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Record W2752283290 · doi:10.21967/jbb.v2i3.83

Xylan/chitosan composites prepared by an ionic liquid system with unique antioxidant properties

2017· article· en· W2752283290 on OpenAlexvenueno aff
Hailong Gao, Na Liu, Shuzhen Ni, Hai‐Xia Lin, Yingjuan Fu

Bibliographic record

VenueJournal of Bioresources and Bioproducts · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsnot available
Fundersnot available
KeywordsChitosanGlutaraldehydeXylanComposite numberChelationThermal stabilityIonic liquidAntioxidantABTSMaterials scienceSolubilityChemical engineeringChemistryMetal ions in aqueous solutionCelluloseComposite materialMetalOrganic chemistryDPPHCatalysis

Abstract

fetched live from OpenAlex

Antioxidant function and solubility in water are highly desirable in many applications of chitosan. In this paper, a xylan/chitosan composite was prepared in an ionic liquid system using glutaraldehyde as a crosslinking agent. The antioxidant activity, reducing capacity and metal ion chelating ability of the resulting composite were determined. The chemical structure and thermal stability were analyzed by FT-IR, XRD and TGA. The results showed that chitosan was successfully cross-linked with xylan by glutaraldehyde in the ionic liquid system. Compared to pure chitosan, the ABTS&#183;<sup>+</sup> scavenging activity of the xylan/chitosan composite increased from 10.56% to 97.59%. After cross-linking with xylan by glutaraldehyde, the reducing power of xylan/chitosan composite increased from 0.054 to 2.109. The capacity of chelating metal ion of the composite also increased from 42.35% to 86.71% compared to that of pure chitosan. An underlying mechanism was proposed to account for the improvement of the chemical properties of the chitosan in the composite.

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)0.001
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 score0.004
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

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