Development of microfluidic acoustic flow cytometry based on simultaneous ultrasound backscatter and photoacoustics for micron and sub-micron size objects
Bibliographic record
Abstract
I developed a flow cytometer based on simultaneous detection of ultra-high frequency ultrasound backscatter and photoacoustic waves from individual micron scale objects, such as, cells, microparticles, and microbubbles owing in a microuidic channel. Individual micron scale objects are ow focused through a focal zone, where both ultrasound and laser pulses focus, in a microchannel of a polydimethylsiloxane (PDMS) based microuidic device. At the focal zone, the objects are simultaneously insonified by ultrasound (center frequency 375 MHz) and irradiated by nanosecond laser (532 nm wavelength) pulses. The interactions generate ultrasound backscatter and photoacoustic signals from the individual objects, which are strongly dependent on their size, morphology, and biomechanical properties, such as the Young's modulus, and optical absorption properties. These parameters can be extracted by analyzing the unique spectral features of the detected signals. At frequencies less than 100 MHz, the signals from the micron scale objects do not contain these unique spectral signatures, thus higher frequencies are required. Cell analysis is the main application of interest using the acoustic flow cytometer. Combining ultrasound backscatter and photoacoustics results in sufficient information about a single cell that can be used for single cell analysis and for diagnostics applications. However, the usage of this system is not limited to biological cells. This system can also be used for analyzing individual microbubbles, which are used as ultrasound contrast agents. During my research, a novel microuidic technique is developed to generate microbubbles of desired sizes by shrinking microbubbles from O(100) _m by applying a suitable vacuum pressure. These shrunken bubbles of different sizes can be used as samples to validate the acoustic ow system for microbubble analysis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".