Using Microfluidics to Study Magnetotactic Bacteria
Bibliographic record
Abstract
The purpose of this thesis was to investigate the swimming behavior of magnetotactic bacteria 1) in flow conditions and 2) in porous media; and further 3) to exploit their unique characteristics towards bio-actuation of small droplets. Bacteria are found in every habitable niche on Earth. In their planktonic lifestyle they often inhabit dynamic environments where their motility is influenced by flow and the proximity to different surfaces. Recently, considerable interest has been demonstrated in the use of bacteria to perform complex tasks, such as carrying cargo for targeted drug delivery. \nMagnetotactic bacteria (MTB) found in both freshwater and marine environments can orient to, and swim along, the geomagnetic field lines, a behavior called magnetotaxis. While foraging in their native habitats, their ultimate swimming path originates from the competition between magnetotaxis and hydrodynamic influences related to flow and nearby surfaces. MTB have advantages over other bacteria as microbiorobots for controlled transport due to their motility and steerability. However, how MTB interact with complex environments in aquatic environments has remained poorly defined. Therefore, to better exploit the abilities of MTB for in vivo applications, understanding their behavior in relevant environments is crucial. \nBy using microfluidics and microscopy techniques, I have demonstrated in this thesis that magnetotaxis enables directed motion of Magnetospirillum magneticum over long distances in flow conditions relevant to both aquatic environments and biomedical applications. These MTB can overcome higher flow velocities when directed to swim perpendicular to the flow as compared to upstream. In addition, I showed that magnetotaxis enables MTB to migrate effectively through both homogenous and heterogeneous porous micromodels, interacting with obstacles and overcoming tortuous flow fields. These results bring new insight into MTB navigation in environments similar to their natural habitats, and their potential in vivo applications as microbiorobots. Lastly, I have presented a biologically-driven magnetic actuation of droplets on a superhydrophobic surface using MTB. With magnetotaxis for navigation, it is possible to harness MTB to transport microdroplets, thus suggesting their potential for lab-on-a-chip applications.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".