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Record W4384525883 · doi:10.3390/molecules28145446

The Enhanced Durability of AgCu Nanoparticle Coatings for Antibacterial Nonwoven Air Conditioner Filters

2023· article· en· W4384525883 on OpenAlexaff
Fang Zhou, Jiabing Peng, Yujie Tao, Longlai Yang, De‐Quan Yang, E. Sacher

Bibliographic record

VenueMolecules · 2023
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsPolytechnique MontréalRegroupement Québécois sur les Matériaux de Pointe
Fundersnot available
KeywordsDurabilityMaterials scienceX-ray photoelectron spectroscopyNanoparticleAntibacterial activityComposite materialChemical engineeringNuclear chemistryNanotechnologyChemistryBacteria

Abstract

fetched live from OpenAlex

Antibacterial nonwoven fabrics, incorporated with Ag, have been applied as masks and air conditioner filters to prevent the spread of disease from airborne respiratory pathogens. In this work, we present a comparison study of Ag ions: Ag and AgCu nanoparticles (NPs) coated onto nonwoven fabrics intended for use as air conditioner antibacterial filters. We illustrate their color changes and durability running in air conditioners using antibacterial activity testing and X-ray Photoelectron Spectroscopic (XPS) analysis. We found that AgCu NPs showed the best antibacterial efficacy and durability. XPS analysis indicated that the Ag concentration, on both the AgCu and Ag- NP-coated fibers, changed little. On the contrary, the Ag concentration on Ag ion-coated fibers decreased by ~30%, and the coated NPs aggregated over time. The color change in AgCu NP-coated fabric, from yellow to white, is caused by oxide shell formation over the NPs, with nearly 46% oxidized silver. Our results, both from antibacterial evaluation and wind blowing tests, indicate that AgCu NP-coated fibers have higher durability, while Ag ion-coated fibers have little durability in such applications. The enhanced durability of the AgCu NP-coated antibacterial fabrics can be attributed to stronger NP-fiber interactions and greater ion release.

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 score0.158
Threshold uncertainty score0.147

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 head0.013
GPT teacher head0.279
Teacher spread0.266 · 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
Published2023
Admission routes1
Has abstractyes

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