membranes. Stiffer PA hydrogels (with bulk Young's moduli ≈1-10 kPa) immobilize the microspheres more firmly (lower diffusion coefficient and position variance), and coating the particles with zwitterionic lipid bilayers (DOPC) completely eliminates adhesion, possibly by repulsive dispersion forces. Remarkably, embedding polyethylene glycol-grafted lipid bilayers (DSPE-PEG2k-Amine) in the zwitterionic fluid membranes produces stronger adhesion, possibly because of polymer-hydrogel attraction and entanglement. This study provides new insights to guide the design of nanoparticles and substrates with tunable adhesion, leading to smarter delivery, sorting, and screening of micro- and nano-systems.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".