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Electrokinetic Removal of Cd and Cu from Mine Tailing: EDTA Enhancement and Voltage Intensity Effects

2020· article· en· W3101921318 on OpenAlexaff
Mahdiyeh Sadat Torabi, Gholamreza Asadollahfardi, Milad Rezaee, Niloufar Bahrami Panah

Bibliographic record

VenueJournal of Hazardous Toxic and Radioactive Waste · 2020
Typearticle
Languageen
FieldEngineering
TopicElectrokinetic Soil Remediation Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEthylenediaminetetraacetic acidElectrokinetic phenomenaDistilled waterElectrokinetic remediationChemistryCadmiumCopperMetalElectro-osmosisIntensity (physics)ChelationInorganic chemistryEnvironmental chemistryElectrophoresisChromatographyElectrode

Abstract

fetched live from OpenAlex

This study investigated electrokinetic removal of cadmium and copper from contaminated soil of the Koushk mine tailing dam. The effect of ethylenediaminetetraacetic acid (EDTA) 0.1 M as catholyte and NaOH 0.1 M as an anolyte was investigated. Two voltage gradients, 1 and 2 V/cm, were used in the study. Therefore, six sets of tests were conducted, five 10-day tests and one 13-day test. The study measured pH, electroosmosis flow, electric current, and the concentration of the target metals. The energy expenditure of each test was calculated based on the measured electrical current. The results indicate that usage of EDTA as the catholyte along with distilled water as anolyte did not enhance the cadmium and copper removal. This could be due to antagonistically affected results from competition between metal cations and the EDTA. Application of NaOH instead of distilled water as an anolyte incorporated with EDTA as catholyte showed better remediation results, which might be due to facilitating metal–EDTA movement and formation. A study on the voltage intensity revealed that higher voltage applications can improve metal removal. The final 13-day test showed better removal efficiency compared with similar 10-day test.

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 score0.022
Threshold uncertainty score0.630

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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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Same venueJournal of Hazardous Toxic and Radioactive WasteSame topicElectrokinetic Soil Remediation TechniquesFrench-language works237,207