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Record W2329147559 · doi:10.1021/je2004808

Ion-Pair Association Constant for LiOH in Supercritical Water

2011· article· en· W2329147559 on OpenAlexaff
Andriy Plugatyr, Ruth A. Carvajal-Ortiz, Igor M. Svishchev

Bibliographic record

VenueJournal of Chemical & Engineering Data · 2011
Typearticle
Languageen
FieldEngineering
TopicSubcritical and Supercritical Water Processes
Canadian institutionsTrent University
Fundersnot available
KeywordsSupercritical fluidChemistryIon-associationThermodynamicsConstant (computer programming)Aqueous solutionAtmospheric temperature rangeIonMolecular dynamicsRange (aeronautics)Heat capacityPhysical chemistryComputational chemistryOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

The equilibrium ion-pair association constant for LiOH in supercritical water is determined by means of molecular dynamics (MD) simulations via the potential of mean force calculation. Simulations are performed along three supercritical isotherms of (673.15, 773.15, and 873.15) K, covering a density range from (0.05 to 0.8) g·cm –3 . Over the examined temperature and density range, the obtained association constant increases with increasing temperature and decreasing density. A significant increase in the association constant is observed upon transition into the low density (ρ < ρ c ) supercritical region. The obtained results are compared with the available experimental data at the corresponding states. To determine the corresponding states, an accurate reference equation of state for the simulated water model is used. An analytical expression for the association constant of LiOH in aqueous solution over the examined thermodynamic range is given. The results are of practical interest for chemistry control in the supercritical water-cooled nuclear reactor heat transport system.

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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.229
Teacher spread0.194 · 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

Citations31
Published2011
Admission routes1
Has abstractyes

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