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Record W4237783273 · doi:10.32920/ryerson.14653710

Electrochemical removal of zinc and nickel ions from wastewater using porous electrodes

2021· preprint· en· W4237783273 on OpenAlexaff
Qazi Sabir

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceNickelElectrochemistryAnodeZincAluminiumElectrodeWastewaterCathodePorosityFlux (metallurgy)Analytical Chemistry (journal)MetallurgyComposite materialChemistryEnvironmental engineeringChromatography

Abstract

fetched live from OpenAlex

Simulated wastewater containing Ni++ and Zn++ was treated using an electrochemical cell. Porous aluminum cathode and porous stainless steel anode were used in a flow-through configuration. For porous catholdes, both aluminium foam and corrugated aluminum plates having perforations were used. To study the effects of applied voltage and volumetric liquid flux on the removal of Ni++ and Zn++, the electrochemical cell was operated for 48 hours at different applied voltages of 5, 10, 15, 20 and 25 V, and at different volumetric liquid fluxes both in the laminar (0.00471 and 0.00943 m³.m-².s-¹) and turbulent regimes (0.01414, 0.01886 and 0.02357 m³.m-².s-¹). For the maximum removal of both nickel and zinc ions, the optimum applied voltage and volumetric liquid flux were found to be 12 V and 0.02357 m³.m-².s-¹, respectively; under these operating conditions, the concentrations of Ni++ and Zn++ in the simulated wastewater were reduced by 85.5% and 98%, respectively. Operating beyond an applied voltage of 12 V, the removal of Zn++ was slightly improved and achieved a maximum value of 99.05% at 25 V; however, an opposite trend was observed in case of Ni++ removal, which finally decreased to 56% at 25 V., because of the excessive precipitation of Ni++ as nickel hydoroxide.

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 categoriesMeta-epidemiology (narrow)
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 score0.063
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.252
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

Citations0
Published2021
Admission routes1
Has abstractyes

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