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Record W1964419862 · doi:10.1021/ie9008502

Reductive Precipitation of Elemental Selenium from Selenious Acidic Solutions Using Sodium Dithionite

2009· article· en· W1964419862 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2009
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSodium dithioniteDithioniteSeleniumChemistryPrecipitationInorganic chemistryStoichiometryDecompositionSulfurNuclear chemistrySodium hydroxideOrganic chemistry

Abstract

fetched live from OpenAlex

In this work, the batch-reactor reduction of selenious acid (H 2 SeO 3 ) species with sodium dithionite (Na 2 S 2 O 4 ) from weakly acidic sulfate solutions containing 300 mg/L of selenium at 23 ο C was studied. The results showed that, at an initial pH value of <1.7 and a dithionite stoichiometric excess of >3, <0.5 μg/L of selenium(IV) remained in solution after reduction. The reductive precipitation reaction started as soon as dithionite was added in the selenium-bearing solution and was completed within less than a minute. However, it was determined that the precipitate was not stable in the presence of the dithionite decomposition byproduct and partially redissolved after several hours. The reaction product, characterized using X-ray diffraction, scanning electron microscopy, and chemical analysis, was determined to be red amorphous selenium. The precipitate, in addition to elemental selenium, was determined to contain monoclinic sulfur that was apparently formed via a side reaction pathway involving the decomposition of dithionite.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.140
GPT teacher head0.357
Teacher spread0.217 · 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