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Record W2052336363 · doi:10.1103/physrevlett.91.016104

Direct Imaging of Nanoparticle Embedding to Probe Viscoelasticity of Polymer Surfaces

2003· article· en· W2052336363 on OpenAlexaff
Jonathan H. Teichroeb, James A. Forrest

Bibliographic record

VenuePhysical Review Letters · 2003
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Dynamics and Properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials sciencePolystyreneNanoparticlePolymerGlass transitionAnnealing (glass)EmbeddingViscoelasticityParticle (ecology)Particle sizeNanotechnologyMolecular physicsChemical physicsComposite materialChemical engineeringPhysics

Abstract

fetched live from OpenAlex

Atomic force microscopy was used to study the embedding of gold nanoparticles into the surface of polystyrene films. The rate of embedding was determined at temperatures near the bulk glass transition temperature by measuring the apparent nanosphere height as a function of annealing time. In particular, relative height measurements of the adhered particles were made at temperatures below the bulk value. In the absence of enhanced surface dynamics or yield processes no embedding is expected to occur for . Measurements on 10 and 20 nm particles both indicated that the particles did embed 3--4 nm into the polymer for . Both the extent and time frame for engulfment appear to be independent of the particle diameter. The results suggest a more mobile surface region on the order of 3--4 nm thick, with a lower glass transition temperature than the bulk.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.277
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), 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

Citations176
Published2003
Admission routes1
Has abstractyes

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