Removal of living cells from biosensing surfaces in droplet-based microfluidics using surface acoustic waves
Bibliographic record
Abstract
Removal of living biological cells from surfaces is a critical process for many applications in the area of biosensing and lab-on-a-chip. Trypsin is one of the most effective biochemical tools used to cleave the cells proteins that are responsible for bonding cells to surfaces [K. A. Walsh, Meth. Enzymol. 19, 41 (1970)]. We propose a method using Rayleigh-type (20 MHz) surface acoustic wave (SAW)-based mixing [Renaudin et al., Lab Chip 10, 111 (2010)] as an accelerator for trypsin-mediated removal of living cells from surfaces. In the experiments, a 10 μL droplet of Hank’s Balanced Salt Solution (HBSS)-trypsin is placed on a piezoelectric substrate covered with human embryonic kidney cells (HEK293). Using phase contrast microscopy, cells removal time for different acoustic power levels and trypsin concentrations is measured. Results from validation experiments show that a minimum of 180 s is necessary to completely release surface-bonded cells covered by the 10 µL droplet without the use of SAW (negative control). By using microstreaming flow in the droplets generated by the SAW, cells are released from the surface in less than 8 s. This work will contribute to improved lab-on-a-chip devices based on living cell biosensing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".