Nano‐Scale Particle Separation with Tilted Standing Surface Acoustic Wave: Experimental and Numerical Approaches
Bibliographic record
Abstract
Abstract To eliminate the precise microchannel alignment requirement for parallel surface acoustic wave (pSSAW) fabrication, a tilted standing surface acoustic wave (tSSAW) is incorporated in many studies recently. In tSSAW, the microchannel is tilted at a specific angle (θ) instead of being parallel to interdigital transducer fingers. This causes the pressure node lines to be formed in the tilted direction to the fluid flow, which leads to better controllability in the separation or manipulation of the particles and higher efficiency. In this study, we developed a three‐dimensional (3D) finite element analysis (FEA) simulation to study the effect of the tSSAW device parameters to maximize its performance in particle separation. The outcomes of the 3D simulation of tSSAW devices indicate that the optimum values of the microchannel tilt angle (θ) and the aperture length must be between 5° ≤ θ ≤ 15° and the same as the microchannel length, respectively. The optimum values are considered in the fabrication of the tSSAW device, where it is tested with 20 μm, 15 μm, and 600 nm polystyrene particles.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".