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Record W1585187334 · doi:10.1002/jctb.4399

Sonoelectrochemical oxidation of carbamazepine in waters: optimization using response surface methodology

2014· article· en· W1585187334 on OpenAlexaff
Nam Nghiep Tran, Patrick Drogui, Satinder Kaur Brar

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsEffluentElectrolysisWastewaterResponse surface methodologySonicationChemistryCarbamazepineCentral composite designPhosphoniumPulp and paper industryChromatographyElectrodeNuclear chemistryWaste managementEnvironmental scienceEnvironmental engineeringOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Abstract BACKGROUND The decomposition of carbamazepine ( CBZ ) in synthetic solution and in municipal effluent was investigated using a sono‐electrochemical reactor. Sono‐electrolysis was conducted using two concentrical electrodes installed in a cylindrical reactor containing a ceramic transducer. RESULTS CBZ concentration ( C 0 =10 mg L −1 ) optimally diminished up to 90% by applying a current intensity of 4.86 A for 177 min and by imposing an ultrasound power of 38.29 W. The optimal conditions were subsequently applied for tertiary treatment of municipal wastewater effluent contaminated with 10 µg CBZ L −1 . The reported removal efficiencies of CBZ , TOC , COD and colour were 93%, 60%, 93% and 86%, respectively. Likewise, the toxicity was completely removed (bacterium Vibrio fisheri ) from the municipal wastewater effluent (>96%). CONCLUSIONS The advantages of coupling ultrasonication and electrooxidation ( US‐EO ) treatment for CBZ removal were demonstrated. An experimental design methodology based on response surface methodology was applied to determine the optimal experimental conditions in terms of cost/effectiveness of removal. © 2014 Society of Chemical Industry

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)0.001
metaresearch head score (Gemma)0.001
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 score0.154
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.271
Teacher spread0.255 · 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

Citations25
Published2014
Admission routes1
Has abstractyes

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