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Record W2314771244 · doi:10.1021/ac4032466

Chemical Generation of Arsane and Methylarsanes with Amine Boranes. Potentialities for Nonchromatographic Speciation of Arsenic

2014· article· en· W2314771244 on OpenAlexaff
Emanuela Pitzalis, Massimo Onor, Marco Carlo Mascherpa, Giacomo Pacchi, Zoltán Mester, Alessandro D’Ulivo

Bibliographic record

VenueAnalytical Chemistry · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsChemistryArsenicProtonationReactivity (psychology)Inorganic chemistryAmmonia boraneHydrideBoraneMasking agentOrganic chemistryDehydrogenationMetalIon

Abstract

fetched live from OpenAlex

The efficiency of chemical generation of arsanes from inorganic arsenic, monomethylarsonic acid (MMAA) and dimethylarsinic acid (DMAA), to arsane, AsH3, monomethylarsane, CH3AsH2 (MMA), and dimethylarsane, (CH3)2AsH (DMA), has been investigated in different reaction media with the aim to better elucidate the mechanisms controlling their generation process and to find the experimental conditions to implement a nonchromatographic arsenic speciation analytical method, which is based on the selective determination of some arsenic species. Studies were performed by continuous flow hydride generation coupled with atomic spectrometry (CF-HG-AS), using different reductants such as borane-ammonia (AB), borane-tert-butylamine (TBAB), and sodium tetrahydridoborate (THB) in HCl and HClO4 media, in the presence or absence of L-cysteine (Cys). The efficiency of HG processes for MMA and DMA is mainly controlled by the reactivity of the substrates with the borane, which could be strongly influenced by the formation of ion couples. The protonation of arsane did not play a significant role in the employed reaction system. By taking advantage of the different reactivity pattern of As species in selected generation conditions, DMAA and MMAA could be selectively determined in 0.5 and 10 M HClO4 solutions, respectively, in the presence of Cys, with AB as the reducing agent. The presence of Cys as a masking agent and the peculiar reducing properties of AB ensured a good control of interferences, as far as it has been observed for Co(II), Ni(II), Cu(II), Fe(II), Fe(III). The overall time needed to complete the prereduction step has been verified for MMAA and DMAA at different acidities in order to achieve the best selectivity. The selective determination of DMAA with AB/Cys in HClO4 has been optimized and applied to certified reference materials (CRMs) of natural waters CASS-4, SLRS-4, and NASS-4 (NRCC). The estimation of DMAA concentration allows us to correct the concentration of As(III) for the interference of DMAA in the selective determination of As(III) according to a selective HG method recently reported.

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 score0.043
Threshold uncertainty score0.275

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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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

Citations26
Published2014
Admission routes1
Has abstractyes

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