Hopping mechanism of particles and cells escaping from optoelectronic tweezer traps
Bibliographic record
Abstract
Optoelectronic tweezers (OET) is an opto-electro-fluidic micromanipulation technology that uses light-induced dielectrophoresis (DEP) for touch-free actuation of micro-scale objects in physical, chemical and biomedical studies. In this work, we introduce a new method to evaluate the behavior of particles trapped in OET traps and used this technique to study their escape mechanism. Particles experiencing negative DEP were made to move in a circular path on a microscope stage, such that the particles’ velocities and trajectories could be observed under different conditions. At high velocities, particles were observed to escape the trap vertically (into the suspending medium) before settling back onto the surface. Three-dimensional numerical simulations of electrical field distribution indicated that the vertical displacement phenomenon occurs when the particle experiences the strongest DEP force at the boundary of the light pattern, lifting the trapped particle to a region where viscous drag exceeds the local horizontal DEP force, thereby forcing it to escape OET confinement. Similar ‘hopping’ phenomena were also observed for cells and particles of different sizes. We propose that the escape mechanism clarified in this work is a general one for objects manipulated by negative DEP in an OET trap, which will be important to consider in the future design of OET-enabled micromanipulation tools for a wide range of applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".