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Modification and Characterization of Biodegradable Chitosan/ Starch-Based Films with Monomer 1,4-Butanediol Diacrylate (BDDA) by Gamma Radiation

2013· article· en· W2113717522 on OpenAlexvenueno aff
Nousin Akter, Farah Hossain, Fahad B. Quader, Poonam Alamgir

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2013
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceChitosanUltimate tensile strengthStarchFourier transform infrared spectroscopyScanning electron microscopeMonomerChemical engineeringComposite materialCastingPolymer chemistryNuclear chemistryPolymerOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Chitosan reinforced starch-based biodegradable films were prepared by solution casting. Tensile strength (TS), tensile modulus (TM), elongation at break (%), and water vapor permeability (WVP) of the 50% chitosan containing starch-based films were found to be 47 MPa, 550 MPa, 16%, and 2.45 g·mm/m2·day·kPa, respectively. Monomer 1,4 butanediol diacrylate (BDDA) was added (0.25-1% by wt) to the starch/chitosan (50:50) based film formulation. Then, films were cast and gamma irradiated from a radiation dose varied from 1 kGy to 25 kGy. Then mechanical and barrier properties were evaluated. The highest TS (80 MPa) and TM (880 MPa) of the films were found by using 0.5% monomer at 5 kGy dose. The WVP of the films were found to be 1.50 g·mm/m2·day·kPa which is 38.77% lower than control starch/chitosan-based films. Molecular interactions due to incorporation of BDDA were supported by Fourier transform infra red (FTIR) spectroscopy. The water uptake of the films pointed out better hydrophobic character due to incorporation of BDDA in starch/chitosan-based films. Surface morphologies of BDDA treated films were examined by scanning electron microscope (SEM) and suggested better morphologies due to BDDA treatment with starch/chitosan-based biodegradable films.

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)0.000
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 score0.015
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.017
GPT teacher head0.293
Teacher spread0.276 · 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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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