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Record W2889716812 · doi:10.1117/12.2318836

Hopping mechanism of particles and cells escaping from optoelectronic tweezer traps

2018· article· en· W2889716812 on OpenAlexaff
Shuailong Zhang, Aaron R. Wheeler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMechanism (biology)OptoelectronicsOptical tweezersTrappingMaterials sciencePhysicsOpticsBiologyQuantum mechanics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.178
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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