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Record W2731642337 · doi:10.5004/dwt.2017.20766

Removal of 2,4-dichlorophenol from aqueous solution using ultrasonic/H2O2

2017· article· en· W2731642337 on OpenAlexaff
Mehrnaz Sadrnourmohamadi, Ali Poormohammadi, Halime Almasi, Ghorban Asgari, Adel Ahmadzadeh, Abdolmotaleb Seid‐Mohammadi

Bibliographic record

VenueDesalination and Water Treatment · 2017
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
Keywords2,4-DichlorophenolUltrasonic sensorAqueous solutionChemistryWaste managementMaterials scienceChemical engineeringNuclear chemistryEngineeringAcousticsOrganic chemistryGeologyPhysics

Abstract

fetched live from OpenAlex

ABSTRACT The effect of ultrasound/hydrogen peroxide on the removal efficiency of 2,4-dichlorophenol (2,4-DCP) from aqueous solutions was investigated. The effects of solution pH, sonication time, H 2 O 2 concentration, and tert-butanol (t-BuOH) concentration were also examined on process efficiency in 2,4-DCP degradation. Ultrasonic treatment at 20 kHz in combination with hydrogen peroxide resulted in increased removal efficiency of 2,4-DCP. The removal efficiency was improved with increasing sonication time, and the maximum efficiency was obtained at pH of 3. t-BuOH decreased the removal efficiency of 2,4-DCP under optimum conditions by quenching hydroxyl radicals. According to the obtained results, 74.6% chemical oxygen demand removal was achieved with US/H 2 O 2 process after 90 min of reaction time at pH of 3 and H 2 O 2 concentration of 0.1 mol/L. The decomposition kinetic data fitted well with the pseudo-first-order kinetic model with a rate constant of 0.025 min –1 .

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 score0.007
Threshold uncertainty score0.307

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 head0.034
GPT teacher head0.278
Teacher spread0.244 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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