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Record W2133182164 · doi:10.1139/s07-044

Exposure times and energy densities for ultrasonic disinfection of <i>Escherichia coli</i>, <i>Pseudomonas aeruginosa</i>, <i>Enterococcus avium</i>, and sewage

2008· article· en· W2133182164 on OpenAlexvenueno aff
David M. Stamper, Eric Holm, Robert A. Brizzolara

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2008
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsnot available
FundersOffice of Naval Research
KeywordsSonicationVolume (thermodynamics)Ultrasonic sensorSewageUltrasoundPseudomonas aeruginosaChemistryUltrasound energyMicrobiologyMaterials sciencePulp and paper industryBacteriaBiologyChromatographyEnvironmental engineeringEnvironmental scienceMedicine

Abstract

fetched live from OpenAlex

We investigated the disinfection of sewage and several bacterial pure cultures relevant to sewage using ultrasound produced by the magnetostrictive material, TERFENOL-D. Particular attention was paid to measuring the ultrasonic exposure time and energy density required to kill bacteria. Ultrasonic power was measured and recorded during the treatments, and bacterial kill curves were constructed based on both exposure time and total energy. In 100 mL of synthetic graywater medium, bacterial pure cultures required from ~5 to 16 min per log kill at moderate intensity (~25 W·cm –2 ), while a sewage sample took several times longer to experience a 90% kill. The effects of intensity, horn size, sonication medium, and vessel geometry and volume on exposure time and energy density were explored. Intensity, horn size, and the sonication medium had significant effects on bacterial kill by ultrasound, but vessel geometry did not. The volume of the liquid being treated and the treatment time were correlated, indicating that the volume of the disinfection zone was smaller than the volume of the liquid being treated.

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 score0.220
Threshold uncertainty score0.396

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 head0.004
GPT teacher head0.173
Teacher spread0.168 · 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

Citations16
Published2008
Admission routes1
Has abstractyes

Explore more

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