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Record W2071758994 · doi:10.1002/cjce.20002

Determination of ternary solutions concentration in liquid–liquid extraction by the use of attenuated total reflectance‐Fourier transform infrared spectroscopy and multivariate data analysis

2008· article· en· W2071758994 on OpenAlexvenueno aff
Tzayhrí Gallardo‐Velázquez, Guillermo Osorio‐Revilla, Fernando Cárdenas-Bailón, M. C. Beltrán‐Orozco

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2008
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Chemometric Analyses
Canadian institutionsnot available
FundersInstituto Politécnico Nacional
KeywordsRaffinateAttenuated total reflectionExtraction (chemistry)Fourier transform infrared spectroscopyPartial least squares regressionChemistryTernary operationChromatographyAnalytical Chemistry (journal)Fourier transformInfrared spectroscopySolventCorrelation coefficientMathematicsOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A simple and rapid Fourier transform infrared‐attenuated total reflectance (FTIR‐ATR) spectroscopic chemometric method was developed to determine the concentration of solute and solvent in the raffinate layer in a liquid–liquid extraction (LLE) process using partial least squares (PLS) regression. Five type I extraction systems were used with different solute–solvent affinity. The developed model (correlation coefficient from 0.91 to 0.99) was validated with known concentration samples, and in all cases the difference was not larger than 0.5%w. The LLE for the five extraction systems was carried out in a three‐stage crosscurrent extraction process, quantifying the solute and solvent with the chemometric model developed. The results were used to calculate the stage and overall stage efficiencies for the five systems. This method showed to be fast and precise for the quantification of ternary systems in LLE.

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 score0.103
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.041
GPT teacher head0.272
Teacher spread0.231 · 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

Citations9
Published2008
Admission routes1
Has abstractyes

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