MétaCan
Menu
Back to cohort
Record W2972656729 · doi:10.1117/12.2528394

Colloidal lithography for trapping 10 nm enzymes

2019· article· en· W2972656729 on OpenAlexaff
Adarsh Lalitha Ravindranath, Mirali Seyed Shariatdoust, Samuel Mathew, Reuven Gordon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsOptical tweezersMaterials scienceTweezersLithographyFinite-difference time-domain methodPlasmonFabricationNanotechnologyNanolithographyAperture (computer memory)TrappingPhotolithographyOpticsCharacterization (materials science)OptoelectronicsPhysics

Abstract

fetched live from OpenAlex

The single-beam gradient force optical tweezers have transformed various fields of scientific research by enabling manipulation and characterization of single molecules. Conventional optical tweezers pose limitations in trapping particles in the sub-Rayleigh regime. These limitations have been overcome with the help of plasmonic nanoapertures like the double-nanohole aperture. A modified colloidal lithography technique has been used in fabrication of double-nanohole apertures achieving dimensions appropriate for trapping single molecules in this regime. This paper demonstrates optical trapping of a single 10 nm enzyme, rubisco, using double-nanohole apertures fabricated using the modified colloidal lithography technique as well as presents the results from transmission characterization of different double-nanohole apertures carried out using the finite-difference time-domain (FDTD) simulations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.219
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicOrbital Angular Momentum in OpticsFrench-language works237,207