Bibliographic record
Abstract
<p>Microfluidics plays a pivotal role in advancing lab-on-a-chip technologies, offering the potential to create bulk nanobubbles for therapeutic applications. However, the utilization of microfluidics for this purpose has been limited. This dissertation introduces innovative microfluidic techniques aimed at producing uniform bulk nanobubbles and sub-10 μm microbubbles.</p> <p>In the first dissertation objective, I harnessed microbubble shrinkage to create uniform sub-10 μm microbubbles via microfluidic flow-focusing. Microbubbles typically contract as gas escapes and dissolves after formation. I investigated the influence of gas type, concentration, lipid content, and initial microbubble size on shrinkage. Results showed that adjusting lipid concentration and initial microbubble diameter allows precise control over final bubble size. Results from the first objective highlight a key finding: the size range of biomedical microbubbles is no longer limited by the microfluidic orifice width. Instead, the concentration of lipids can now be employed to regulate their diameters. I discovered that using the lipid solution in this thesis requires microbubbles' initial diameter under 10 μm to produce bulk nanobubbles.</p> <p>The second dissertation objective involved optimizing microfluidic flow-focusing to create bulk nanobubbles. This new technique produced nanobubbles with diameters of 170 – 260 nm. Notably, a critical initial microbubble diameter was identified, below which shrinkage behavior significantly altered. Larger initial microbubbles shrank to a stable size as seen earlier. However, those smaller than the critical diameter unexpectedly contracted into nanobubbles, much smaller than anticipated.</p> <p>For the third objective of this dissertation, I presented a new microfluidic technique to generate bulk nanobubbles whose concentrations were more than 23 times higher than those of the flow- focusing bulk nanobubbles. This microfluidic method can generate bubbles with starting diameters of 2 – 10 μm (below the critical diameter discovered in objective 2) that reduce to 100 – 140 nm following shrinking. Bulk nanobubbles created using this approach are more monodisperse than those produced using the flow-focusing method. I expect the new techniques in this thesis to enable controlled and uniform microfluidic production of bulk nanobubbles and microbubbles for diverse applications.</p>
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".