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Record W2331267504 · doi:10.1021/nl404233z

Quantification of High-Efficiency Trapping of Nanoparticles in a Double Nanohole Optical Tweezer

2014· article· en· W2331267504 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNano Letters · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOptical tweezersTrappingBrownian motionMaterials scienceOptical forceStiffnessLaserLaser power scalingMolecular physicsDielectricTrap (plumbing)OpticsNanoparticleBrownian dynamicsOptical powerOptoelectronicsNanotechnologyChemistryPhysics

Abstract

fetched live from OpenAlex

We measure the dynamics of 20 nm polystyrene particles in a double nanohole trap to determine the trap stiffness for various laser powers. Both the autocorrelation analysis of Brownian fluctuations and the trapping transient analysis provide a consistent value of ∼ 0.2 fN/nm stiffness for 2 mW of laser power, which is similar to theoretical calculations for aperture trapping. As expected, the stiffness increases linearly with laser power. This is comparable to the stiffness obtained for conventional optical traps for trapping, but for ten times smaller dielectric particles and less power. This approach will allow us to quantitatively evaluate future aperture-based optical traps, with the goal of studying the folding dynamics of smaller proteins (∼ 10 kDa) and small-molecule interactions.

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.

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 score0.069
Threshold uncertainty score0.345

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 head0.011
GPT teacher head0.230
Teacher spread0.219 · 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