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Record W4389380231 · doi:10.17725/rensit.2023.15.411

A nature-like anti-infective impregnation of a medical mask and a method for its application

2023· article· en· W4389380231 on OpenAlexaff
S. N. Gaydamaka, М. А. Гладченко, А.А. Корнилова, Igor' V. Kornilov

Bibliographic record

VenueRadioelectronics Nanosystems Information Technologies · 2023
Typearticle
Languageen
FieldMedicine
TopicMedical and Biological Ozone Research
Canadian institutionsHyperion Technologies (Canada)
Fundersnot available
KeywordsMaterials scienceCarbon fibersCarbon dioxideIonChemical engineeringNanotechnologyComposite materialChemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The potential for creating anti-infective impregnation based on aminopolysaccharide and silver ions is shown. Thanks to a unique water-insoluble complex of silver clusters (Ag 0.034 mg/cm2 of mask material) in natural aminopolysaccharide, the impregnated material exhibits significant antibacterial properties. It has been shown that impregnation does not interfere with the effective removal of carbon dioxide, which can accumulate on the inside of the mask during breathing. It is noted that the use of a new nature-like anti-infective impregnation opens up the possibility of increasing the wear resistance of the impregnated material, while the impregnation is not removed from the surface of the mask material during intensive use of the mask.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.338
Teacher spread0.327 · 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 designOther design
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

Citations0
Published2023
Admission routes1
Has abstractyes

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