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Record W2005171236 · doi:10.1039/b406307d

Buffer composition changes in background electrolyte during electrophoretic run in capillary zone electrophoresis

2004· article· en· W2005171236 on OpenAlexaff
Alexander Stoyanov, Janusz Pawliszyn

Bibliographic record

VenueThe Analyst · 2004
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCapillary electrophoresisElectrolyteElectrophoresisChemistryAnalytical Chemistry (journal)ElectrolysisChromatographyBuffer (optical fiber)Capillary actionBuffer solutionElectrodeMaterials science

Abstract

fetched live from OpenAlex

The electrophoretic behaviors of different analytes in capillary zone electrophoresis were studied by the Whole Column Imaging Detection (WCID). For capillary zone electrophoresis (CZE) in conventional buffer systems, non-constant sample plug movement characterized by progressive decrease of peak migration velocity was observed. The appropriate velocity decrease was correlated with a degree of ionization of the analyzed ion, thus the effect observed could be explained by fast buffer composition change resulting in the development of a non-linear pH gradient. To visualize the appropriate pH gradient, the concentration profile of initially uniformly distributed amphoteric substances was also monitored. The evolution of the concentration profile exhibited very complex dynamics. In addition, it was found that the nature of the electrode solutions strongly affect changes in the background electrolyte. In the case of traditional background electrolytes with an acid-base pair for electrode solutions a non-uniform ampholyte concentration developed quickly, leading finally to a quasi-stationary profile similar to those typical of IEF. Possible approaches to suppress a negative impact of the background electrolyte composition changes during electrophoretic run on CE-separation are presented herein. In particular it was observed that zwitterionic buffers are able to withstand prolonged electrolysis much better compared to traditional buffers.

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 categoriesMeta-epidemiology (narrow)
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 score0.039
Threshold uncertainty score1.000

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 head0.005
GPT teacher head0.194
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.

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

Citations19
Published2004
Admission routes1
Has abstractyes

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