Bibliographic record
Abstract
<div>In biomedical research, there is a high demand for tools that provide high precision, costeffective, and portable methodologies for diagnostic and drug delivery purposes. The main focus of this thesis is on ultrasound techniques, where sound waves are employed for conducting in vivo and in vitro tests for different diagnostic and therapeutic applications. First, bubble-mediated ultrasound approaches for imaging are explored, and then, a bubble-free acoustofluidic strategy is proposed for in vitro intracellular delivery applications. </div><div>As a significant component of many ultrasound techniques, microbubbles have been used as contrast agents and for targeted imaging and drug delivery applications. Size, monodispersity, and stability of microbubbles are important characteristics for the effectiveness of these techniques, and therefore, various methods have been developed for producing microbubbles. In the first microfluidic approach, an expansion-mediated breakup regime is proposed that enables a controlled breakup of large bubbles into smaller size microbubbles in a microfluidic device. Also, various population distributions are reported, and the governing dimensionless numbers are identified. In the second approach, by taking advantage of the dynamics of the bubble size variation inside a gas permeable microfluidic device, the shrinkage of large bubbles into smaller size microbubbles is presented. Theoretical modeling and experimental verification are conducted to identify the design parameters governing the final size of the microbubbles. It is also shown that by controlling the mixing ratio of a high-molecular-weight gas with a low-molecular-weight gas, this approach could enable the production of nanobubbles.<br></div><div>An acoustofluidic strategy for probing cellular stiffness and facilitating intracellular delivery is also presented. Acoustic waves are employed to control the oscillations of adherent cells in a microfluidic channel. Novel observations are reported that individual cells are able to induce microstreaming flow when they are excited by controlled acoustic waves in vitro. A strong correlation between cell stiffness and cell-induced microstreaming flow is observed. Also, it is shown that the combined effect of acoustic excitation and cell-induced microstreaming can facilitate the cellular uptake of different size cargo materials. Successful delivery of 500 kDa dextran to various cell lines with unprecedented efficiency in the range of 65–85% in a 20 min treatment is demonstrated.<br></div>
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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".