MétaCan
Menu
Back to cohort
Record W2038541157 · doi:10.1071/en09094

Impact of various inorganic oxyanions on the removal rates of hexavalent chromium mediated by zero-valent iron

2010· article· en· W2038541157 on OpenAlexafffund
Mario Rivero-Huguet, William D. Marshall

Bibliographic record

VenueEnvironmental Chemistry · 2010
Typearticle
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsChemistryZerovalent ironHexavalent chromiumSulfateNitrateChromiumInorganic chemistryReaction rate constantSilicateContext (archaeology)PhosphateArsenateKineticsNuclear chemistryAdsorptionArsenic

Abstract

fetched live from OpenAlex

Environmental context. Oxyanions in soil extract can interfere with the zero valent iron induced reduction of chromium(VI) to chromium(III). At pH 6, the reaction rate was decreased (2 to 6-fold) by an equivalent of arsenate, phosphate or silicate but was increased by sulfate and remained unchanged by borate or nitrate. At pH 2, not only was the rate of reaction dramatically increased (~900-fold) but interferences from the major components of soil solution (nitrate, silicate and sulfate) were minimised. Abstract. The rate of zero-valent iron (ZVI) mediated reduction of CrVI was dependent on the condition of the ZVI surface, the pH of the medium and on the presence of inorganic oxyanions that can interfere with the process by competing for active sites on the ZVI surface. Whereas at pH 2, a single exponential decay provided an acceptable fit to the data, for pH 6 an appreciably better fit to the data was obtained with the sum of two exponential decays. The surface area normalised rate constant (kSA1) corresponding to the first decay was considered to model reactions at exposed active sites and kSA2, corresponding to the second decay, was considered to model decomposition kinetics through an intervening oxyhydroxide layer above the ZVI surface. The rate of CrVI reduction was decreased ~900-fold when the pH was increased from 2 to 6 in the absence of competing ions. At pH 2, interferences from the major components of soil solution (nitrate, silicate and sulfate) were minimised.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.998

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)0.0030.000

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 head0.004
GPT teacher head0.195
Teacher spread0.191 · 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

Citations11
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental ChemistrySame topicEnvironmental remediation with nanomaterialsFrench-language works237,207