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Record W4372228062 · doi:10.1021/acssuschemeng.3c00367

Controlled Chitosan Molecular Weight Reduction by Mechanochemical and Aging-Based Phosphoric Acid Hydrolysis

2023· article· en· W4372228062 on OpenAlexafffund
Galen Yang, Edmond Lam, Audrey Moores

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2023
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsNational Research Council CanadaMcGill UniversityCentre in Green Chemistry and Catalysis
FundersFonds de recherche du Québec – Nature et technologiesNational Research Council CanadaCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsChitosanDispersityChitinPhosphoric acidGel permeation chromatographyBiopolymerChemistryHydrolysisChemical engineeringNuclear chemistryMaterials sciencePolymer chemistryOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Chitosan is typically produced by the deacetylation of the chitin biopolymer. Its molecular weight (Mw) is a function of the chitin source and the deacetylation conditions. It remains a challenge to both control the Mw of chitosan and obtain good monodispersity, which are essential for optimal function of chitosan in different applications. This study explores the use of mechanochemistry and aging as effective methods toward the controlled reduction of the Mw of commercial high Mw chitosan (HMwChs) with phosphoric acid, where Mw was measured by gel permeation chromatography (GPC). In comparison to conventional solution-based methods, this new mechanochemical method features improved process mass intensity in comparison to conventional techniques and affords low Mw chitosan (LMwChs) samples in a range of 200 to 19.5 kDa, with a polydispersity down to1.8.

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 categoriesMeta-epidemiology (narrow)
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 score0.006
Threshold uncertainty score1.000

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 head0.002
GPT teacher head0.181
Teacher spread0.179 · 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.

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

Citations23
Published2023
Admission routes2
Has abstractyes

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