Controlled Shrinkage of Microfluidically Generated Microbubbles by Tuning Lipid Concentration
Bibliographic record
Abstract
Monodisperse microbubbles with diameters less than 10 μm are desirable in several ultrasound imaging and therapeutic delivery applications. However, conventional approaches to synthesize microbubbles, which are usually agitation-based, produce polydisperse bubbles that are less desirable because of their heterogeneous response when exposed to an ultrasound field. Microfluidics technology has the unique advantage of generating size-controlled monodisperse microbubbles, and it is now well established that the diameter of microfluidically made microbubbles can be tuned by varying the liquid flow rate, gas pressure, and dimensions of the microfluidic channel. It is also observed that once the microbubbles form, the bubbles shrink and eventually stabilize to a quasi-equilibrium diameter, and that the rate of stabilization is related to the lipid solution. However, how the lipid solution concentration affects the degree of bubble shrinkage, and the stable size of microbubbles, has not been thoroughly examined. Here, we investigate whether and how the lipid concentration affects the degree of microbubble shrinkage. Namely, we utilize a flow-focusing microfluidic geometry to generate monodisperse bubbles, and observe the effect of gas composition (2.5, 1.42, and 0.17 wt % octafluoropropane in nitrogen) and lipid concentration (1-16 mg/mL) on the degree of microbubble shrinkage. For the lipid system and gas utilized in these experiments, we observe a monotonic increase in the degree of microbubble shrinkage with decreasing lipid concentration, and no dependency on the gas composition. We hypothesize that the degree of shrinkage is related to lipid concentration by the self-assembly of lipids on the gas-liquid interface during bubble generation and subsequent lipid packing on the interface during shrinkage, which is arrested when a maximum packing density is achieved. We anticipate that this approach for creating and tuning the size of monodisperse microbubbles will find utility in biomedical applications, such as contrast-enhanced ultrasound imaging and ultrasound-triggered gene delivery.
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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) | 0.000 | 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".