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Record W2595818559 · doi:10.3934/bioeng.2017.2.223

Impact of operating conditions on chromatographic column performance: experimental studies on adsorption of high-value minor whey proteins

2017· article· en· W2595818559 on OpenAlexaff
Naeimeh Faraji, Yan Zhang, Ajay K. Ray

Bibliographic record

VenueAIMS bioengineering · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein purification and stability
Canadian institutionsMemorial University of NewfoundlandWestern University
Fundersnot available
KeywordsChemistryAdsorptionVan Deemter equationChromatographyBreakthrough curveVolumetric flow rateIon chromatographyIon exchangeParticle (ecology)Expanded bed adsorptionPorosityAnalytical Chemistry (journal)IonElutionHigh-performance liquid chromatographyOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

Over the last decade, ion-exchange chromatography (IEC) has been extensively explored for protein purification at both small and large scales. Despite several IEC columns are commercialized, the physical phenomena underlying the adsorption of proteins on ion-exchange columns performance has not been thoroughly investigated. In this work, the influence of operating conditions on the adsorption of lactoperoxidase (LP) and lactoferrin (LF) on cation exchange chromatography adsorbent is experimentally studied in order to understand fundamental pertaining to underlying mechanism. Analysis was carried out in columns with different IDs (7.7 and 16 mm), packed for 100 mm with 90 μm particle size polymer-grafted cation exchanger. The flow distribution was measured using acetone as a non-binding tracer. An evaluation of van Deemter plots was done as well as LP breakthrough curves at different flow rates and LP loading concentrations. The results were compared with two columns in terms of efficiency and the LP binding capacity. The dynamic binding capacity at 10% breakthrough was found to be independent of the applied flow rate. Surprisingly for both systems, LP breakthrough takes place later at higher loading concentrations, which is in contrast to IEC. The results propose a major presence of non-ideal effects as steric shielding and charge repulsion of protein in the adsorption. In addition, the accessibility of binding sites for protein at higher concentrations seems more available than sodium counter-ions in buffer.

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 score0.109
Threshold uncertainty score0.462

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 head0.021
GPT teacher head0.316
Teacher spread0.295 · 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
Published2017
Admission routes1
Has abstractyes

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