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Measurement of Chromium Species in Water Samples

2019· other· en· W2997459063 on OpenAlexaff
Vasile I. Furdui, Stefanie Mädler

Bibliographic record

VenueEncyclopedia of Water · 2019
Typeother
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsToronto Metropolitan UniversityMinistry of the Environment, Conservation and Parks
Fundersnot available
KeywordsChemistryChromiumMass spectrometryInductively coupled plasma mass spectrometryChromatographyIsotope dilutionElectrospray ionizationFlow injection analysisTandem mass spectrometryDetection limitAnalytical Chemistry (journal)Hexavalent chromiumCapillary electrophoresis

Abstract

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Abstract While trivalent chromium (Cr(III)) is an essential nutrient, hexavalent chromium (Cr(VI)) is a human carcinogen and genotoxic. In evaluation of the toxicological risks and environmental impacts, the separate analysis of Cr(III) and Cr(VI) in water samples provides more information than the analysis of total Cr. The relatively high instability of the Cr species in natural water samples further complicates the analysis, requiring preservation or other advanced procedures. This article is intended to present the broad range of available separation methods for Cr analysis, discussing the possible interferences, detection limits, and preserving options. The intense color complex formed between dichromate ions and 1,5‐diphenylcarbazide (DPC) is used for Cr(VI) measurement in spectrophotometric, flow injection analysis (FIA), ion chromatography (IC), liquid chromatography (LC), capillary electrophoresis (CE), and electrochemical methods. Atomic spectrometry, molecular spectrometry, and other methods are also discussed. An absolute analysis with accurate measurement of Cr(VI) at the time of sample collection can be achieved with speciated isotope dilution mass spectrometry (SIDMS) by using LC separation coupled to the inductively coupled plasma mass spectrometry (ICP‐MS) or IC separation coupled to electrospray ionization tandem mass spectrometry (ESI‐MS/MS).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.365
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0290.000

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 head0.028
GPT teacher head0.248
Teacher spread0.220 · 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.

Study designBench or experimental
Domainnot available
GenreOther

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

Citations0
Published2019
Admission routes1
Has abstractyes

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