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Record W4310117622 · doi:10.1557/s43578-022-00797-7

Influence of ionic liquid (IL) treatment conditions in the regeneration of cellulose with different crystallinity

2022· article· en· W4310117622 on OpenAlexafffund
Md Mohosin Rana, Hector De la Hoz Siegler

Bibliographic record

VenueJournal of materials research/Pratt's guide to venture capital sources · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Calgary
FundersCanada First Research Excellence Fund
KeywordsCrystallinityMaterials scienceMicrocrystalline celluloseIonic liquidCelluloseAmorphous solidChemical engineeringThermal stabilityNuclear chemistryOrganic chemistryComposite materialChemistry

Abstract

fetched live from OpenAlex

Abstract In this study, we treated microcrystalline cellulose (MCC) with the ionic liquid (IL) 1-butyl-3-methylimmidazolium chloride (BMIMCl) to elucidate the effects that IL treatment conditions have on the properties of amorphized cellulose (AC). Analysis of X-ray diffractograms confirmed that after 20 min of IL treatment AC crystallinity was reduced from 77.7 to 57.1%. After 10 h of IL treatment, AC crystallinity decreased to 29.6%. Chemical and morphological changes in the regenerated AC were determined via FT-IR and SEM studies. The rapid transformation of crystalline cellulose domains into amorphous domains is linked to the small MCC particle size (20 μm) and the presence of a moisture-free condition during IL treatment. Thermal stability of the treated cellulose, as determined by TGA and DSC profiling, decreased as the crystallinity index decreased. The high tunability of cellulose crystallinity by IL treatment provides a facile way for designing more efficient hybrid bioactive materials for biomedical applications. Graphical abstract

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)0.004
metaresearch head score (Gemma)0.001
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 score0.052
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.335
Teacher spread0.306 · 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

Citations38
Published2022
Admission routes2
Has abstractyes

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