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Record W2039854105 · doi:10.1103/physrevb.74.174106

Model for phase transition based on statistical disassembly of nuclei at intermediate energies

2006· article· en· W2039854105 on OpenAlexaff
G. Chaudhuri, S. Das Gupta, Mark Sutton

Bibliographic record

VenuePhysical Review B · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsMcGill University
Fundersnot available
Keywordsvan der Waals forcePhase transitionPhysicsScalingMonte Carlo methodNucleonSigmaFinite volume methodThermodynamicsNuclear physicsQuantum mechanicsGeometryMathematics

Abstract

fetched live from OpenAlex

Consider a model of particles (nucleons) that has a two-body interaction, which leads to bound composites with saturation properties. These properties are: all composites have the same density and the ground-state energies of composites with $k$ nucleons are given by $\ensuremath{-}kW+\ensuremath{\sigma}{k}^{2∕3}$, where $W$ and $\ensuremath{\sigma}$ are positive constants. $W$ represents a volume term and $\ensuremath{\sigma}$ a surface-tension term. These values are taken from nuclear physics. We show that in the large $N$ limit where $N$ is the number of particles, such an assembly in a large enclosure at finite temperature shows properties of liquid-gas phase transition. We do not use the two-body interaction but the gross properties of the composites only. We show that (a) the $p\text{\ensuremath{-}}\ensuremath{\rho}$ isotherms show a region where pressure does not change as $\ensuremath{\rho}$ changes just as in the Maxwell construction of a Van der Waals gas, (b) in this region the chemical potential does not change, and (c) the model obeys the celebrated Clausius-Clapeyron relations. A scaling law for the yields of composites emerges. For a finite number of particles $N$ (up to some thousands) the problem can be easily solved on a computer. This allows us to study finite particle number effects, which modify phase-transition effects. The model is calculationally simple. Monte Carlo simulations are not needed.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.710
Threshold uncertainty score0.422

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 head0.012
GPT teacher head0.307
Teacher spread0.295 · 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 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

Citations9
Published2006
Admission routes1
Has abstractyes

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