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Record W2331559689 · doi:10.1021/ac1024717

Arsenic Speciation in Sulfidic Waters: Reconciling Contradictory Spectroscopic and Chromatographic Evidence

2010· article· en· W2331559689 on OpenAlexaff
Britta Planer‐Friedrich, Elke Suess, Andreas C. Scheinost, Dirk Wallschläger

Bibliographic record

VenueAnalytical Chemistry · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsTrent University
FundersAmerican Society for Surgery of the Hand
KeywordsChemistryArsenicArseniteDilutionSulfurSulfideGenetic algorithmAtmospheric oxygenAbsorption (acoustics)Inorganic chemistryFraction (chemistry)X-ray absorption spectroscopyOxygenAbsorption spectroscopyIsotope dilutionEnvironmental chemistryAnalytical Chemistry (journal)ChromatographyMass spectrometryOrganic chemistry

Abstract

fetched live from OpenAlex

In recent years, analytical methods have been developed that have demonstrated that soluble arsenic-sulfur species constitute a major fraction of dissolved arsenic in sulfidic waters. However, an intense debate is going on about the exact chemical nature of these compounds, since X-ray absorption spectroscopy (XAS) data generated at higher (mmol/L) concentrations suggest the presence of (oxy)thioarsenites in such waters, while ion chromatographic (IC) and mass spectroscopic data at lower (μmol/L to nmol/L) concentrations indicate the presence of (oxy)thioarsenates. In this contribution, we connect and explain these two apparently different types of results. We show by XAS that thioarsenites are the primary reaction products of arsenite and sulfide in geochemical model experiments in the complete absence of oxygen. However, thioarsenites are extremely unstable toward oxidation, and convert rapidly into thioarsenates when exposed to atmospheric oxygen, e.g., while waiting for analysis on the chromatographic autosampler. This problem can only be eliminated when the entire chromatographic process is conducted inside a glovebox. We also show that thioarsenites are unstable toward sample dilution, which is commonly employed prior to chromatographic analysis when ultrasensitive detectors like ICP-MS are used. This instability has two main reasons: if pH changes during dilution, then equilibria between individual arsenic-sulfur species rearrange rapidly due to their different stability regions within the pH range, and if pH is kept constant during dilution, then this changes the ratio between OH(-) and SH(-) in solution, which in turn shifts the underlying speciation equilibria. This problem is avoided by analyzing samples undiluted. Our studies show that thioarsenites appear as thioarsenates in IC analyses if oxygen is not excluded completely, and as arsenite if samples are diluted in alkaline anoxic medium. This also points out that thioarsenites are necessary intermediates in the formation of thioarsenates.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.999

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)0.0020.000

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 head0.009
GPT teacher head0.231
Teacher spread0.221 · 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 designObservational
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

Citations113
Published2010
Admission routes1
Has abstractyes

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