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Liquid-Liquid Phase Transition in Simulated Supercooled Water Nanodroplets

2025· article· en· W4409003443 on OpenAlexafffund
Shahrazad Malek, Francesco Sciortino, Peter H. Poole, Ivan Saika‐Voivod

Bibliographic record

VenuePhysical Review Letters · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Dynamics and Properties
Canadian institutionsMemorial University of NewfoundlandSt. Francis Xavier UniversityUniversity of the Fraser Valley
FundersNatural Sciences and Engineering Research Council of CanadaMinistero dell’Istruzione, dell’Università e della RicercaEuropean Commission
KeywordsSupercoolingChemical physicsMaterials sciencePhase transitionLiquid waterLaplace pressureRange (aeronautics)Phase (matter)Molecular dynamicsLiquid liquidGlass transitionAmbient pressureThermodynamicsChemistryPhysicsComputational chemistryChromatographySurface tensionPolymerComposite material

Abstract

fetched live from OpenAlex

Using simulations, we demonstrate how a liquid-liquid phase transition (LLPT) manifests in supercooled water nanodroplets. Selecting an interaction potential for which a LLPT occurs in the bulk liquid, we conduct simulations of supercooled water nanodroplets having between 1000 and 80000 molecules. We show that as the droplet size decreases, the Laplace pressure grows large enough to drive the droplets through the transition from the low-density to the high-density liquid phase, and that all droplets in this size range are large enough to have cores exhibiting the structure and properties of bulk water. To guide experiments, we estimate the range of values for the critical pressure of the LLPT in real water that can be observed using nanodroplets, and propose structural and dynamical measures by which the LLPT in nanodroplets can be detected.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.012
GPT teacher head0.292
Teacher spread0.280 · 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 designSimulation or modeling
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

Citations4
Published2025
Admission routes2
Has abstractyes

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