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CELLULOSE SHEETS MADE FROM MICRO/NANOFIBRILLATED FIBERS OF BAMBOO, JUTE AND EUCALYPTUS CELLULOSE PULPS

2019· article· en· W2974448839 on OpenAlexaff
Gustavo Henrique Denzin Tonoli, Vânia Aparecida de Sá, Mário Guimarães, Alessandra de Souza Fonseca, Gregory M. Glenn, Jordão Cabral Moulin, Suhara Panthapulakkal, MOHINI SAIN, Delilah F. Wood, Tina Williams, Lennard Torres, William J. Orts

Bibliographic record

VenueCellulose Chemistry and Technology · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCelluloseBambooCrystallinityComposite materialMaterials scienceCellulose fiberPorosityLigninKraft paperEucalyptusChemistryFiberOrganic chemistryBotany

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the properties of cellulose sheets made from fibrillated micro/nanofibrils of bamboo, jute and eucalyptus. Micro/nanofibrils obtained from alkaline-treated jute had higher crystalline fraction (CF) and crystallinity index (CI). The onset degradation temperature (T onset ) was very similar for all the micro/nanofibrils (from 316 o C to 323 o C). Defibrillated cellulose nanofibrils had an average diameter in the range of 30-50 nm. Cellulose sheets made of bamboo and jute micro/nanofibrils had higher bending strength, elastic modulus and energy to break than the sheets made of eucalyptus micro/nanofibrils. Compared to the bamboo and jute samples, the eucalyptus micro/nanofibril suspension had a greater amount of fiber debris, which may have contributed to higher apparent porosity, water adsorption and water solubility of the cellulose sheets. Further development of the mechanical defibrillation process could lower the production costs of micro/nanofibrils and improve the performance of engineered cellulose-based 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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.226
Teacher spread0.219 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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