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Record W2033365691 · doi:10.1115/imece2010-38781

Colloidal Interactions for Nanopatterned Surfaces Based on Surface Element Integration (SEI) Approach

2010· article· en· W2033365691 on OpenAlexaff
Amir Ehsan Hosseini, Subir Bhattacharjee, Eric M.V. Hoek

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Alberta
Fundersnot available
Keywordsvan der Waals forceAttenuationMaterials scienceSurface roughnessSurface finishElectrostaticsColloidRough surfaceSubstrate (aquarium)Electrostatic interactionSurface (topology)Lattice (music)Molecular physicsChemical physicsCondensed matter physicsNanotechnologyOpticsComposite materialChemistryGeometryPhysicsPhysical chemistryGeology

Abstract

fetched live from OpenAlex

In this study, van der Waals and electrostatic interaction energies on a nanopatterned rough surface was investigated. Surface element integration method (SEI) was applied to determine the interaction between a nanostructured substrate and an infinite flat plate. Hemispherical protrusions or depressions were mathematically generated on a square lattice to represent the rough surface. The size of the asperities and the pitch (separation) between their centers were varied. From the above calculations, we have analyzed the coupling between the range of the interactions and the roughness features of the substrate by comparing the ratios of the rough surface to smooth surface interaction energies per unit area. At small separations, the rough surface van der Waals interaction is seriously attenuated in the presence of protruding asperities. This attenuation is less pronounced for depressions. The attenuation of the van der Waals interaction due to asperities diminishes at large separations. In contrast, attenuation of the electrostatic interaction is independent of the separation.

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 categoriesInsufficient payload (model declined to judge)
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 score0.236
Threshold uncertainty score0.999

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)0.0020.000

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 head0.027
GPT teacher head0.281
Teacher spread0.254 · 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.

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

Citations2
Published2010
Admission routes1
Has abstractyes

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