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Record W1973438915 · doi:10.1002/cjce.20033

Ultrasound‐facilitated electro‐oxidation for treating cyan ink effluent

2008· article· en· W1973438915 on OpenAlexvenueno aff
Chee‐Yong Chua, Kai‐Chee Loh

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsnot available
FundersNational University of Singapore
KeywordsSonicationHydrogen peroxideChemistryCyanEffluentRedoxKineticsCopperElectrolysisChemical engineeringNuclear chemistryInorganic chemistryOrganic chemistryChromatographyWaste managementElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

Abstract The feasibility of using ultrasonication in combination with the Fenton's reaction was investigated for treating cyan ink effluent. A two‐step treatment process was developed—the first step was an ultrasound‐assisted electro‐oxidation, while the second was chemical oxidation through the addition of hydrogen peroxide. The use of electro‐oxidation in the first step significantly reduced the amount of iron needed compared with the conventional Fenton's reaction, resulting in a 98% reduction in the amount of sludge produced. A simple technique based on refractive index measurements was introduced as a rapid way to quantify the amount of sludge produced. It was postulated that ultrasonication in the presence of iron (from electrolysis) in the first step converted the ink components into reaction intermediates which were more amenable to peroxide oxidation in the second step. These intermediates were quantified by ultra‐violet absorption at wavelengths of 275–400 nm. The two‐step treatment process was able to reduce the COD and copper contents in the ink waste water to within the discharge limit, which conventional Fenton's reaction was unable to meet for copper discharge. The same COD removal was also achieved in about half the time. Kinetics study performed to further understand the reaction mechanisms show second‐order kinetics for both steps with activation energies of 18.2 and 20.4 kJ/mol for steps 1 and 2, respectively.

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 score0.073
Threshold uncertainty score0.304

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 head0.007
GPT teacher head0.188
Teacher spread0.181 · 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
Published2008
Admission routes1
Has abstractyes

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