Navigation of Self-Propelled Biocatalytic Micro/Nanomotors in Complex Environments
Notice bibliographique
Résumé
Artificial micro/nanomotors are a class of active matter that can convert various forms of energy into sufficient kinetic energy to overcome Brownian motion and result in self-propulsion. These motors, extensively investigated as substitutes for passive particles, hold promising applications in environmental monitoring, biosensing, and as next-generation drug carriers. Here, three different types of artificial micro/nanomotors are investigated in regards of their motion properties across various environments.<br/>First, using a layer-by-layer assembly method, manganese dioxide nanosheets are coated on the entire surface or one side of the motor in order to obtain homogeneous motors and Janus-shaped motors, respectively. Upon exposure to 300 mM hydrogen peroxide as fuel, Janus-shaped motors demonstrate directional motion in cell media, reaching a maximum speed of 50 μm s-1. In contrast, homogeneous motors cannot surpass Brownian motion. This difference in mobility generates attention to one of Janus-shaped motors' main benefits in design. Moreover, the Janus-shaped motors, owing to their mobility, demonstrate the ability to effectively detoxify the culture cell media near cells assaulted with hydrogen peroxide. This crucial characteristic significantly enhances the cell survival rate, as supported by statistical evidence. In comparison to homogeneous motors, Janus-shaped motors prove superior in both movement and detoxification capabilities.<br/>Second, the objective is to devise a motor utilizing collagen or gelatine as fuel and collagenase as a power unit, transforming it into a potent carrier for drug delivery across biological barriers like the extracellular matrix. For that, different surface immobilized collagenase-based motors are assembled. Subsequently, the motion properties of these motors within collagen fibre networks are examined, with a focus on the effects of the geometric parameters (i.e., size and morphology) of the motors, the composition of the core particles, and the density of collagen fibre networks on the motor velocity. In low-viscosity environments, motors made of 500 nm-diameter polystyrene core particles can achieve an average speed of up to 30 μm s-1. However, with an increase in core size and fibre density or upon replacing the core with a silica-based one, the average speed diminishes by 2-3 times. Subsequently, gelatine is used as a continued model for the extracellular matrix. The 500-nm motor with a silica core exhibits an average speed of 13 μm s-1 in low-viscous gelatine, highlighting the impact of the motor mass on speed when compared to a polystyrene core motor of the same size (~28 μm s-1). At the same time, the motors show a decreasing trend in average velocity with both increasing the core particle size and the gelatine viscosity.<br/>Third, to increase the collagenase loading capacity of the silica particle motor, poly(2-(diethylamino)ethyl methacrylate) polymer brushes are grown on the surface of the motor and collagenase is deposited on the brushes. The size (or mass) dependence of motor speed proves significant in low-viscosity gelatine environments but becomes negligible in high-viscous gelatine. Specifically, the velocities of polymer brush containing collagenase motors with diameters of 500 nm and 1 μm in low-viscous gelatine are ~33 μm s-1 and 18 μm s-1, respectively. Remarkably, these two motors exhibit an identical speed (~18 μm s-1) in the high-viscous gelatine. Lastly, the 500-nm motors are encapsulated in giant unilamellar vesicles made of lipids, to determine their ability to cross the lipid membrane. The random constrained motion of the motors in gelatine loaded giant vesicles is comparable to the velocity measured in a homogeneous environment with a similar viscosity. However, the motors lack the ability to traverse the lipid bilayer of the giant vesicles. Finally, smaller motors of ~100 nm in diameter are fabricated and exposed to 3D cell aggregates to assess their capacity to actively penetrate the aggregates. Notably, they demonstrate effective penetration into the 3D cell aggregates. <br/>In summary, this thesis explores the navigation capabilities of three self-propelled artificial nanomotors in complex and uneven environments. All in all, it establishes a fundamental understanding of their mobility in non-uniform biologically related environments, such as cell media and the extracellular matrix.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».