MétaCan
Menu
Back to cohort
Record W2070665457 · doi:10.1002/jctb.1575

Combined electrochemical and biological treatment of industrial wastewater using porous electrodes

2006· article· en· W2070665457 on OpenAlexafffund
Huu Doan, Jiangning Wu, Robert Mitzakov

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrochemistryAnodeCathodeElectrodeMaterials sciencePorosityVolumetric flow rateMass transferChemical engineeringChemistryMetallurgyChromatographyComposite material

Abstract

fetched live from OpenAlex

Abstract Zn 2+ , Ni 2+ and propylene glycol methyl ether were simultaneously removed from simulated wastewater in a column consisting of an aerated packed bed and an electrochemical cell with a porous aluminium foam cathode and a porous stainless steel anode. After 48 h of sole electrochemical treatment at a liquid rate of 0.00183 m 3 m −2 s −1 , Zn 2+ and Ni 2+ were reduced by 95 and 80% respectively. In the turbulent flow regime with liquid rates varied from 0.0137 to 0.0366 m 3 m −2 s −1 , both Zn 2+ and Ni 2+ removal decreased by about 15% rather than increased as expected for a mass transfer‐controlled process. This can be attributed to bubble formation at the cathode surface under turbulent flow, which led to a lower active surface area for the electrodeposition of metal ions. Porous electrodes enhanced the metal removal by 17 and 60% for Zn 2+ and Ni 2+ respectively as compared with flat plate electrodes. Using combined biological and electrochemical treatment at a water rate of 0.00183 m 3 m −2 s −1 and an air rate of 0.0518 m 3 m −2 s −1 , 99% of Zn 2+ and 95% of Ni 2+ were removed. In addition, the 5 day biological oxygen demand (BOD 5 ) was reduced by 58% concurrently over 72 h of treatment. Copyright © 2006 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)0.000
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 score0.014
Threshold uncertainty score0.946

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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

Citations23
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Chemical Technology & BiotechnologySame topicMicrobial Fuel Cells and BioremediationFrench-language works237,207