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Record W2084090498 · doi:10.1063/1.4823513

Diffusivity and hydration of hydrazine in liquid and supercritical water through molecular dynamics simulations and split-flow pulse injection experiments

2013· article· en· W2084090498 on OpenAlexaff
Dimitrios T. Kallikragas, Kashif I. Choudhry, Andriy Plugatyr, Igor M. Svishchev

Bibliographic record

VenueThe Journal of Chemical Physics · 2013
Typearticle
Languageen
FieldEngineering
TopicSubcritical and Supercritical Water Processes
Canadian institutionsTrent University
Fundersnot available
KeywordsSupercritical fluidMolecular dynamicsChemistryDiffusionThermal diffusivityWater modelAqueous solutionThermodynamicsMoleculeSelf-diffusionHydrazine (antidepressant)Chemical physicsPhysical chemistryComputational chemistryOrganic chemistryChromatography

Abstract

fetched live from OpenAlex

The diffusion properties and hydration structure of hydrazine in an aqueous solution are investigated through molecular dynamics simulations and split-flow pulse injection experiments. The simulations are performed from ambient conditions along the liquid side of the liquid-vapor coexistence curve, up to the critical point, and in the supercritical region at temperatures of 673, 773, 873, and 973 K and at densities ranging from 0.1 to 0.8 g cm(-3). The spatial distributions functions for hydrated water are presented. At ambient conditions, hydrazine is hydrated by 24 water molecules with about 1.6 H-bonds being donated to each nitrogen atom. The hydration number decreases with temperature along the coexistence curve and is seen to increase with system density in the supercritical region. At low density supercritical conditions, hydrazine has no appreciable hydration structure and is surrounded by only 2 water molecules at 873 K and 0.1 g cm(-3). The diffusion coefficients for hydrazine at subcritical state conditions are found to be in agreement with Stokes-Einstein and Wilke-Chang predictions. The diffusion coefficients in the supercritical region are found to correlate more closely with the overall fit to the Dymond equation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.270

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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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 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

Citations11
Published2013
Admission routes1
Has abstractyes

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