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Record W3213080717 · doi:10.1063/5.0068183

Trapping positions in a dual-beam optical trap

2021· article· en· W3213080717 on OpenAlexafffund
Aidan Rafferty, Thomas C. Preston

Bibliographic record

VenueJournal of Applied Physics · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOptical tweezersTrappingBeam (structure)OpticsTrap (plumbing)PerpendicularRange (aeronautics)Particle (ecology)Optical powerPhysicsMaterials scienceLaser

Abstract

fetched live from OpenAlex

Optical trapping has become an important tool in a wide range of fields. While these traps are most commonly realized using optical tweezers, dual-beam optical traps offer specific advantages for certain experiments. It is commonly assumed that a particle will become trapped midway between the focal points of the two beams. However, this is not always the case. We perform a theoretical and experimental investigation of trapping positions of weakly absorbing, spherical particles in a dual-beam optical trap. We evaluate the effect of offsetting the beams in the direction of propagation and identify four regimes with distinct trapping behavior. The effect of an offset perpendicular to the propagation direction and an imbalance in power between the two beams is also considered. Experiments utilize an aqueous aerosol particle whose size can be readily controlled and monitored over hundreds of nanometers. As such, it serves as an excellent probe of the optical trap. We demonstrate that it is possible to fit the evolution of the particle trapping position in order to determine the position of the particle relative to the focal point of each beam. The results presented here provide key insights into the workings of dual-beam optical traps, elucidating more complex behaviors than previously known.

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 score0.309
Threshold uncertainty score0.491

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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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

Citations17
Published2021
Admission routes2
Has abstractyes

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