Colloidal particle-hydrogel interfacial interactions
Bibliographic record
Abstract
Colloidal adhesion to soft, aqueous interfaces or inside bulk soft materials has drawn much attention recently. Micro and nanoparticle-based drug delivery to soft tissues demands decent knowledge about the colloidal dynamics on soft, sticky tissues. In biomedical engineering, understanding the interfacial deformation and flow properties of soft scaffolds interacting with micron and nano sized cells and drug carriers is of great importance without which proper cell-specific substrates cannot be designed. Moreover, perceiving the mechanism of receptor-ligand type interaction at soft interfaces, biofouling, and cell attachment in microfluidic devices demands characterizing the soft adhesion at a single microparticle scale. In this thesis, silica microspheres are used as sensors reflecting the interaction between the soft materials, including lipid bilayers (mimicking cell surface), grafted polymers (mimicking polymer-coated drug carriers), and hydrogels (mimicking extracellular matrices and soft tissues), through their self-diffusion on a coated flat substrate. The results showed that colloidal particles underwent Brownian or non-Brownian (anomalous) motion. The anomalous interfacial dynamics were observed when entanglement, such as the grafted-polymer interaction with hydrogels, was possible. The dynamics of an optically trapped silica microsphere with various coatings in a polyacrylamide (PA) hydrogel vicinity were resolved in nanometer scale using back-focal-plane interferometry position detection, combined with optical tweezers, from which micro-scale rheological behavior of the interface was ascertained. Various microspherical probes on PA substrates with a controlled stiffness have been used in the absence of external forces (passive microrheology) or under an external oscillatory shear (active microrheology). Passive interfacial microrheology results were interpreted using two approaches, namely a diffusion coefficient-binding stiffness method, developed in this work, and the well-known viscoelasticity formalism of Mason (1995). The former furnished substrate elasticity-correlated binding stiffness, and the later suggested that despite significant interfacial attachment, bare and lipopolymer (DSPE-PEG2k)-doped lipid bilayer (DOPC)-coated silica microspheres (termed as DSPE-coated particles) experience almost a thousand times lower elastic stress compared to bulk inclusions. The softer the PA substrate, the lower adhesion stiffness (i.e., an higher long-time Brownian position variance). Interestingly, coating microspheres with phospholipid fluid membranes (DOPC) eliminated the interfacial attachment to PA substrates, despite attractive electrostatic forces, independent of the gel elasticity, providing a non-adhesive probe to characterize fluid properties in the gel contact proximity. Active microrheology with bare, DOPC-coated, and DSPE-coated silica microspheres on PA gels furnished interfacial viscoelastic properties versus PA elasticity, external shear rate, and optical restoring force exerted on the trapped particles, which suggested a substrate stiffness-dependent decrease in the binding stiffness when increasing the exerted force on the particle. The interfacial adhesion phase diagrams were constructed within the Cole-Cole (Nyquist analysis) formalism. The results may help to design biocompatible wet glues for advanced biomedical applications, such as non-intrusive in vivo stitching.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".