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Record W4233431789 · doi:10.32920/ryerson.14665257

Simultaneous removal of metal ions and oxidation of linear alkylbenzene sulfonate by combined electrochemical and photocatalytic processes

2021· preprint· en· W4233431789 on OpenAlexaff
Mitra Saidi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPhotocatalysisElectrochemistryElectrolyteZincInorganic chemistryChemistryNickelMetal ions in aqueous solutionMetalSupporting electrolyteDegradation (telecommunications)IonVolumetric flow rateElectrodeCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Simultaneous electrochemical removal of Zn⁺₂ and Ni⁺₂ ions and photooxidation of linear alkylbenzene sulfonate (LAS) over TiO₂ particles were investigated. To achieve this objective, first, the effect of different variables such as current density, pH and flow rate on sole electrochemical reduction of metal ions was studied. Both controlling pH in the range of 5.5-6 and increasing the liquid volumetric flux effectively improved the rate of Zn⁺₂ ion reduction, but they did not have any significant effect on the rate of Ni⁺₂ ion reduction. Under optimum operating conditions ... and using total electrolyte volume of 6 L, zinc and nickel were reduced by 86% and 53% respectively, over a 7-hour treatment period. Sole photocatalytic treatment of LAS and controlling pH between 5-5.5 resulted in 60% LAS degradation. However, temperature and flow rate did not have any considerable effect on the rate of LAS degradation compared to the photocatalytic system alone. LAS was degraded in the combined system by 76% compared to 60% in the sole photocatalytic system. However, using the combined system, zinc and nickel were reduced by 81% and 47% respectively, which were slightly less than those obtained in the electrochemical system alone.

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 score0.018
Threshold uncertainty score0.793

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 science0.0000.001
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 head0.232
Teacher spread0.225 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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