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Degradation of Bisphenol S Using O3 and/or H2O2 with UV in a Flow-Through Reactor

2016· article· en· W2298612319 on OpenAlexafffund
Mitra Mehrabani-Zeinabad, Gopal Achari, Cooper H. Langford

Bibliographic record

VenueJournal of Environmental Engineering · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaHealth CanadaUniversity of Pittsburgh
KeywordsMineralization (soil science)ChemistryWastewaterBisphenol ADegradation (telecommunications)OzoneEnvironmental chemistryTotal organic carbonHydrogen peroxideUltravioletSewage treatmentKineticsPulp and paper industryNuclear chemistryChromatographyEnvironmental engineeringEnvironmental scienceOrganic chemistryMaterials scienceNitrogen

Abstract

fetched live from OpenAlex

In this paper, degradation of bisphenol S (BPS) in spiked water and postsecondary-treated wastewater by a variety of oxidation processes was investigated in a flow-through reactor. Kinetics of degradation in O3, ultraviolet (UV)/O3, UV/H2O2, and UV/O3/H2O2 were determined and reported. The degradation of BPS by O3 alone was competitive with the UV-promoted methods. An increase in BPS loss with O3 was observed in postsecondary-treated wastewater, demonstrating its potential application as a treatment method for BPS. Total organic carbon (TOC) as an indicator of the extent of organic compound mineralization and sulfate as a by-product produced during degradation were measured. It was noted that although the loss of BPS was similar in the UV/O3/H2O2 and the O3 processes, the overall mineralization rate was different, indicating that the combination technique offers a more efficient system in terms of overall mineralization rate. The UV/O3/H2O2 process was the most effective and led to complete mineralization of 50 mg/L BPS within 90 min in both spiked water and postsecondary-treated wastewater.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.557

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.238
Teacher spread0.218 · 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

Citations17
Published2016
Admission routes2
Has abstractyes

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