Recent advances in microfluidic actuation and micro-object manipulation via surface acoustic waves
Bibliographic record
Abstract
The realization of microscale total analysis systems and lab-on-a-chip technologies requires efficient actuation (mixing, pumping, atomizing, nebulizing, driving, etc.) of fluids on the microscopic scale and dexterous manipulation (separation, sorting, trapping, concentration, merging, patterning, aligning, focusing, etc.) of micro-objects (cells, droplets, particles, nanotubes, etc.) in open (sessile droplets) as well as confined spaces (microchannels/chambers). These capabilities have been recently achieved using powerful acoustofluidic techniques based on high-frequency (10-1000 MHz) surface acoustic waves (SAWs). SAW-based miniaturized microfluidic devices are best known for their non-invasive properties, low costs, and ability to manipulate micro-objects in a label-free manner. The energy-efficient SAWs are also compatible with conventional microfabrication technologies. The present work critically analyses recent reports describing the use of SAWs in microfluidic actuation and micro-object manipulation. Acoustofluidic techniques may be categorized according to the use of travelling SAWs (TSAWs) or standing SAWs (SSAWs). TSAWs are used to actuate fluids and manipulate micro-objects via acoustic streaming flow (ASF) as well as acoustic radiation force (ARF). SSAWs are mainly used for micro-object manipulation and are rarely employed for microfluidic actuation. We have reviewed reports of new technological developments that have not been covered in other recent reviews. In the end, we describe the future prospects of SAW-based acoustofluidic technologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".