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Record W3183753114 · doi:10.1002/cjce.24272

Prevention of pathogen microorganisms at indoor air ventilation system using synthesized copper nanoparticles

2021· article· en· W3183753114 on OpenAlexvenueno aff
Karine Machry, Clóvis Wesley Oliveira de Souza, Mônica Lopes Aguiar, André Bernardo

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsnot available
Fundersnot available
KeywordsMicroorganismAdhesionNanoparticlePathogenCopperFilter (signal processing)Filtration (mathematics)ChemistryMaterials scienceBacteriaMicrobiologyChemical engineeringNanotechnologyComposite materialBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract This article describes the impregnation of copper nanoparticles (CuNP) in a polyester fibre filter that can be used in solid–gas filtration to retain the spread of pathogen microorganisms in indoor environments. The impregnation of the CuNP was achieved by spraying the suspension on the surface of filter media. An acid pretreatment was also evaluated to increase the adhesion between fibre and nanoparticle. The synthesis of the CuNP was done by chemical reduction. The bacterial effect was measured through the contact method for Escherichia coli and Staphylococcus aureus , and we demonstrate that the presence of CuNP to filter media reduced up to 99.99% of gram‐negative and 99.98% of gram‐positive bacteria. The pretreatment with HCl was a good alternative to filter modification due to the higher adhesion between CuNP and the fibre while the high efficiency against pathogen microorganisms was kept. The modification of filters with CuNP can improve the air quality of indoor environments, vanishing the pathogen microorganisms circulating in the air.

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 score0.011
Threshold uncertainty score0.214

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 head0.012
GPT teacher head0.212
Teacher spread0.200 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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