Efficient Low Shear Flow-based Trapping of Biological Entities
Bibliographic record
Abstract
Capturing cells or biological entities is an important and challenging step toward in-vitro studies of cells under a precisely controlled microscale environment. In this work, we have developed a compact and efficient microdevice for on-chip trapping of micro-sized particles. This hydrodynamics-based trapping system allows the isolation of polystyrene micro-particles with a shorter time while inducing a less hydrodynamic deformation and stress on the particles or cells both after and before trapping. A numerical simulation was carried out to design a hydrodynamic trapping mechanism and optimize the geometric and fluidic parameters affecting the trapping efficiency of the microfluidic network. By using the finite element analysis, the velocity field, pressure field, and hydrodynamic force on the micro particles were studied. Finally, a PDMS microfluidic device was fabricated to test the device's ability to trap polystyrene microspheres. Computational fluid analysis and experimental testing showed a high trapping efficiency that is more than 90%. This microdevice can be used for single cell studies including their biological, physical and chemical characterization.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".