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Record W2133314765 · doi:10.1039/c2nr31832f

Salt-mediated kinetics of the self-assembly of gold nanorods end-tethered with polymer ligands

2012· article· en· W2133314765 on OpenAlexaff
Kun Liu, Cristina Resetco, Eugenia Kumacheva

Bibliographic record

VenueNanoscale · 2012
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNanorodSelf-assemblyIonic bondingElectrolyteSolventPolymerSalt (chemistry)ChemistryKineticsMetal ions in aqueous solutionMoleculeSolubilityAqueous solutionMetalIonNanoparticleChemical engineeringNanotechnologyMaterials scienceOrganic chemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Studies on the self-assembly of metal nanoparticles (NPs) in the presence of ions are motivated by the biosensing applications of NP clusters and the capability to control the morphology of clusters of oppositely charged NPs. The effect of ions has been explored for the self-assembly of metal NPs capped solely with ionic ligands, whereas, in general, the surface of NPs can be coated with a mixture of ligands interacting with each other by non-electrostatic forces. In the present work, we examined the kinetics of self-assembly of gold nanorods capped with a mixture of low-molecular weight ionic molecules and nonpolar polymer ligands. We show that in contrast with earlier reports on the effect of electrolytes on NP self-assembly, the driving force for the accelerated self-assembly of nanorods is the reduction in polymer solubility in the presence of ions, rather than the screening of the electric double layer of the charged ligands. The reported results are important for NP self-assembly occurring in mixed solvents, in which attraction forces between nonpolar ligands are governed by the balance between solvent-solvent and solvent-salt interactions. Furthermore, the addition of salts can be used to increase the rate of nanorod self-assembly, which, otherwise, is an intrinsically slow process.

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 score0.012
Threshold uncertainty score0.252

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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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

Citations35
Published2012
Admission routes1
Has abstractyes

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