Colloidal particle-hydrogel interfacial interactions
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».