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Record W4299426271 · doi:10.17615/r1gh-6765

Structural Transitions in Nanoparticle Assemblies Governed by Competing Nanoscale Forces

2020· article· en· W4299426271 on OpenAlexfundno aff
Eugenia Kumacheva, Anna Klinkova, Rachelle M. Choueiri, Michael Rubinstein, Héloïse Thérien‐Aubin

Bibliographic record

VenueFigshare · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsnot available
FundersDivision of Materials ResearchDivision of ChemistryNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthNational Science Foundation
KeywordsNanoscopic scaleNanoparticleNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

Assembly of nanoscale materials from nanoparticle (NP) building blocks relies on our understanding of multiple nanoscale forces acting between NPs. These forces may compete with each other and yield distinct stimuli-responsive self-assembled nanostructures. Here, we report structural transitions between linear chains and globular assemblies of charged, polymer-stabilized gold NPs, which are governed by the competition of repulsive electrostatic forces and attractive poor solvency/hydrophobic forces. We propose a simple quantitative model and show that these transitions can be controlled by the quality of solvent, addition of a salt, and variation of the molecular weight of the polymer ligands.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.998

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.2000.003

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 head0.036
GPT teacher head0.239
Teacher spread0.203 · 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; both teacher heads agree on what is shown here.

Study designObservational
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
Published2020
Admission routes1
Has abstractyes

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