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Record W4315874259 · doi:10.1038/s41598-022-27319-z

Complementing two-photon fluorescence detection with backscatter detection to decipher multiparticle dynamics inside a nonlinear laser trap

2023· article· en· W4315874259 on OpenAlexaff
Anita Devi, Sumit Yadav, Arijit K. De

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsUniversity of Alberta
FundersScience and Engineering Research BoardIndian Institute of Science Education and Research MohaliUniversity Grants Commission
KeywordsOptical tweezersBackscatter (email)FemtosecondPhysicsExcitationLaserFluorescencePhotonTrappingTrap (plumbing)OpticsComputer science

Abstract

fetched live from OpenAlex

Using wide-field and point detection modalities, we show how optical trapping dynamics under femtosecond pulsed excitation can be explored by complementing detection of two-photon fluorescence with backscatter. Radial trajectories of trapped particles are mapped from correlated/anti-correlated fluctuations in backscatter pattern whereas temporal evolution of two-photon fluorescence is used to mark the onset of trapping involving multiple particles. Simultaneous confocal detection of backscatter and two-photon fluorescence estimates axial trap stiffness, delineating short-time trapping dynamics. When a second particle is being trapped an oscillatory signal is observed which is due to interference of backscatter amplitudes, revealing inter-particle interactions within the trap. These findings are crucial steps forward to achieve controlled manipulation by harnessing optical nonlinearity under femtosecond pulsed excitation.

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)0.001
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 score0.121
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.253
Teacher spread0.241 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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