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Record W2069093124 · doi:10.1103/physreve.79.021404

Critical heat flux around strongly heated nanoparticles

2009· article· en· W2069093124 on OpenAlexaff
Samy Mérabia, Pawel Keblinski, Laurent Joly, Laurent J. Lewis, Jean‐Louis Barrat

Bibliographic record

VenuePhysical Review E · 2009
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersAgence Nationale de la Recherche
KeywordsNucleate boilingHeat fluxMaterials scienceNanoparticleHeat transferCritical heat fluxBoilingBoiling pointThermodynamicsCurvatureParticle (ecology)Leidenfrost effectMechanicsNanotechnologyPhysics

Abstract

fetched live from OpenAlex

We study heat transfer from a heated nanoparticle into surrounding fluid using molecular dynamics simulations. We show that the fluid next to the nanoparticle can be heated well above its boiling point without a phase change. Under increasing nanoparticle temperature, the heat flux saturates, which is in sharp contrast with the case of flat interfaces, where a critical heat flux is observed followed by development of a vapor layer and heat flux drop. These differences in heat transfer are explained by the curvature-induced pressure close to the nanoparticle, which inhibits boiling. When the nanoparticle temperature is much larger than the critical fluid temperature, a very large temperature gradient develops, resulting in close to ambient temperature just a radius away from the particle surface. The behavior reported allows us to interpret recent experiments where nanoparticles can be heated up to the melting point, without observing boiling of the surrounding liquid.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.288
Teacher spread0.271 · 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 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

Citations96
Published2009
Admission routes1
Has abstractyes

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