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Record W2314056098 · doi:10.1021/nn4044589

Rational Design for the Controlled Aggregation of Gold Nanorods <i>via</i> Phospholipid Encapsulation for Enhanced Raman Scattering

2014· article· en· W2314056098 on OpenAlexaff
Alexander F. Stewart, Anna Lee, Aftab Ahmed, Shell Ip, Eugenia Kumacheva, Gilbert C. Walker

Bibliographic record

VenueACS Nano · 2014
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsNanorodDispersityRaman scatteringPolymerMaterials scienceRaman spectroscopyChemical engineeringSelf-assemblyNanotechnologyDynamic light scatteringColloidSolubilityMoleculeChemistryNanoparticlePolymer chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

This study describes a procedure that found a balance between the ability of polymer-stabilized nanorods (NRs) to self-assemble and the creation of narrow gaps to make reproducibly bright surface-enhanced Raman scattering (SERS) nanorod dimers. NRs were end-functionalized with polymers, which enabled end-to-end self-assembly of NR chains and control over inter-rod separation through polymer molecular weight (MW). We found a way to quench the self-assembly, by phospholipid encapsulation, reducing the polydispersity of the aggregates while rendering them water-soluble. This reduction in polydispersity and preferential isolation of short-chain nanorod species is important for maximizing SERS enhancement from nanorod chains. We prepared NR aggregates that exhibit ∼5-50 times greater SERS intensity than isolated rods (and ∼750× greater than bare dye) depending on inter-rod separation, when using Oxazine 725 reporter molecules. Colloidal stability of NR aggregates and temporal stability of the SERS signal in water were observed for 110 days. With enhanced SERS intensity, water solubility, and stability, these NR aggregates are promising optical probes for future biological applications.

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 score0.418
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.246
Teacher spread0.226 · 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

Citations69
Published2014
Admission routes1
Has abstractyes

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