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Record W4249751275 · doi:10.26434/chemrxiv.8081156.v1

Mechanoenzymatic Breakdown of Chitinous Material to N-Acetylglucosamine: The Benefits of a Solvent-Free Environment

2019· preprint· en· W4249751275 on OpenAlexaff
J. P. Daniel Therien, Fabien Hammerer, Tomislav Friščić, Karine Auclair

Bibliographic record

VenueChemRxiv · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicStudies on Chitinases and Chitosanases
Canadian institutionsMcGill University
Fundersnot available
KeywordsChitinChitinaseSolventHydrolysisChemistryAqueous solutionBall millMonomerCatalysisChemical engineeringN-AcetylglucosamineAcetic acidOrganic chemistryBiomass (ecology)Pulp and paper industryEnzymeBiologyEngineeringEcologyPolymer

Abstract

fetched live from OpenAlex

We report an innovative and efficient mechanoenzymatic method to hydrolyze chitin to N-acetylglucosamine monomer using chitinase under the recently developed RAging methodology, i.e. a combination of mechanochemical ball milling activation and accelerated aging repeated over many cycles, in the absence of bulk solvent. Our result show that the activity of chitinases increases several times by switching from traditional solvent-based conditions of enzymatic catalysis to solvent-free RAging, which operates on moist solid substrates. Importantly, RAging is also highly efficient for the production of N-actylglucosamine directly from shrimp and crab shell biomass without any other processing, except for a gentle wash with aqueous acetic acid.

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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.003
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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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