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Record W2604006593 · doi:10.1021/acs.est.7b00089

Impact of Pore Size on Fenton Oxidation of Methyl Orange Adsorbed on Magnetic Carbon Materials: Trade-Off between Capacity and Regenerability

2017· article· en· W2604006593 on OpenAlexaff
Ye Xiao, Josephine M. Hill

Bibliographic record

VenueEnvironmental Science & Technology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAdsorptionMethyl orangeChemistryActivated carbonCarbon fibersChemical engineeringEnvironmental chemistryInorganic chemistryMaterials scienceCatalysisOrganic chemistryComposite materialPhotocatalysisComposite number

Abstract

fetched live from OpenAlex

The economic cleanup of wastewater continues to be an active area of research. In this study, the influence of pore size on regeneration by Fenton oxidation for carbon materials with adsorbed methyl orange (MO) was investigated. More specifically three carbon supports, with pore sizes ranging from mainly microporous to half microporous-half mesoporous to mainly mesoporous, were impregnated with γ-Fe 2 O 3 to make them magnetic and easy to separate from solution. The carbon samples were characterized before adsorption and after regeneration with hydrogen peroxide at 20 °C. In addition, adsorption kinetics and isotherms were collected, and the Weber–Morris intraparticle diffusion model and Freundlich isotherm model fit to the data. The adsorption capacity increased with increasing microporosity while the regeneration efficiency increased with increasing mesoporosity. Further experiments with varying regeneration and adsorption conditions suggested that the regeneration process may be kinetically limited. The MO adsorbed in the micropores was strongly adsorbed and difficult to remove unlike the MO adsorbed in the mesopores, which could be reacted under relatively mild conditions. Thus, there was a trade-off between adsorption capacity and regeneration.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
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 studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.265
Teacher spread0.251 · 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

Citations63
Published2017
Admission routes1
Has abstractyes

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