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Record W4213151525 · doi:10.6000/1929-5030.2022.11.08

The Friction Coefficient Investigation during Electrochemical Baromembrane Separation of Model Solutions

2022· article· en· W4213151525 on OpenAlexvenueno aff
К. В. Шестаков

Bibliographic record

VenueJournal of Applied Solution Chemistry and Modeling · 2022
Typearticle
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMass transferMembraneElectrochemistrySeparation (statistics)ThermodynamicsCellulose acetateChemistryKinetic energyAnalytical Chemistry (journal)ChromatographyMathematicsElectrodePhysical chemistryStatisticsPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

A large number of scientific papers in recent decades have been devoted to the mathematical description of a mass transfer of substances through membranes. The choice of a mathematical model is to be carried out depending on the specific separation process used. This study represents a verification of how the predicted value of the solute flow density obtained using pre-calculated friction coefficients corresponds to real experimental data for electrochemical baromembrane separation of model solutions. The experimental and theoretically calculated values of the Ni (II), Cu (II), and Fe (III) cations flux density through the heterogeneous cellulose acetate membranes MGA-95 and MGA-100 are compared. It is established that the use of the mass transfer friction model is quite effective in predicting the kinetic characteristics of electrochemical membrane separation of solutions using semipermeable membranes. The calculation error in most cases is within one percent.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.019
GPT teacher head0.241
Teacher spread0.221 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied Solution Chemistry and ModelingSame topicMembrane-based Ion Separation TechniquesFrench-language works237,207