MétaCan
Menu
Back to cohort
Record W4390726944 · doi:10.26434/chemrxiv-2024-xr4dv

Mechanochemical and Aging-Based Reductive Amination with Chitosan and Aldehydes Affords High Degree of Substitution Functional Biopolymers

2024· preprint· en· W4390726944 on OpenAlexafffund
Galen Yang, Sophie Régnier, Noah Huin, Tracy Liu, Edmond Lam, Audrey Moores

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldChemistry
TopicAntimicrobial agents and applications
Canadian institutionsNational Research Council CanadaMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNational Research Council CanadaCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsReductive aminationChitosanSurface modificationAminationAldehydePolymerAmine gas treatingCombinatorial chemistryChemistryMaterials scienceAlkylationOrganic chemistryChemical engineeringCatalysis

Abstract

fetched live from OpenAlex

Chitosan is readily available from various biomass waste streams including crustaceans, cephalopods, insects, and fungus. The polymer possesses primary amine groups which are great handles for functionalization. Yet efficient functionalization with high degree of substitution is challenging to achieve via solvothermal methods due to limitations in chitosan solvation properties. Herein we report a mechanochemical and aging-based method directly addressing this point. Working in the solid-phase helps stabilize the formation of Schiff bases from chitosan and aldehydes, affording a novel pathway to the green functionalization of chitosan by reductive alkylation, with unprecedentedly high degrees of substitution. The method showed great efficacy and compatibility for chitosan to be functionalized with 21 different aldehyde substrates and a low process mass intensity (PMI) of 36. This work also opens a new avenue for the development of mechanochemical and aging-based reductive amination transformations.

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 score0.004
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

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

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 head0.229
Teacher spread0.212 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueChemRxivSame topicAntimicrobial agents and applicationsFrench-language works237,207