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Record W1975943479 · doi:10.1021/es702885f

Extraction of Hexavalent Chromium from Chromated Copper Arsenate Treated Wood under Alkaline Conditions

2008· article· en· W1975943479 on OpenAlexaff
Suzana Radivojevic, Paul Cooper

Bibliographic record

VenueEnvironmental Science & Technology · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChromated copper arsenateChemistryChromiumHexavalent chromiumNuclear chemistryChromium CompoundsExtraction (chemistry)Leaching (pedology)CopperOrganic chemistry

Abstract

fetched live from OpenAlex

Information on chromium (Cr) oxidation states is essential for the assessment of environmental and health risks associated with the overall life-cycle of chromated copper arsenate (CCA) treated wood products because of differences in toxicity between trivalent [Cr(III)] and hexavalent [Cr(VI)] chromium compounds. Hypothetical Cr(VI) fixation products were investigated in CCA type C treated sawdust of aspen and red pine during or following preservative fixation by extraction with Cr(VI)-specific extractants. Cr(VI) was found only in alkaline extracts of treated wood. A major source of Cr(VI) was method-induced oxidation of fixed Cr(III) during alkaline extraction, as confirmed by demonstrated oxidation of Cr(III) from CrCl3 treated wood. Oxidation of nontoxic and immobile Cr(III) to toxic and mobile Cr(VI) was facilitated by the presence of wood at pH > 8.5. Thermodynamic equilibrium between Cr(III) and Cr(VI) is affected by pH, temperature, rates of dissolution of CrIII) compounds, and oxygen availability. Results of this study recommend against alkaline extraction protocols for determination of Cr(VI) in treated wood. This Cr oxidation mechanism can act as a previously unrecognized route for generation of hazardous Cr(VI) if CCA treated wood is exposed to alkaline conditions during its production, use, or waste management.

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 categoriesScience and technology studies, Insufficient 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
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 studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.022
GPT teacher head0.230
Teacher spread0.208 · 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
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

Citations14
Published2008
Admission routes1
Has abstractyes

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