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Record W2788150887 · doi:10.1364/oe.26.005300

Escape from an Optoelectronic Tweezer Trap: experimental results and simulations

2018· article· en· W2788150887 on OpenAlexafffund
Shuailong Zhang, A. N. Nikitina, Yujie Chen, Yanfeng Zhang, Lin Liu, Andrew G. Flood, Joan Juvert, M. Dean Chamberlain, Nazir P. Kherani, Steven L. Neale, Aaron R. Wheeler

Bibliographic record

VenueOptics Express · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Toronto
FundersEngineering and Physical Sciences Research CouncilSun Yat-sen UniversityNatural Sciences and Engineering Research Council of CanadaNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsOpticsOptical tweezersTrap (plumbing)Optical forcePhysicsOptoelectronicsMaterials science

Abstract

fetched live from OpenAlex

velocities with nN forces. In this work, we analyze the behavior of particles manipulated by negative dielectrophoresis (DEP) forces in an OET trap. A user-friendly computer interface was developed to generate a circular rotating light pattern to control the movement of the particles, allowing their force profiles to be conveniently measured. Three-dimensional simulations were carried out to clarify the experimental results, and the DEP forces acting on the particles were simulated by integrating the Maxwell stress tensor. The simulations matched the experimental results and enabled the determination of a new "hopping" mechanism for particle-escape from the trap. As indicated by the simulations, there exists a vertical DEP force at the edge of the light pattern that pushes up particles to a region with a smaller horizontal DEP force. We propose that this phenomenon will be important to consider for the design of OET micromanipulation experiments 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 score0.064
Threshold uncertainty score0.495

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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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

Citations37
Published2018
Admission routes2
Has abstractyes

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