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Record W1967576426 · doi:10.1088/0953-8984/22/3/035105

A molecular dynamics simulation of the diffusion of the solute (Au) and the self-diffusion of the solvent (Cu) in a very dilute liquid Cu–Au solution

2009· article· en· W1967576426 on OpenAlexaff
Barbara Szpunar, Reginald W. Smith

Bibliographic record

VenueJournal of Physics Condensed Matter · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsUniversity of SaskatchewanQueen's University
Fundersnot available
KeywordsDiffusionMolecular dynamicsWork (physics)ThermodynamicsSolventConstant (computer programming)Fick's laws of diffusionEffective diffusion coefficientAtmospheric temperature rangeChemistryChemical physicsRange (aeronautics)Molecular diffusionSelf-diffusionMaterials sciencePhysical chemistryComputational chemistryPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

The identification of the manner in which a solute diffusion coefficient (D) might vary with temperature (T) in a fused metal or semimetal has led to considerable experimental study and some theoretical analysis. However, the conclusions of this work are inconsistent. In the present work, molecular dynamics studies of diffusion of a very dilute solute (Au) in liquid Cu are presented. Using the simple Enskog theory of diffusion, it is shown that the ratio of the diffusion constant of the solute to the diffusion constant of the solvent for a very dilute solution is approximately constant. This prediction is confirmed by molecular dynamics simulations although the values of ratios agree only within 20%-25%. In agreement with experiment, current simulations predict that within the usually investigated temperature range, the diffusion coefficient is linearly dependent on temperature. A very small contribution of parabolic behavior can only be observed for a temperature range much wider than that available for physical experiments due to materials limitations.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.194

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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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 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

Citations8
Published2009
Admission routes1
Has abstractyes

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