MétaCan
Menu
Back to cohort
Record W2082821131 · doi:10.1039/b212492k

Acoustic wave detection of chemical species electrokinetically transported within a capillary tube

2003· article· en· W2082821131 on OpenAlexafffund
Paul C. H. Li, Ronald Prasad

Bibliographic record

VenueThe Analyst · 2003
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsSimon Fraser University
FundersSimon Fraser University
KeywordsElectrokinetic phenomenaCapillary actionDetection limitAnalyteTransducerChemistryCapillary electrophoresisAnalytical Chemistry (journal)Capillary waveChromatographyMaterials scienceAcousticsOpticsSurface wave

Abstract

fetched live from OpenAlex

For the first time, we report the acoustic wave detection of chemical species being transported in a capillary tube to a region where acoustic coupling occurs. The measured parameter was a change in phase, which was originally only attributed to a change in solution density as the analyte passed by the detection region. Accordingly, we report the detection of change in phase as various chemical species (e.g. Cy5 dye, Cy5-derivatized glycine and underivatized glycine) were introduced into and migrated along a capillary tube through electrokinetic processes. To improve detection sensitivity, we modified various experimental parameters, such as run buffer concentration, capillary wall thickness and transducer frequency. Although acoustic wave detection was feasible, the peak width and detection limit were inadequate as compared to conventional detection methods for HPLC or CE. Nevertheless, the effects of various physical and chemical relaxation processes on acoustic wave absorption were discussed, and this has shed some light on explaining some observations, which cannot be explained by density differences alone. Accordingly, the acoustic wave method is suggested to investigate these processes, as studied in ultrasonic relaxation spectroscopy, in a flow system.

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 score0.031
Threshold uncertainty score0.373

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 head0.007
GPT teacher head0.176
Teacher spread0.168 · 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

Citations1
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueThe AnalystSame topicMicrofluidic and Capillary Electrophoresis ApplicationsFrench-language works237,207