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Record W4214601119 · doi:10.1038/s41529-022-00224-6

Chemical and molecular structure transformations in atomistic conformation of cellulose nanofibers under thermal environment

2022· article· en· W4214601119 on OpenAlexafffund
Viktoriya Pakharenko, Otávio Augusto Titton Dias, Sankha Mukherjee, Samir K. Konar, Chandra Veer Singh, Kristiina Oksman, Mohini Sain

Bibliographic record

Venuenpj Materials Degradation · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsNanocelluloseCelluloseDihedral angleChemical structureChemical transformationNanofiberMaterials scienceChemical shiftThermalChemical engineeringChemistryCrystallographyMoleculeNanotechnologyOrganic chemistryPhysical chemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract The structural changes of the glucopyranose chain and the chemical compositional response of cellulose nanofibers (CNFs) under thermal exposure (at 190 °C for 5 h) have remained a significant gap in the understanding of the long-term performance of nanocellulose. Herein, CNF films with different chemical compositions were investigated to confirm the structural transformation of glucopyranose (coupling constant of OH groups changed up to 50%) by nuclear magnetic resonance (NMR) analysis. Remarkably, the glucopyranose rings underwent partial dehydration during the thermal exposure resulting in enol formation. This study confirms the chain mobility that could lead to the conformational and dimensional changes of the CNFs during thermal exposure. The broad range of conformations was defined by the dihedral angles that varied from ±27° to ±139° after thermal exposure. Investigation into the mechanism involving chemical transformation of the substrates during heating is important for the fabrication of the next generation of flexible electrical 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.002
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

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