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Record W2204371092 · doi:10.1139/cjc-2015-0411

Correlation between global thermodynamic functions and experimental data in multicomponent heterogeneous systems

2015· article· en· W2204371092 on OpenAlexvenueno aff
Igor Povar, Oxana Spînu

Bibliographic record

VenueCanadian Journal of Chemistry · 2015
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryPrecipitationThermodynamicsCoprecipitationDissolutionPhase (matter)Inorganic chemistryPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The correlation between global thermodynamic functions and such experimental data, which quantitatively characterize the precipitation–dissolution processes of sparingly soluble compounds, as the degree of precipitation and residual concentrations of the solid-phase components in saturated solutions under real conditions, taking into account the complex formation reactions, has been deducted. The paper intends also to introduce widely formal thermodynamic methods for forecasting the conditions of mutual transformation of solid phases through chemical synthesis by precipitation methods, optimization of coprecipitation methods, fractional precipitation from homogeneous solutions, and separation and analysis of chemical compounds. Within the method of residual concentrations, the thermodynamic parameters of the process of precipitating cadmium ions with potassium decanoate from acid and alkaline solutions for different temperatures were investigated. On the basis of the experimentally determined degree of precipitation and its dependence on temperature, the temperature coefficients and overall thermodynamic characteristics of the precipitation process ([Formula: see text], [Formula: see text], and [Formula: see text]) were determined. The optimum conditions of the investigated process of precipitation have been established.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.242

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 head0.032
GPT teacher head0.244
Teacher spread0.212 · 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 designSimulation or modeling
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

Citations8
Published2015
Admission routes1
Has abstractyes

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