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Flame Retardant and Mechanical Properties of Modified Paper Using Inorganic Salts for Packaging Applications

2015· article· en· W2012723960 on OpenAlexvenueno aff
Ahmed M. Youssef, Ola A. Mohamed, Magda A. El-Samahy, Amal H. Abdel Kader, Luhac Pichtec

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2015
Typearticle
Languageen
FieldMaterials Science
TopicFlame retardant materials and properties
Canadian institutionsnot available
Fundersnot available
KeywordsFire retardantMaterials scienceThermogravimetric analysisScanning electron microscopeChemical engineeringLithium hydroxideAbsorption of waterHydroxideInfrared spectroscopyComposite materialUltimate tensile strengthMagnesiumOrganic chemistryIon exchangeMetallurgyChemistryIon

Abstract

fetched live from OpenAlex

The paper sheet prepared from unbleached rice straw containing different concentration of inorganic salts, that work as flame retardant additives (Lithium hydroxide one hydrate (LiOH.1H2O), Magnesium hydroxide (Mg (OH)2. 15H2O), aluminum sulphate (Al2(SO4)3) and calcium carbonate (CaCO3)) were investigated using horizontal burning test against flame retardant. Also, the prepared paper sheets were characterized using scanning electron microscope (SEM), thermal gravimetric analysis (TGA) and Infrared spectroscopy (IR). Furthermore, the water absorption was determined. In addition, the mechanical properties (breaking length, tear factor and burst factor) of the prepared paper sheet were evaluated. Correspondingly, the physical (structure and mechanical) properties of the prepared paper sheet were enhanced in terms of tensile, tear factor and water absorption. Also, delaying firing process will help in protecting the materials from burning, saving many of economic effect. So the prepared paper sheet can be used for different industrial applications especially in packaging applications.

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 score0.003

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 head0.146
GPT teacher head0.368
Teacher spread0.223 · 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".

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Citations1
Published2015
Admission routes1
Has abstractyes

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