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Record W2060475016 · doi:10.1021/ac0608568

On-Line Enhancement Technique for the Analysis of Nucleotides Using Capillary Zone Electrophoresis/Mass Spectrometry

2006· article· en· W2060475016 on OpenAlexafffund
Yong‐Lai Feng, Jiping Zhu

Bibliographic record

VenueAnalytical Chemistry · 2006
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsHealth Canada
FundersHealth CanadaAustralian Government
KeywordsChemistryElectrokinetic phenomenaCapillary electrophoresisChromatographyAnalytical Chemistry (journal)AnalyteMass spectrometryCapillary actionElectrophoresisElectrospraySample preparation

Abstract

fetched live from OpenAlex

A new on-line capillary zone electrophoresis/mass spectrometry (CZE/MS), constant pressure-assisted electrokinetic injection (PAEKI), for the analysis of negatively charged nucleotides is reported. PAEKI uses an applied pressure to counterbalance the reverse electroosmotic flow in the capillary column during sample injection, while taking advantage of the field amplification in the sample medium. At balance, the running buffer in the column is stationary, permitting potentially unlimited injection time, and hence unlimited sample enrichment power. The ability of PAEKI to maintain a narrow sample zone over a long injection time seems to be a result of the formation of a high ion concentration band at the boundary of the two media due to rapid deceleration of the migrating ions at the boundary. The injected amount of analytes proved to be linearly proportional to both the field amplification factor, which is expressed as the ratio of resistivities of sample medium to running buffer, and the injection time, which extended up to 1200 s in CZE/MS and 3600 s in CZE/UV. For a 300-s on-line PAEKI injection in CZE/MS, 3 orders of magnitude sample enhancement (5000-fold enrichment) could be observed for the four single nucleotides without compromising separation efficiency and peak shape, and an achievement of detection limits between 0.04 and 0.07 ng/mL. With appropriate sample cleanup, PAEKI can be used in the analysis of single nucleotides in enzyme-digested DNA.

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: none
Teacher disagreement score0.611
Threshold uncertainty score0.682

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 head0.010
GPT teacher head0.239
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

Citations49
Published2006
Admission routes2
Has abstractyes

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