Individual nanobubbles detection using acoustic based flow cytometry
Bibliographic record
Abstract
We use a novel acoustic-based flow cytometer to detect individual nanobubbles flowing in a microfluidic channel using high-frequency ultrasound and photoacoustic waves. Each individual nanobubble (or cluster of nanobubbles) flowing through the foci of high-frequency ultrasound (center frequency 375 MHz) and nanosecond laser (532 nm) pulses interacts with both pulses to generate ultrasound backscatter and photoacoustic waves. We use in-house generated nanobubbles, made of lipid shells and octafluoropropane gas core, to detect ultrasound backscatter signals using an acoustic flow cytometer. Nanobubble solutions sorted in size through differential centrifugation are diluted to 1:10,000 v/v in phosphate buffered saline solution to maximize the probability that the detected signals are from individual nanobubbles. Nanobubble populations were sized using resonant mass measurement. Results show that the amplitude of the detected ultrasound backscatter signal is dependent on the nanobubble size. The average amplitude of the ultrasound backscatter signals from at least 950 nanobubbles with an average diameter of 150 nm, 225 nm, and 350 nm was 5.1±2.5 mV, 5.3±2.3 mV, and 6.4±1.8 mV, respectively. Similarly, we detected interleaved ultrasound backscatter and photoacoustic signals from nanobubbles tagged with Sudan Black B dye. The average amplitude of the ultrasound backscatter and photoacoustic signals from these black nanobubbles with an average diameter of 238 nm is 10±11 mV and 54±75 mV, respectively. The presence of the dye on the shell suppressed unique features seen in the ultrasound backscatter from the nanobubbles without dye. At present, there is no robust commercial technique able to analyze the ultrasonic response of individual nanobubbles. The acoustic flow cytometer can potentially be used to analyze physical parameters, such as size and ultrasonic response, of individual nanobubbles.
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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".