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Record W2321672199 · doi:10.1021/ac026223k

Trace Level Determination of Lead in Solid Samples by UV Laser Ablation and Laser-Enhanced Ionization Detection

2003· article· en· W2321672199 on OpenAlexafffund
Jean-François Gravel, Philippe Nobert, Jean-François Y. Gravel, Denis Boudreau

Bibliographic record

VenueAnalytical Chemistry · 2003
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryAnalytical Chemistry (journal)Laser ablationDetection limitIonizationArgonLaserVaporizationOptics

Abstract

fetched live from OpenAlex

Laser-enhanced ionization was investigated as a detection technique for trace elemental analysis of solid samples by laser ablation. Laser ablation of aluminum samples was performed in an ablation cell, and the ablated material was carried by a flow of gas to a miniature LEI flame where Pb was detected. This decoupling of ablation cell and detector allowed the independent optimization of vaporization and detection processes. We have investigated the different excitation schemes for Pb and uncovered five new LEI-active transitions in the visible range. We have demonstrated that the use of an argon-oxygen/acetylene flame sheathed with argon resulted in the elimination of background interference from the two-photon ionization of nitric oxide. We have shown that the use of helium as a carrier gas results in a higher ablation yield and lower pulse-to-pulse variations in LEI signal and in better analytical figures of merit. We have characterized the performance of the technique in terms of detection limits and dynamic range, and we have obtained a detection limit of 60 ng/g for the determination of Pb in high purity aluminum.

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 score0.093
Threshold uncertainty score0.499

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 head0.015
GPT teacher head0.242
Teacher spread0.228 · 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

Citations13
Published2003
Admission routes2
Has abstractyes

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