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Record W2097598250 · doi:10.1039/b814401j

Vapor generation coupled with furnace atomization plasma emission spectrometry for detection of mercury

2009· article· en· W2097598250 on OpenAlexafffund
Anderson Schwingel Ribeiro, Mariana Antunes Vieira, Patrícia Grinberg, Ralph E. Sturgeon

Bibliographic record

VenueJournal of Analytical Atomic Spectrometry · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsNational Research Council Canada
FundersNational Research Council Canada
KeywordsChemistryDetection limitMercury (programming language)Analytical Chemistry (journal)TinMass spectrometryPlasmaSeparator (oil production)Water vaporChlorideChromatography

Abstract

fetched live from OpenAlex

A low power atmospheric pressure plasma source, furnace atomization plasma emission spectrometry (FAPES), was directly coupled to both a conventional chemical vapor generation system based on use of tin chloride reductant as well as a UV-photoreduction system for detection of cold vapor mercury. The resonance line emission at 253.7 nm was monitored. The 70 W low power He plasma was tolerant to the introduction of water vapor from the gas–liquid separator and, with the furnace heated to 400 °C, a precision of measurement of 2.4% RSD at 1 ng mL−1 was achieved. A limit of detection of 250 pg mL−1Hg in river water samples via UV-photoreduction and 240 pg mL−1 using conventional tin chloride reduction was obtained. The LOD could be improved 5-fold through use of a simple gold amalgamation system. Conventional chemical generation of mercury using the NaBH4/HCl system produced too much hydrogen to permit efficient coupling to the FAPES source as the He plasma is extinguished by the load of molecular vapor (i.e., hydrogen).

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)0.001
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 score0.236
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.014
GPT teacher head0.264
Teacher spread0.250 · 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

Citations18
Published2009
Admission routes2
Has abstractyes

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