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Record W2315287291 · doi:10.1021/cg200861e

Interfacial Free Energy: An Entropy Portent to Energy Changes

2011· article· en· W2315287291 on OpenAlexafffund
Payman Pirzadeh, Eric N. Beaudoin, Peter G. Kusalik

Bibliographic record

VenueCrystal Growth & Design · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaWestern Canada Research GridUniversity of Calgary
KeywordsThermodynamicsNucleationSurface energyChemical physicsEntropy (arrow of time)Thermodynamic free energyChemistryMolecular dynamicsMaterials sciencePhysicsComputational chemistry

Abstract

fetched live from OpenAlex

Interfacial free energy is a critical quantity governing the behavior in processes such as crystal nucleation and growth, and there have been extensive efforts to measure or calculate this quantity for a variety of systems. Here, we show that profiles for the changes in energy and entropy across a solid/liquid interface can be extracted from molecular simulations. These smooth molecular level profiles can then be employed to estimate the free energy profile across a two-phase system. A distinctive feature of this profile is the presence of a peak in the free energy at the interface arising from the displacement of the change in entropy in advance of that of the energy. This concept is explicitly examined for the basal and prism interfaces of ice/water systems at the melting point and at undercooled conditions. The values obtained from this analysis for the interfacial free energy and thickness are in accord with previous estimates. The success of this approach demonstrates that microscopic details, captured in molecular level profiles, can be effectively linked to key thermodynamic quantities such as the interfacial free energy.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.051
GPT teacher head0.217
Teacher spread0.166 · 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 designTheoretical or conceptual
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

Citations18
Published2011
Admission routes2
Has abstractyes

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