MétaCan
Menu
Back to cohort
Record W1965106458 · doi:10.1103/physreve.72.056304

Surface-thermal capacity of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="normal">D</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mi mathvariant="normal">O</mml:mi></mml:mrow></mml:math>from measurements made during steady-state evaporation

2005· article· lv· W1965106458 on OpenAlexafffund
Fei Duan, C. A. Ward

Bibliographic record

VenuePhysical Review E · 2005
Typearticle
Languagelv
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsThermal conductionThermodynamicsHeat capacityThermalConvectionHeat fluxGibbs free energyEvaporationEnergy (signal processing)Materials sciencePhysicsHeat transfer

Abstract

fetched live from OpenAlex

When ${\mathrm{D}}_{2}\mathrm{O}(l)$ evaporates into its vapor under steady-state conditions with the temperature field in the liquid arranged so that there is no buoyancy-driven convection and the Marangoni number is less than $\ensuremath{\sim}100$, it is found that the interface is quiescent and thermal conduction to the interface supplies energy at a sufficient rate to evaporate the liquid. However, if the evaporation rate is raised so that the Marangoni number goes above $\ensuremath{\sim}100$, the interface is transformed: a fluctuating thermocapillary flow occurs, and thermal conduction no longer supplies energy at a sufficient rate to evaporate the liquid. An energy analysis indicates conservation of energy can be satisfied only if thermocapillary convection is taken into account, and the surface-thermal capacity ${c}_{\ensuremath{\sigma}}$ is assigned a value of $32.5\ifmmode\pm\else\textpm\fi{}0.8\phantom{\rule{0.3em}{0ex}}\mathrm{kJ}∕({\mathrm{m}}^{2}\phantom{\rule{0.2em}{0ex}}\mathrm{K})$ when the temperature is in the range $\ensuremath{-}10\phantom{\rule{0.2em}{0ex}}\ifmmode^\circ\else\textdegree\fi{}\mathrm{C}\ensuremath{\leqslant}{T}^{LV}\ensuremath{\leqslant}3.7\phantom{\rule{0.2em}{0ex}}\ifmmode^\circ\else\textdegree\fi{}\mathrm{C}$. This value is consistent with that found previously for ${\mathrm{H}}_{2}\mathrm{O}$, and application of the Gibbs model gives a qualitative explanation for the value. Once the value of the surface-thermal capacity is known, the local heat flux along the interface can be calculated and statistical rate theory can be used to predict the local vapor-phase pressure on the interface. Since this theory introduces no adjustable parameters, the predicted pressure can be compared directly with that measured: this comparison indicates the mean of the pressures predicted to exist on the interface is in close agreement with those measured $\ensuremath{\sim}20\phantom{\rule{0.3em}{0ex}}\mathrm{cm}$ above the interface, and the small pressure gradient along the interface is consistent with the thermocapillary convection predicted from the interfacial temperature gradient.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0320.008

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 head0.030
GPT teacher head0.253
Teacher spread0.224 · 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

Citations38
Published2005
Admission routes2
Has abstractyes

Explore more

Same venuePhysical Review ESame topicnanoparticles nucleation surface interactionsFrench-language works237,207