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Record W2963553063 · doi:10.5539/ijc.v11n2p86

Contamination Control in a Portable-Materials With Photochemical Process

2019· article· en· W2963553063 on OpenAlexvenueno aff
Bruno Pereira de Oliveira, Kate Cristina Blanco, Vanderlei Salvador Bagnato

Bibliographic record

VenueInternational Journal of Chemistry · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLegionella and Acanthamoeba research
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsChemistryContaminationContamination controlMicroorganismOzoneProcess (computing)Food spoilageEnvironmental chemistryProcess engineeringComputer scienceBacteriaOrganic chemistry

Abstract

fetched live from OpenAlex

Contamination control area refers to the control / eliminated of the activities of microorganisms present in the materials or process. This technique to eliminated is major importance in this study are those that cause food spoilage and are infectious. The principal areas of interest in this study are health, aerospace and food industry because the materials utilized in the process charging the contamination; thus, to control these microorganisms, moist heat and chemicals are used. However, technological development has led to a problem with some portable-materials, as these techniques are not suitable in specific cases in which there may be material damage and health safety problems. To address these problems, the present research identified a new constructive experimental device that has a triple deck (UV, Ozone, and UV-Ozone). This configuration can sterilize material with photochemical process cannot be damaged. After this protocol was generated, flat plate materials were contaminated with E. coli and 3 different configuration protocols were applied. The results show a microbial reduction of approximately 99.999% after an exposure of 30 – 40 min with ozone/UV, 30 min with UV-only, and 20 min with ozone. This device has the ability to sterilize the materials.

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 score0.020
Threshold uncertainty score0.267

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 head0.264
Teacher spread0.261 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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