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Record W2048732335 · doi:10.1002/jctb.841

Treatment of wastewater using a combined biological and electrochemical technique

2003· article· en· W2048732335 on OpenAlexafffund
Huu Doan, Jianning Wu, Edwin Boithi, Megan Storrar

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2003
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAerationWastewaterElectrochemistryElectrolyteSupporting electrolyteChlorideChemistryVolumetric flow rateNuclear chemistryPulp and paper industryChromatographyEnvironmental engineeringElectrodeEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Simulated wastewater containing propylene glycol methyl ether, Ni 2+ and Zn 2+ was treated using an aerated packed column. An electrochemical cell was also installed beneath the packing support in the bottom section of the column. The flowrate of water through the column was maintained at 8.94 kg m −2 s −1 . For 72 h of treatment using aeration alone, the BOD 5 was reduced by 43%, 56%, and 54% for air flowrates of 0.00094, 0.00141, and 0.00188 kg m −2 s −1 respectively. On the other hand, when the electrochemical cell was activated without aeration, the BOD 5 was reduced by 15%, 17%, and 23% at the applied voltages of 8, 16, and 25 V respectively. When both aeration of the wastewater and a voltage of 25 V were used, the percentage removal of BOD 5 , on the average, was about 65% for all air flowrates used. The concentrations of Ni 2+ and Zn 2+ were also reduced by 57% and 61% respectively, in the absence of the supporting electrolyte. The addition of potassium chloride (100 ppm) as the supporting electrolyte enhanced the metal removal by 30%. Copyright © 2003 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 categoriesResearch integrity
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 score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0020.001
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 head0.012
GPT teacher head0.247
Teacher spread0.235 · 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.

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
Published2003
Admission routes2
Has abstractyes

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