MétaCan
Menu
Back to cohort
Record W1996561591 · doi:10.1021/ie0701524

Speciation-Based Chemical Equilibrium Model of CaSO<sub>4</sub> Solubility in the H + Na + Ca+ Mg + Al + Fe(II) + Cl + SO<sub>4</sub> + H<sub>2</sub>O System

2007· article· en· W1996561591 on OpenAlexafffund
Zhibao Li, George P. Demopoulos

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2007
Typearticle
Languageen
FieldChemical Engineering
TopicChemical and Physical Properties in Aqueous Solutions
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSolubilityChemistryAqueous solutionSolubility equilibriumSulfateMetalActivity coefficientChlorideDissociation (chemistry)Equilibrium constantInorganic chemistryChemical equilibriumThermodynamicsPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

This work describes a self-consistent unified chemical model for calculating the solubility of CaSO 4 phases in the H + Na + Ca+ Mg + Al + Fe(II) + Cl + SO 4 + H 2 O system from low to high solution concentration within the temperature range of 298−353 K. The model was built with the aid of OLI Systems platform via the regression of new solubility data of calcium sulfate dihydrate in HCl or HCl + CaCl 2 aqueous solutions containing various metal chloride salts, such as NaCl, MgCl 2, FeCl 2, and AlCl 3 . Via this regression analysis, new Bromley−Zemaitis activity coefficient model parameters and empirical dissociation constant parameters were determined for many ion pairs consisting of cations (Na +, Mg 2+, Fe 2+, and Al 3+ ) and anions, HS, and Al ), as well as for the species MgSO 4 (aq), AlS, and Al . The new model was shown to successfully predict the solubility of calcium sulfate phases in multicomponent systems not used in model parametrization. The new model is used to explain the complex effect metal chlorides have on the solubility of CaSO 4 phases on the basis of governing metal−sulfate speciation equilibria.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
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 score0.243
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.005
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 head0.060
GPT teacher head0.285
Teacher spread0.225 · 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; both teacher heads agree on what is shown here.

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

Citations14
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueIndustrial & Engineering Chemistry ResearchSame topicChemical and Physical Properties in Aqueous SolutionsFrench-language works237,207