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Record W2549262603 · doi:10.1177/1082013216679010

Actinometric and biodosimetric evaluation of UV-C dose delivery in annular, Taylor–Coutte and coiled tube continuous systems

2016· article· en· W2549262603 on OpenAlexaff
Alexandra Müller, Marta Orłowska, Michael Knörr, Mario Stahl, Ralf Greiner, Tatiana Koutchma

Bibliographic record

VenueFood Science and Technology International · 2016
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsLaminar flowTaylor–Couette flowChemistryTube (container)Reynolds numberMechanicsAnalytical Chemistry (journal)Continuous reactorMaterials scienceTurbulenceOpticsChromatographyPhysicsComposite materialBiochemistry

Abstract

fetched live from OpenAlex

In this study, the evaluation of the performance of two thin-film UV-C reactors (annular and Taylor–Couette) and a coiled tube system is presented using actinometry and biodosimetry methods. The iodide/iodate actinometry method was found suitable for comparison of the efficiency of UV-C dose delivery of the UV-C continuous flow systems. Inactivation kinetics of Escherichia coli ATCC 8739 in quarter-strength Ringer’s solution (absorption coefficient α 254 nm ∼ 0 cm −1 ) at various flow conditions at Reynolds numbers in the range of 26 to 3000 showed a good correlation between the different reactor types. In high UV-C absorbing liquids, the inactivation efficiency increases due to the improved radial mixing. The inactivation performance of the Taylor–Couette system correlates to the annular reactor when no rotation force is applied. The residence time distributions showed the narrowest distribution with the coiled tube system at comparable flow rates. The results indicate that, despite the laminar flow conditions, the performance of the Taylor–Couette unit becomes equal to the turbulent flow conditions of the coiled tube reactor by rotation of the inner cylinder.

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)0.001
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 score0.331
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.249
Teacher spread0.232 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

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