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Removal of Methylene Blue in aqueous solutions by Electrocoagulation process: Adsorption, Kinetics, studies

2018· article· en· W2907092756 on OpenAlexaff
Alain Stéphane Assémian, Konan Edmond Kouassi, Kopoin Adouby, Patrick Drogui, David Boa

Bibliographic record

VenueEuropean Journal of Chemistry · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsElectrocoagulationChemistryMethylene blueAdsorptionAqueous solutionAnodeCathodeKineticsElectrochemistryKinetic energyCurrent densityNuclear chemistryAnalytical Chemistry (journal)Inorganic chemistryChromatographyElectrodeCatalysisOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The purpose of this study is to understand the mechanism driving the removal of methylene blue through electrocoagulation process. Experiments were carried out using iron as anode and cathode in a batch electrochemical cell operated in a monopolar configuration. The effects of operating parameters (initial pH, current density, initial dye concentration and energy consumption) on the removal of methylene blue from solution were investigated. The results showed that the optimum removal efficiency of 93.2% was achieved for a current density of 9.66 mA/cm2, optimal pH of 8±0.01 with a specific energy consumption of 7.451 kWh/m3. Afterwards, first and second-order rate equations were successively applied to study adsorption kinetics models. On top of usual correlation coefficients (r2), statistical test Chi-square (χ2) were applied to evaluate goodness of fit and consequently find out the best kinetic model. Results showed that MB adsorption process onto iron hydroxides formed in aqueous solution during electrocoagulation treatment followed a second-order kinetic.

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 score0.005
Threshold uncertainty score0.328

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 head0.266
Teacher spread0.250 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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