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Record W3134333130 · doi:10.3934/matersci.2021008

Poly(vinyl alcohol) nanocomposites: Recent contributions to engineering and medicine

2021· article· en· W3134333130 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAIMS Materials Science · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposite Synthesis and Irradiation
Canadian institutionsConcordia University
Fundersnot available
KeywordsVinyl alcoholNanocompositeMaterials scienceGraphenePolymerVinyl esterVinyl polymerMontmorilloniteOxideChemical engineeringPolymer chemistryComposite materialNanotechnologyCopolymer

Abstract

fetched live from OpenAlex

<abstract> Poly(vinyl alcohol) is a water soluble hydrophilic synthetic polymer. As a matrix it can be mixed with fillers having nano dimensions to form polymer nanocomposites with interesting properties, different than those of PVA. From the chemical point of view nanofillers can be elements such as Au, Ag, Se, oxides such as CuO, SrO, TiO<sub>2</sub>, graphene oxide, minerals like hallosite, montmorillonite, tubes such as carbon nanotubes, hallosite nano tubes, etc. As presented in this review, some of the nanofillers are able to change and improve poly(vinyl alcohol)'s mechanical, thermal, and electrical properties, and can find uses in medicine. In the latter, due to the fact that poly(vinyl alcohol) is biocompatible, biodegradable, and bioabsorbant, it is possible to produce poly(vinyl alcohol) nanocomposites to be used like antibacterial, tissue engineering, drug carrier, wound dressing, etc. The present article retains the most recent (2019–2020) studies done on poly(vinyl alcohol) nanocomposites structure and contributions to engineering and medicine. </abstract>

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.276
Teacher spread0.263 · 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