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Record W2044477419 · doi:10.1002/cjce.21971

Enantioseparation of racemic mandelic acid by simulated moving bed chromatography using Chiralcel‐OD column

2014· article· en· W2044477419 on OpenAlexafffundvenue
Shimin Mao, Yan Zhang, Sohrab Rohani, Ajay K. Ray

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsMemorial University of NewfoundlandWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMandelic acidChromatographyChemistrySimulated moving bedColumn chromatographyColumn (typography)Chiral column chromatographyChiral stationary phaseHigh-performance liquid chromatographyOrganic chemistryAdsorptionMathematics

Abstract

fetched live from OpenAlex

The chiral separation of the racemic mandelic acid (( R,S )‐MA) by simulated moving bed chromatography with columns packed with Chiralcel‐OD stationary phase is investigated experimentally and numerically. The transport dispersion model combined with the modified Langmuir isotherm was applied to predict the dynamic behaviour and separation performance of the SMB process. The Triangle Theory was used to obtain the complete separation region of the SMB operation. The influences of the switching time, loading, extract flow rate and column configuration on the SMB performance were also studied. The online monitoring system consisting of circular dichroism (CD) and UV detector in series was employed to measure the composition and purity of the extract and raffinate streams. Good agreement between the experimental data and model predictions proved the efficiency and reliability of SMB modelling and the Triangle theory in the design and operation of SMB chromatography. Experimental results show that purity and recovery at extract and raffinate port can reach 99% although not at the same time.

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 score0.072
Threshold uncertainty score0.732

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 head0.006
GPT teacher head0.195
Teacher spread0.189 · 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

Citations4
Published2014
Admission routes3
Has abstractyes

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