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Record W4285304316 · doi:10.1039/d2gc01141g

Highly regioselective surface acetylation of cellulose and shaped cellulose constructs in the gas-phase

2022· article· en· W4285304316 on OpenAlexaff
Tetyana V. Koso, Marco Beaumont, Blaise L. Tardy, Daniel Rico del Cerro, Samuel Eyley, Wim Thielemans, Orlando J. Rojas, Ilkka Kilpeläinen, Alistair W. T. King

Bibliographic record

VenueGreen Chemistry · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersH2020 European Research CouncilInterreg Vlaanderen-NederlandChina Scholarship CouncilKU LeuvenAcademy of FinlandProvincie West-VlaanderenUniversity of MelbourneAalto-YliopistoInterregVlaamse regeringEuropean Commission
KeywordsNanocelluloseCelluloseRegioselectivityAerogelCellulosic ethanolAcylationChemistryChemical engineeringPhase (matter)AcetylationKineticsOrganic chemistryMaterials scienceCatalysisNanotechnology

Abstract

fetched live from OpenAlex

, which cellulose hydroxyls are preferentially acylated and if acylation can be restricted to the surface, preserving crystallinities/morphologies. Consequently, we reexplore simple gas-phase acetylation of modern-day cellulosic building blocks - cellulose nanocrystals, pulps, dry-jet wet spun (regenerated cellulose) fibres and a nanocellulose-based aerogel. Using advanced analytics, we show that the gas-phase acetylation is highly regioselective for the C6-OH, a finding also supported by DFT-based transition-state modelling on a crystalloid surface. This contrasts with acid- and base-catalysed liquid-phase acetylation methods, highlighting that gas-phase chemistry is much more controllable, yet with similar kinetics, to the uncatalyzed liquid-phase reactions. Furthermore, this method preserves both the native (or regenerated) crystalline structure of the cellulose and the supramolecular morphology of even delicate cellulosic constructs (nanocellulose aerogel exhibiting chiral cholesteric liquid crystalline phases). Due to the soft nature of this chemistry and an ability to finely control the kinetics, yielding highly regioselective low degree of substitution products, we are convinced this method will facilitate the rapid adoption of precisely tailored and biodegradable cellulosic materials.

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 score0.003
Threshold uncertainty score0.574

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 studies0.0000.000
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 head0.020
GPT teacher head0.279
Teacher spread0.260 · 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

Citations31
Published2022
Admission routes1
Has abstractyes

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