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Record W2093807804 · doi:10.1021/ie701739g

Photocatalytic Inactivation of Airborne Bacteria in a Continuous-Flow Reactor

2008· article· en· W2093807804 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2008
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsWestern University
FundersNational University of Singapore
KeywordsPhotocatalysisAerosolizationBatch reactorChemistryLight intensityBioaerosolEscherichia coliIntensity (physics)Relative humidityVolumetric flow rateContinuous reactorContinuous stirred-tank reactorResidence time (fluid dynamics)Nuclear chemistryAerosolCatalysisOrganic chemistryBiochemistryBiology

Abstract

fetched live from OpenAlex

In this study, a continuous annular reactor was used to characterize the TiO 2 -mediated inactivation of an aerosolized Gram-negative bacterium, Escherichia coli K-12 (ATCC 10798), by varying UV-A intensity (0.5−3.4 mW/cm 2 ), relative humidity (RH) (from 51 ± 0.61 to 85 ± 4.7%), and photocatalyst loading (960 and 1516 mg/m 2 ) at an air flow rate of 1 L/min. Inactivation rate of E. coli K-12 increased with an increase in TiO 2 loading, UV-intensity, and RH. A UV-A dose of 0.03−0.204 J/cm 2 at an average UV-A intensity of 0.5−3.4 mW/cm 2, at a residence time of 1.1 min, is sufficient to fully and continuously inactivate E. coli K-12 passing through the reactor. The photocatalytic inactivation rates obtained in the continuous flow reactor compared well with our earlier batch inactivation rates conducted at a UV-A intensity of 0.015 mW/cm 2 and a TiO 2 loading of 1516 mg/m 2 . This demonstrates the possibility of scaling up of the photocatalytic inactivation process for bioaerosol based on batch kinetic data.

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.

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)0.001
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 score0.009
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.072
GPT teacher head0.289
Teacher spread0.217 · 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