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Record W2998539520 · doi:10.3390/app10010394

Optical Trapping, Sizing, and Probing Acoustic Modes of a Small Virus

2020· article· en· W2998539520 on OpenAlexafffund
Jeffrey Burkhartsmeyer, Yanhong Wang, Kam Sing Wong, Reuven Gordon

Bibliographic record

VenueApplied Sciences · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOptical tweezersTrappingMaterials scienceParticle (ecology)Raman spectroscopyPlasmonMolecular physicsOpticsOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Prior opto-mechanical techniques to measure vibrational frequencies of viruses work on large ensembles of particles, whereas, in this work, individually trapped viral particles were studied. Double nanohole (DNH) apertures in a gold film were used to achieve optical trapping of one of the smallest virus particles yet reported, PhiX174, which has a diameter of 25 nm. When a laser was focused onto these DNH apertures, it created high local fields due to plasmonic enhancement, which allowed stable trapping of small particles for prolonged periods at low powers. Two techniques were performed to characterize the virus particles. The particles were sized via an established autocorrelation analysis technique, and the acoustic modes were probed using the extraordinary acoustic Raman (EAR) method. The size of the trapped particle was determined to be 25 ± 3.8 nm, which is in good agreement with the established diameter of PhiX174. A peak in the EAR signal was observed at 32 GHz, which fits well with the predicted value from elastic theory.

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 score0.072
Threshold uncertainty score0.272

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 head0.035
GPT teacher head0.207
Teacher spread0.171 · 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

Citations34
Published2020
Admission routes2
Has abstractyes

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