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Record W4388928721 · doi:10.46770/as.2023.101

Novel Liquid Microjunction Introduction System for Inductively Coupled Plasma Mass Spectrometry

2023· article· en· W4388928721 on OpenAlexafffund
Diane Beauchemin

Bibliographic record

VenueAtomic Spectroscopy · 2023
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaQueen's University
KeywordsChemistryInductively coupled plasma mass spectrometryMass spectrometryAnalytical Chemistry (journal)Inductively coupled plasmaPlasmaEnvironmental chemistryChromatographyNuclear physics

Abstract

fetched live from OpenAlex

A novel sampling probe composed of a coaxial-tube that functions at a continuous-flow was used to inject 1 μL of sample into an inductively coupled plasma mass spectrometer.Simply touching a sample to the dome of liquid at the inlet of the liquid microjunction (LMJ) allows for sample introduction into a flowing carrier stream that transports sample into the plasma-similar to the mechanism of sample transport in flow injection analysis.This self-cleaning sampling system, allows for sampling of minute amounts of liquids at atmospheric pressure as well as leaching of soluble components off the surface of a solid.Preliminary results demonstrate the implementation of this probe in steady-state, flow injection, and surface sampling modes.A substantial reduction of matrix effects and oxide interferences resulted with 1 μL injections.In fact, the performance of LMJ with 1 μL injections in terms of spectroscopic and non-spectroscopic interference mitigation, sensitivity and detection limit is very similar to that previously reported with 1 μL mono-segmented flow analysis.At.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.263
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes2
Has abstractyes

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