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Effect of temperature on methylene blue decolorization in aqueous medium in electrical discharge plasma reactor

2011· article· en· W2121624433 on OpenAlexfundno aff
Luís Otávio de Brito Benetoli, Bruno Mena Cadorin, Cícero da S Postiglione, Ivan Gonçalves de Souza, Nito Ângelo Debacher

Bibliographic record

VenueJournal of the Brazilian Chemical Society · 2011
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoUniversidade Federal de Santa CatarinaMcMaster University
KeywordsMethylene blueHydrogen peroxideAqueous solutionChemistryPlasmaActivation energyAqueous mediumNuclear chemistryPhysical chemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

This study investigates the effect of bath solution temperature on methylene blue decolorization in a point-to-plate electrical discharge plasma reactor. The results show an increase in the initial percentage of dye decolorization with an increase in the bath solution temperature from 4 to 28 ºC, but from 28 to 37 ºC there was no change. However, when the temperature was raised to 47 ºC the initial dye decolorization percentage decreased. The hydrogen peroxide (H2O2) formation in water during the plasma treatment decreased as the bath temperature increased from 4 to 47 ºC. The dye decolorization reaction occurred via a complex mechanism where zero- and first-order processes took place at the same temperature, following zero-order in the initial step and first-order with the progress of the reaction. The activation energy involved in the first-order process was 13.09 kJ mol-1.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.255
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), 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

Citations64
Published2011
Admission routes1
Has abstractyes

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