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Record W2053868532 · doi:10.1002/jctb.1371

Coupling ultraviolet photolysis and biofiltration for enhanced degradation of aromatic air pollutants

2005· article· en· W2053868532 on OpenAlexafffund
Madjid Mohseni, Joy Lan Zhao

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2005
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiofilterChemistryPhotodissociationDegradation (telecommunications)PollutantUltravioletEnvironmental chemistryXyleneWater treatmentToluenePhotochemistryOrganic chemistryEnvironmental engineeringMaterials scienceEnvironmental science

Abstract

fetched live from OpenAlex

Abstract Coupling UV photolysis and biofiltration was evaluated as an effective treatment strategy for the enhanced degradation of hardly biodegradable aromatic volatile organic compounds (VOCs). o ‐Xylene, a recalcitrant and poorly water‐soluble VOC, was used as a model compound and treated in two parallel treatment systems with and without UV pretreatment. Contaminated streams with flow rates of 0.186–0.384 m 3 h −1 and inlet o ‐xylene concentrations of up to 0.22 g m −3 were passed through the treatment system. About 20% (between 10 and 35%) of o ‐xylene was converted into water‐soluble intermediates during the UV photolysis stage, which partially oxidized o ‐xylene to more water‐soluble and biodegradable byproducts. The untreated contaminant along with the byproducts of UV photolysis was then removed effectively in the biofiltration stage, with improvements of up to 100% compared with the control biofiltration process. The results suggested that combined UV photolysis–biofiltration is promising as an effective technique to eliminate hydrophobic and recalcitrant organic compounds from contaminated air steams. Copyright © 2005 Society of Chemical Industry

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
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 score0.076
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.245
Teacher spread0.236 · 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.

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

Citations34
Published2005
Admission routes2
Has abstractyes

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