Navigation of Self-Propelled Biocatalytic Micro/Nanomotors in Complex Environments
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".