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Record W4415618718 · doi:10.1002/smll.202507076

Extended Plasmonic Nanostructures Templated by Tobacco Mosaic Virus Coat Protein

2025· article· en· W4415618718 on OpenAlexafffund
Ismael Abu‐Baker, Alexander Al‐Feghali, Elliot Zolfaghar, Gangamallaiah Velpula, Artur Biela, Steven De Feyter, Jonathan G. Heddle, Gonzalo Cosa, Amy Szuchmacher Blum

Bibliographic record

VenueSmall · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsMcGill University
FundersAcademic Computer Centre Cyfronet, AGH University of Science and TechnologyNatural Sciences and Engineering Research Council of CanadaVlaamse regeringMcGill UniversityInfrastruktura PL-GridKU LeuvenCentre québécois sur les matériaux fonctionnelsFonds Wetenschappelijk Onderzoek
KeywordsNanorodNanostructurePlasmonCoat proteinNanoparticleNanoscopic scaleNanometreMetamaterial

Abstract

fetched live from OpenAlex

Optical and magnetic metamaterials possess interesting properties that cannot be achieved with conventional materials. However, there is currently no synthetic method offering both scalability and nanometer spatial precision. Biotemplating is a promising technique that has the potential to organize nanoscale components with high precision while being scalable and low-cost. Here, we demonstrate a versatile template using hexahistidine-tagged tobacco mosaic virus coat protein. The protein self-assembles into disks, which further assemble into extended nanostructures under mild conditions. Large sheets with either hexagonal or square packing and core-shell nanorods are formed, and gold nanoparticles are attached to the disks within each nanostructure to form assemblies of nanoparticle rings.

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 categoriesInsufficient payload (model declined to judge)
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 score0.189
Threshold uncertainty score0.997

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)0.0040.000

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 spread0.213 · 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.

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
Published2025
Admission routes2
Has abstractyes

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