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Record W4407681923 · doi:10.1016/j.carbpol.2025.123414

Influence of chitin purity on its dissolution behaviour in alkaline solvent

2025· article· en· W4407681923 on OpenAlexafffund
Julia Pohling, Kelly Hawboldt, Deepika Dave

Bibliographic record

VenueCarbohydrate Polymers · 2025
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsMemorial University of Newfoundland
FundersNewfoundland and Labrador
KeywordsDissolutionChitinSolventChemistryChemical engineeringNuclear chemistryOrganic chemistryChitosan

Abstract

fetched live from OpenAlex

The biopolymer chitin is a promising ingredient for bioplastics due to its strength and stability. Chitin fibres must be isolated from biomass by stripping away tightly bound minerals, proteins, and pigments. Depending on the desired chitin purity, this isolation process can be long and cost-prohibitive on industrial scale. Following isolation, chitin fibres must be dissolved or separated which is challenging due to chitins recalcitrance. Cryo-assisted dissolution in potassium hydroxide has been identified as a fast and non-toxic approach. However, to date, this method has not received widespread attention and has not been evaluated for chitins from different species. The present study demonstrated the dissolution of Northern pink shrimp (P. borealis) chitin using KOH solvent and the influence of residual impurities on dissolution behaviour was investigated for the first time. Three chitin samples with purities ranging from 84 to 97 % w/w were compared, and results confirmed that chitin with lower purity can be successfully dissolved in KOH solvent, provided that minerals were removed prior to dissolution. However, dissolution behaviour and physical properties were strikingly different between the samples. Microscopic analysis of chitin particles allowed the differentiation between exoskeleton features and the identification of insoluble particles, providing potential new avenues to improve dissolution processes.

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)0.000
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 score0.029
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

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

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 head0.267
Teacher spread0.255 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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