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Record W2150583340 · doi:10.1139/s04-060

Ultraviolet photooxidation for the biodegradability enhancement of airborne o-xylene

2005· article· en· W2150583340 on OpenAlexfundvenueno aff
Madjid Mohseni, Loo‐Hwa Koh, David CS Kuhn, D. Grant Allen

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2005
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiodegradationChemistryPhotodissociationOxidizing agentXyleneOzoneUltravioletBiofilterHydrogen peroxidePhotochemistryEnvironmental chemistryOrganic chemistryTolueneMaterials scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

Ultraviolet (UV) photolysis was evaluated as a technique to convert recalcitrant aromatic volatile organic compounds (VOCs) into more biodegradable compounds. o-Xylene was investigated as the model compound due to its low biodegradability and low water solubility. o-Xylene contaminated gaseous streams with inlet loadings of up to 2700 g·m –3 ·h –1 were passed through an annular photoreactor equipped with a UV source emitting light at 254 nm and 185 nm wavelengths. Ultraviolet photolysis effectively degraded o-xylene at a maximum removal rate of about 700 g·m –3 ·h –1 , with the principle oxidizing species being hydroxyl radical. Of the total o-xylene removed, measured as total organic carbon (TOC) or chemical oxygen demand (COD), between 30% and 50% was converted to water-soluble and more biodegradable intermediates. The biodegradability of the photolysis intermediates was comparable to that of methyl ethyl ketone (MEK), which is 2–10 times more biodegradable than o-xylene. These results show that the use of UV photolysis is a promising and effective pretreatment technique for enhancing the biodegradability of recalcitrant organic compounds such as aromatics. Key words: VOC, o-xylene, photolysis, biodegradation, air treatment, biofiltration, ozone, hydrogen peroxide.

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 score0.090
Threshold uncertainty score0.175

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 head0.008
GPT teacher head0.211
Teacher spread0.203 · 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

Citations10
Published2005
Admission routes2
Has abstractyes

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