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Record W4306377418 · doi:10.3390/coatings12101562

Crosslinked PVA/Nanoclay Hydrogel Coating for Improving Water Vapor Barrier of Cellulose-Based Packaging at High Temperature and Humidity

2022· article· en· W4306377418 on OpenAlexafffund
Feng Gu, Yang Wenjing, Junlong Song, Huining Xiao, Wangxia Wang, Zhaosheng Cai

Bibliographic record

VenueCoatings · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of New Brunswick
FundersGovernment of Jiangsu ProvinceNatural Sciences and Engineering Research Council of CanadaNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsCoatingPolyvinyl alcoholMaterials scienceContact angleWater vaporTransmission rateComposite materialCelluloseHumidityMoistureChemical engineeringThermal stabilityRelative humidityChemistryTransmission (telecommunications)Organic chemistry

Abstract

fetched live from OpenAlex

Improving the water vapor barrier of cellulose-based packaging in tropical conditions is very challenging for moisture-sensitive products. In this work, we developed a crosslinked polyvinyl alcohol/nanoclay (PVA/NC) hydrogel for paper surface coating. Layered NC and crosslinking can effectively improve the stability of PVA and block the flow paths of water molecules at elevated temperature and humidity. The result indicates that the crosslinked PVA/NC hydrogel coating (4 g/m2) can reduce the water vapor transmission rate of copy paper from 1861 g/m2/day to 195 g/m2/day at 38 °C (90% RH). The coated paper has an initial contact angle of 108° and can maintain hydrophobicity (>90°) under direct contact with water for three minutes. A Kit No. as high as 12 and a Cobb No. of 10 were also achieved by the coating.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.015
GPT teacher head0.259
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2022
Admission routes2
Has abstractyes

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