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Record W2312589283 · doi:10.2166/wqrjc.2014.032

Quantifying ultraviolet inactivation kinetics in nearly opaque fluids

2014· article· en· W2312589283 on OpenAlexaff
Ferdinando Crapulli, Domenico Santoro, Michael Sasges, Shaunak Ray, Housyn Mahmoud, Ajay K. Ray

Bibliographic record

VenueWater Quality Research Journal · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsUniversity of WaterlooTrojan Technologies (Canada)Western University
Fundersnot available
KeywordsOpacityKineticsUltravioletTransmittanceCollimated lightChemistryOpticsMaterials scienceThermodynamicsAnalytical Chemistry (journal)ChromatographyPhysicsLaser

Abstract

fetched live from OpenAlex

The use of ultraviolet light for inactivation of pathogens is an engrained, low-cost, eco-friendly method for disinfection of nearly transparent (UVT254 > 30%/cm) contaminated fluids for which a standard-collimated beam apparatus is typically used for measurement of intrinsic inactivation kinetics. However, such a device cannot be used for low ultraviolet transmittance (UVT254 < 30%/cm) and nearly opaque (UVT254 < 10%/cm) fluids because of the lack of sufficient mixing and intrinsic inactivation kinetics controlled by dose distribution and mass-transfer effects. In this paper, a computational fluid dynamics (CFD) model was used to determine the validity regime for accurate ultraviolet inactivation kinetics studies in low transmittance and nearly opaque fluids when a new Taylor–Couette collimated beam apparatus, which exploits flow instability through the formation of toroidal counter-rotating vortices, is used for irradiations. A Taylor number of ∼ 46,500 was sufficient to overcome the very short UV light penetration at UVT254 ∼ 0.001%/cm as long as the log10 reduction value was used as controlling parameter. Specifically, it was identified that, in case of first-order inactivation kinetics, the applied average dose (AD) should not be higher than three times the dose required for one log10 inactivation (also known as D10) in order to generate data for accurate kinetic studies.

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)0.007
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
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 integrity0.0000.001
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 head0.140
GPT teacher head0.394
Teacher spread0.254 · 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 designObservational
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

Citations1
Published2014
Admission routes1
Has abstractyes

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