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A novel approach to cold vapor generation for the determination of mercury in biological samples

2008· article· en· W2112174162 on OpenAlexaff
Éder José dos Santos, Amanda Beatriz Herrmann, Vera Lúcia Azzolin Frescura, Ralph E. Sturgeon, Adilson José Curtius

Bibliographic record

VenueJournal of the Brazilian Chemical Society · 2008
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsNational Research Council Canada
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsMercury (programming language)ChemistryCertified reference materialsAnalytical Chemistry (journal)Detection limitDissolutionAqueous solutionInductively coupled plasmaArgonStandard solutionInductively coupled plasma atomic emission spectroscopyOrganomercuryInductively coupled plasma mass spectrometryCalibration curveStandard additionPlasmaChromatographyMass spectrometry

Abstract

fetched live from OpenAlex

A novel approach to the generation of mercury vapor at high pH by the simple addition of NaOH or other base to a solution containing mercury ions is described. Subsequent addition of another reducing agent, such as NaBH4 or SnCl2, is unnecessary. Following total dissolution of sample using HNO3 and H2O2 in a closed microwave vessel, the pH of the sample solution is increased to 13 by the addition of NaOH. After standing for 90 min in a closed vessel, the Hg0 is directed to the plasma by a flow of argon. Emission from mercury is measured at 253.652 nm by an axial view inductively coupled plasma optical emission spectrometer (ICP OES). The procedure was applied to five certified biological samples, yielding a detection limit (3s, n = 10) of 0.04 µg g-1 based on a nominal sample mass of 0.5 g in a final volume of 50 mL. Calibration was achieved using simple aqueous standard solutions containing 0.1 mol L-1 NaOH. The procedure was efficient, with determined values lying in the range of 85-113 % of the certified values, showing good agreement at the 95% confidence level (t-test). The precision was fit for purpose, with relative standard deviations ranging from 7 to 9%. Organomercury species in solution were not detected; only Hg2+ in solution produces a signal. This new procedure provides for a simple approach to quantitation (and potentially speciation).

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)0.001
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 score0.011
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.096
GPT teacher head0.307
Teacher spread0.210 · 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
Published2008
Admission routes1
Has abstractyes

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