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Record W1987484645 · doi:10.1063/1.3479003

Ordering and single-file diffusion in colloidal systems

2010· article· en· W1987484645 on OpenAlexaff
Salvador Herrera-Velarde, Adalberto Zamudio‐Ojeda, Ramón Castañeda-Priego

Bibliographic record

VenueThe Journal of Chemical Physics · 2010
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Dynamics and Properties
Canadian institutionsWestern University
Fundersnot available
KeywordsDiffusionStatistical physicsContext (archaeology)Brownian dynamicsCoulombChemical physicsBrownian motionSuperparamagnetismStructure factorPhysicsCondensed matter physicsColloidChemistryThermodynamicsQuantum mechanicsPhysical chemistryElectron

Abstract

fetched live from OpenAlex

The structural properties and the single-file diffusion in one-dimensional interacting colloidal systems are studied by means of Brownian dynamics simulations. We consider three types of particle interactions, namely, Weeks-Chandler-Andersen, screened Coulomb, and superparamagnetic potentials. We find that, regardless of the interaction potential, at low densities particles are distributed in a typical fluidlike structure and at higher densities or potential strengths become spatially correlated at long-distances. Particularly, our findings demonstrate that one-dimensional systems, with particles interacting repulsively, show common structural and dynamical behaviors at the boundary in which the degree of ordering changes dramatically; the main peak of the static structure factor becomes highly narrow with a height of S(c)≈7, whereas the reduced mobility factor F, which is associated with the single-file diffusion at long-times or long wavelengths, reaches values F(∗)≈0.1. These features are analyzed and discussed in the context of a local order-disorder transition.

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 score0.139

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 head0.011
GPT teacher head0.202
Teacher spread0.191 · 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

Citations28
Published2010
Admission routes1
Has abstractyes

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