Bibliographic record
Abstract
Droplet microfluidics is the science of manipulation of discrete volumes of fluid in the nanoliter to femtolitre range. One of the important applications of droplet microfluidics is cargoencapsulation. The droplets can be loaded with live cells or therapeutics, with applications in single-cell analysis or drug delivery, respectively. The conventional system in droplet microfluidics is water-oil, where droplets of water that encapsulate the cargo are generated in an immiscible phase of oil. Because of the presence of the non-biocompatible oil phase, this traditional two-phase system poses complications for biomedical applications. Over the past decade, aqueous two-phase systems (ATPS) have emerged as an alternative for the conventional water-oil systems. ATPSs are composed of immiscible aqueous solutions of two incompatible polymers or a polymer and a salt. Owing to the aqueous nature of both phases, these systems are biocompatible. In this thesis, I explain the development of a magnetophoretic all-aqueous droplet microfluidic platform by the integration of a commonly used polymeric ATPS and aqueous based ferrofluids—a colloidal suspension of iron oxide nanoparticles. I have applied this platform to two major applications of droplet microfluidics: single-cell analysis and drug delivery. In the first part of my thesis, I show that ATPS droplets can be functionalized with ferrofluid for magnetic droplet manipulation. I show control over droplet manipulation by changing various parameters in the system such as the magnetic field and ferrofluid concentration. Additionally, I modify an existing mathematical model which was previously developed for modeling the deflection of magnetic beads in microfluidics and apply it to my droplet microfluidic system and show a good agreement between the model and the experimental data. Next, I show that by coupling cell-triggered Rayleigh-Plateau instability and diamagnetism in an ATPS droplet microfluidic platform, a pure population of cell-encapsulating droplets can be generated. Such a pure sample of single-cell encapsulating droplets is an essential component of droplet-based single-cell analysis. I generate single-cell encapsulating droplets by passive methods and separate cell-encapsulating droplets from the empty waste droplets by size-based diamagnetic droplet sorting. I show that by changing the magnetic field, the separation efficiency of empty and cell-encapsulating droplets increases, and cells used in such a microfluidic system show a high level of viability. In the last part of my thesis, I use the magnetic droplet microfluidic platform, reported in the first part of the thesis, for the fabrication of magnetic polyelectrolyte microcapsules. Polyelectrolytes are polymers with an electrolyte group. Once dissolved in deionized water, they become charged. Using two oppositely charged polyelectrolytes in the two phases of an ATPS droplet microfluidic system, and by careful tuning of the concentrations, I fabricate magnetic polyelectrolyte microcapsules. Such microcapsules have applications in targeted drug delivery. Therefore, I characterize the delivery profile of these microcapsules using pseudo-drugs with different molecular weights and show triggered release of their cargo by exposing the microcapsules to different stimuli such as osmotic pressure and change in pH. This magnetophoretic all-aqueous droplet microfluidic platform will find applications in single-cell analysis and fabrication of functionalized drug carriers for targeted drug delivery.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".