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Record W2883289351

Dynamic Deformation of Soft Particle in Dual-trap Optical Tweezers

2010· article· en· W2883289351 on OpenAlexaff
Sébastien Rancourt-Grenier, Ming-Tsu Wei, Jar-Jin Bai, Arthur Chiou, Paul Bareil, Pierre-Luc Duval, Yunlong Sheng, Québec Canada

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsOptical tweezersElasticity (physics)Deformation (meteorology)MechanicsPhysicsStress (linguistics)Mie scatteringClassical mechanicsCauchy stress tensorMaxwell stress tensorOpticsMaterials scienceScatteringLight scatteringThermodynamics
DOInot available

Abstract

fetched live from OpenAlex

The dual-trap optical tweezers is used for deforming and measuring the elasticity of soft particles suspended in aquatic buffer. In the experiment the trapped particle was the Red Blood Cell (RBC) swollen to a spherical form. The 3D radiation stress distribution was computed by ray tracing, the generalized Lorentz-Mie scattering theory with the Tmatrix and the FDTD via the Maxwell stress tensor. The 3D deformation of the cells was then computed with the elastic membrane theory. The calculated deformation can fit to experimental data resulting in cell’s elasticity coefficient. The static approach is valid only for small deformation (5-10%). For a large deformation such as that of the RBC, we consider re-distribution of the radiation stress on the morphologically deformed cell. This stress re-distribution in turn induces subsequent deformation of the deformed cell and new stress re-distribution. The recursive process continues until a final equilibrium state is achieved. This iterative computation was implemented with the finite element method using the COMSOL TM multi-physics models. The deformation results can fit to the experimental data for cell’s deformation up to 20%.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.492

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 head0.004
GPT teacher head0.230
Teacher spread0.225 · 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 designTheoretical or conceptual
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
Published2010
Admission routes1
Has abstractyes

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