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Record W2091399175 · doi:10.1021/ie403772t

Degradation of 4-Chlorobenzoïc Acid in a Thin Falling Film Dielectric Barrier Discharge Reactor

2014· article· en· W2091399175 on OpenAlexaff
Olivier Lesage, Thibault Roques‐Carmes, Jean‐Marc Commenge, Xavier Duten, Michaël Tatoulian, S. Cavadias, Diego Mantovani, Stéphanie Ognier

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDielectric barrier dischargeDegradation (telecommunications)NOxElectrodeMaterials scienceBrassPlasmaAnalytical Chemistry (journal)Contact angleDielectricChemical engineeringChemistryComposite materialMetallurgyChromatographyOptoelectronicsOrganic chemistryElectrical engineeringPhysical chemistry

Abstract

fetched live from OpenAlex

The degradation of 4-chlorobenzoïc acid (4-CBA) in water was performed using an innovative plasma dielectric barrier discharge (DBD) process. The influence of the electrode material directly in contact with the solution was examined. The roles of power and frequency discharge on the treatment efficiency were also evaluated. The hydrodynamic behavior of the solution and physical aspect of the discharge were studied by contact angle measurements and intensified charge-coupled device (iCCD) acquisition, respectively. Analyses showed that the use of stainless steel (SS) resulted in a better efficiency of the process compared with the use of brass (B). The degradation of 4-CBA was 80 and 50% for SS and B, respectively, after 1 h of plasma discharge. The presence of NO x in the plasma discharge and corrosion reaction occurring on the brass surface limited the degradation of the 4-CBA. It was possible to observe two main byproducts and identify the 4-chlorosalicylic acid or 4-chlorohydroxybenzoïc acid. Ultimately, the discharge frequency was reduced to limit the production of NO x species.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.059
GPT teacher head0.318
Teacher spread0.259 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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