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Record W2033058497 · doi:10.1021/ac800908b

Simultaneous Speciation of Monomethylmercury and Monoethylmercury by Aqueous Phenylation and Purge-and-Trap Preconcentration Followed by Atomic Spectrometry Detection

2008· article· en· W2033058497 on OpenAlexfundaboutno aff
Yuxiang Mao, Guangliang Liu, George Meichel, Yong Cai, Guibin Jiang

Bibliographic record

VenueAnalytical Chemistry · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Research Council CanadaNational Institute of Environmental Health SciencesUniversity of Ottawa
KeywordsChemistryGenetic algorithmMass spectrometryChromatographyAqueous solutionDetection limitPurgeEnvironmental chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

A new method for the detection of trace levels of organomercury species has been developed by combining the high enrichment capacity of purge and trap with aqueous phenylation derivatization. Phenylation products of monomethylmercury (MeHg) and monoethylmercury (EtHg) were first separated by capillary gas chromatography and then detected by atomic fluorescence spectrometry (AFS) or inductively coupled plasma mass spectrometry (ICPMS). This combination made it possible to simultaneously quantify trace or ultratrace level of MeHg and EtHg in environmental samples. Method detection limits were 0.03 ng/L for both MeHg and EtHg when AFS was used as the detector and 0.02 and 0.01 ng/L for MeHg and EtHg with ICPMS, respectively. Certified reference materials, IAEA-405 and DORM-2, were analyzed and the results were in accordance with certified values. Both MeHg and EtHg were detected in sediment samples collected from the Florida Everglades and a Canadian wetland. This new method has been validated for the direct detection of trace organomercury species in freshwater samples and has the additional benefits of being free from interference by Cl (-) and dissolved organic matter.

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 score0.024
Threshold uncertainty score0.670

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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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

Citations62
Published2008
Admission routes2
Has abstractyes

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