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Record W3084090424 · doi:10.1680/jenes.20.00028

Efficiency of plasma treatment of water contaminated with persistent organic molecules

2020· article· en· W3084090424 on OpenAlexvenueno aff
V.O. Bereka, Ihor Boshko, I.P. Kondratenko, Yu.L. Zabulonov, D. V. Charnyi, Y. A. Onanko, Andrii Marynin, Volodymyr Krasnoholovets

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2020
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsWastewaterOzoneMethylene blueAqueous solutionDielectric barrier dischargeChemistryPlasmaSewage treatmentWater treatmentMaterials scienceAnalytical Chemistry (journal)ElectrodeEnvironmental chemistryEnvironmental engineeringEnvironmental scienceOrganic chemistryPhotocatalysis

Abstract

fetched live from OpenAlex

The authors examined the efficiency of plasma-pulse-driven dielectric barrier discharge oxidation of a solution of methylene blue (C 16 H 18 ClN 2 S) and wastewater from dye-producing plants, in which the main component is 2,4-dinitrotoluene (CH 3 C 4 H 3 (NO 2 ) 2 ), as well as models of the organic component of wastewater of nuclear power plants, the main constituent of which is a phosphate (PO 4 3− )-based detergent with a 26.8% surfactant content. Water films with a thickness of 0.1 mm were processed with a discharge voltage equal to 3 × 10 11 V/s. The energy efficiency of water treatment was studied against the specific energy input, the frequency of the repetition of the discharge pulses and the parameters of the fluid and gas going through the discharge chamber. The highest energy output was reached at an air velocity in the discharge chamber of ∼1 cm/s, which provides a concentration of ozone (O 3 ) of ∼1.5 mg/l. The highest energy output, the processing of 87 g of wastewater with 1 kWh, was obtained by treating an aqueous solution of methylene blue with an initial concentration of 50 mg/l at 65% decomposition. An energy consumption of 10 J is the most efficient value for treatment of the tested 10 ml wastewater samples.

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 score0.038
Threshold uncertainty score0.125

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 head0.006
GPT teacher head0.175
Teacher spread0.169 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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