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Record W1991311126 · doi:10.3390/ma7031880

Sorptive Uptake Studies of an Aryl-Arsenical with Iron Oxide Composites on an Activated Carbon Support

2014· article· en· W1991311126 on OpenAlexafffund
Jae Hong Kwon, Lee D. Wilson, Ramaswami Sammynaiken

Bibliographic record

VenueMaterials · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsAdsorptionMagnetiteGoethiteIron oxideSorptionChemistryNuclear chemistryInorganic chemistryMaterials scienceOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Sorption uptake kinetics and equilibrium studies for 4-hydroxy-3-nitrobenzene arsonic acid (roxarsone) was evaluated with synthetic magnetite (Mag-P), commercial magnetite (Mag-C), magnetite 10%, 19%, and 32% composite material (CM-10, -19, -32) that contains granular activated carbon (GAC), and synthetic goethite at pH 7.00 in water at 21 °C for 24 h. GAC showed the highest sorptive removal of roxarsone and the relative uptake for each sorbent material with roxarsone are listed in descending order as follows: GAC (471 mg/g) > goethite (418 mg/g) > CM-10 (377 mg/g) CM-19 (254 mg/g) > CM-32 (227 mg/g) > Mag-P (132 mg/g) > Mag-C (29.5 mg/g). The As (V) moiety of roxarsone is adsorbed onto the surface of the iron oxide/oxyhydrate and is inferred as inner-sphere surface complexes; monodentate-mononuclear, bidentate-mononuclear, and bidentate-binuclear depending on the protolytic speciation of roxarsone. The phenyl ring of roxarsone provides the primary driving force for the sorptive interaction with the graphene surface of GAC and its composites. Thus, magnetite composites are proposed as multi-purpose adsorbents for the co-removal of inorganic and organic arsenicals due to the presence of graphenic and iron oxide active adsorption sites.

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 categoriesnone
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 score0.107
Threshold uncertainty score0.535

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)0.0000.000

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 head0.016
GPT teacher head0.261
Teacher spread0.245 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations40
Published2014
Admission routes2
Has abstractyes

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