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Record W2029013388 · doi:10.1021/jp803605d

Mechanical and Frictional Properties of Nanoparticle Monolayers Grafted on Functionalized Mica Substrates

2008· article· en· W2029013388 on OpenAlexafffund
Xavier Banquy, X. X. Zhu, Suzanne Giasson

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2008
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicaMonolayerMaterials scienceNanoparticleComposite materialSurface modificationNanotechnologyChemical engineeringEngineering

Abstract

fetched live from OpenAlex

Normal and lateral forces between two opposing monolayers of grafted polymer nanoparticles (NPs) were measured using the Surface Forces Apparatus in a humid atmosphere. The NPs made of N, N-diethylacrylamide and 2-hydroxyethyl methacrylate have a hydrodynamic diameter of ca. 660 nm at 25 degrees C. The effect of surface roughness was studied by creating surface asperities using different NP grafting densities ranging from 0.41 to 2.63 NPs/mum (2). An increase in the NPs grafting density gave rise to an increase in surface roughness and to a deformation of the nanoparticles caused by the lateral pressure between neighboring particles. An elastoplastic behavior of the nanoparticles was observed for large grafting densities, while a purely elastic behavior was observed for small grafting densities. The lateral forces measured between two opposing NP monolayers sliding past each other followed Amontons' law for all grafting densities. The friction coefficient between the surfaces appeared to increase significantly with an increase in surface roughness, which was inherent to an increase in the elastoplastic behavior of the NP monolayers.

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 score0.005
Threshold uncertainty score0.234

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 head0.018
GPT teacher head0.203
Teacher spread0.185 · 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

Citations19
Published2008
Admission routes2
Has abstractyes

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