A simple acoustofluidic chip for microscale manipulation using evanescent Scholte waves
Bibliographic record
Abstract
Acoustofluidics is acknowledged as a powerful tool offering a contactless and label-free manipulation of fluids, micro-beads, and living cells. To date, most techniques rely on the use of propagating acoustic waves and take advantage of the associated acoustic radiation force in standing or progressive fields. Here, we present a new approach based on the generation of an evanescent acoustic field above a substrate. This field is obtained by means of subsonic interfacial waves giving rise to a well-defined standing wave pattern. By both imaging and probing the evanescent acoustic field, we show that these interfacial waves are guided waves known as quasi-Scholte acoustic waves. Scholte waves present very interesting features for applications in acoustofluidics. Namely, they confine the acoustic energy to the vicinity of the surface, they are nearly lossless and thus can propagate over long distances along the substrate, and finally they do not require any particular material for the substrate. With a very simple and low-cost device we show several examples of applications including patterning lines or arrays of cells, triggering spinning of living cells, and separating plasma from RBC in a whole blood microdroplet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".