Development of a microfluidic device with integrated high frequency ultrasound probe for particle characterization
Bibliographic record
Abstract
A microfluidic flow device incorporating a 200 MHz ultrasound probe has been developed to rapidly characterize micron-sized particles. The device hydrodynamically focuses a particle stream under the ultrasound transducer, where pulse-echo ultrasound is used to probe the passing particles one by one. When the ultrasound wavelength is similar to the particle size, the scattered wave depends strongly on the particle size, and the sound speed and density of the particle and surrounding fluid. Each particle type and size has a unique acoustic signature from which it can be identified. To demonstrate this, polystyrene microbeads with two different sizes were used, 6 or 10 μm. Each particle produced an ultrasound signal, and it was identified as either 6 or 10 μm according to unique features in the ultrasound power spectrum. The ultrasound spectral features agreed with those measured from stationary beads using an acoustic microscope, and also to theoretical predictions. These results show for the first time, a new high-speed method of characterizing micron-sized particles using sound waves with applications towards classifying biological cells.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".