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Nanoring formation via<i>in situ</i>photoreduction of silver on a virus scaffold

2016· article· en· W2546527858 on OpenAlexafffund
Serene S. Bayram, Omar Zahr, Julia Del Re, Amy Szuchmacher Blum

Bibliographic record

VenueNanotechnology · 2016
Typearticle
Languageen
FieldMaterials Science
TopicDiatoms and Algae Research
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthFonds Québécois de la Recherche sur la Nature et les TechnologiesCanada Foundation for Innovation
KeywordsNanoringMaterials scienceNanoparticleNanotechnologyPlasmonSilver nanoparticleMagnetismOptoelectronics

Abstract

fetched live from OpenAlex

The fabrication of plasmonic nanorings remains of substantial interest by virtue of their enhanced electric and magnetic response to light fields which can be subsequently exploited in diverse applications. Scaling down the size of nanorings holds promise in creating artificial magnetism at wavelengths matching the solar spectrum. Nanosized bioscaffolds can be utilized to tackle the challenge of size reduction of metallic rings owing to their miniature features as well as their well-known biomineralization capacity. Herein, we use the tobacco mosaic virus coat protein as a command surface to grow and assemble silver nanoparticles into sub-30 nm rings. The versatility of TMV allows the formation of both solid rings and rings consisting of discrete nanoparticles that are characterized by UV-vis and TEM. The pH-dependent coulombic surface map along with the annular geometry of the protein aggregate allow the generation of rings with or without a central nanoparticle. Our silver rings are believed to be the smallest to date, and they can offer a test material for existing theories on metallic nanorings of this heretofore unreached size scale.

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 score0.037
Threshold uncertainty score0.369

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 head0.015
GPT teacher head0.261
Teacher spread0.246 · 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

Citations15
Published2016
Admission routes2
Has abstractyes

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