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Record W2135247735 · doi:10.1002/aic.11111

Modeling l‐dopa purification by chiral ligand‐exchange chromatography

2007· article· en· W2135247735 on OpenAlexafffund
Nooshafarin Sanaie, Charles A. Haynes

Bibliographic record

VenueAIChE Journal · 2007
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsPfizer
KeywordsChemistryPotentiometric titrationElutionLigand (biochemistry)Equilibrium constantDispersion (optics)Phase (matter)ChromatographyVan Deemter equationThermodynamicsAnalytical Chemistry (journal)Physical chemistryHigh-performance liquid chromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A model describing elution‐band profiles that combines multiple chemical equilibria theory with the nonideal equilibrium–dispersion equation for solute transport is used to predict and characterize the separation of l,d‐dopa by chiral ligand‐exchange chromatography (CLEC). Formation constants and stoichiometries for all equilibrium complexes formed in the interstitial volume and pore liquid are taken from standard thermodynamic databases and independent potentiometric titration experiments. Formation constants for complexes formed with the stationary phase ligand (N‐octyl‐3‐octylthio‐d‐valine) are determined from potentiometric titration data for a water‐soluble analogue of the ligand. This set of pure thermodynamic parameters is used to calculate the spatially discretized composition of each column volume element as a function of time. The model includes a temperature‐dependent pure‐component parameter, determined by regression to a single elution band for the pure component, that corrects for subtle effects associated with immobilizing the N‐octyl‐3‐octylthio‐d‐valine ligand onto the stationary phase. The model is shown to accurately predict elution chromatograms and separation performance as a function of key column operating variables. The model is then used to better understand the connection between chemical equilibria within the system and changes in band profiles and band separation resulting from changes in column operating conditions. © 2007 American Institute of Chemical Engineers AIChE J, 2007

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.977

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.240
Teacher spread0.229 · 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

Citations5
Published2007
Admission routes2
Has abstractyes

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