Chemical Generation of Arsane and Methylarsanes with Amine Boranes. Potentialities for Nonchromatographic Speciation of Arsenic
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".