Acoustic Manipulation of Bio-Particles at High Frequencies: An Analytical and Simulation Approach
Bibliographic record
Abstract
Manipulation of micro and nano particles in microfluidic devices with high resolution is a challenge especially in bioengineering applications where bio-particles (BPs) are separated or patterned. While acoustic forces have been used to control the position of BPs, its theoretical aspects need further investigation particularly for high-resolution manipulation where the wavelength and particle size are comparable. In this study, we used a finite element method (FEM) to amend analytical calculations of acoustic radiation force (ARF) arising from an imposed standing ultrasound field. First, an acoustic solid interaction (ASI) approach was implemented to calculate the ARF exerted on BPs and resultant deformation induced to them. The results were then used to derive a revised expression for the ARF beyond the small particle assumption. The expression was further assessed in numerical simulations of one- and multi-directional standing acoustic waves (SAWs). Furthermore, a particle tracing scheme was used to investigate the effect of actual ARF on separation and patterning applications under experimentally-relevant conditions. The results demonstrated a significant mismatch between the actual force and previous analytical predictions especially for high frequencies of manipulation. This deviation found to be not only because of the shifted ARF values but also due to the variation in force maps in multidirectional wave propagation. Findings of this work can tackle the simulation limitations for spatiotemporal control of BPs using a high resolution acoustic actuation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".