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Record W2021076112 · doi:10.1002/cjce.22040

UVC based advanced oxidation for decolourization and mineralization of calconcarboxylic acid in aqueous solution: Eco‐toxicological effect of post treated solutions and its remedy

2014· article· en· W2021076112 on OpenAlexvenueno aff
Mihir Kumar Sahoo, Bhauk Sinha, R. N. Sharan

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMineralization (soil science)Hydrogen peroxideChemistryAqueous solutionAmmoniumPollutantPeroxideEnvironmental chemistryInorganic chemistryNuclear chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The present study reports two important aspects of wastewater treatment by UVC light (λ = 254 nm) in the presence of hydrogen peroxide (HP) and ammonium persulphate (APS) taking Calconcarboxylic acid (CCA) as a model pollutant. The first part deals with the effect of various operational parameters on the decolourization and mineralization of CCA. Most importantly, the eco‐toxicological effect of the treated solutions was examined on the basis of E. coli growth inhibition bioassay and the remedy for the same has been suggested in the second part. Although both oxidants show higher mineralization at pH 1, APS is preferred over HP for having higher mineralization, biodetoxification, and electrical energy efficiency. The presence of –COOH group in CCA has detrimental effect on its mineralization and biodetoxification, as well as on the electrical energy efficiency. Although a rise in mineralization at all pH is observed by the removal of the –COOH group, it is significant in alkaline media. Since treatment at pH 1 is not ideal for real scale applications, it is advisable to remove the –COOH group before treatment, so as to make treatment possible in alkaline media. Removal of –COOH group also leads to higher biodetoxification in a shorter treatment period.

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)0.001
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 score0.186
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.197
Teacher spread0.191 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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