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Record W130397936 · doi:10.1021/bk-2006-0938.ch012

Cellulose Microfibers as Reinforcing Agents for Structural Materials

2006· book-chapter· en· W130397936 on OpenAlexaff
Ayan Chakraborty, M. Sain, Mark T. Kortschot

Bibliographic record

VenueACS symposium series · 2006
Typebook-chapter
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCelluloseMicrofiberMaterials scienceComposite materialMicrofibrilPulp (tooth)NanocelluloseCellulose fiberWhiskersKraft processCell wallRegenerated cellulosePolyvinyl alcoholPolymer scienceFiberKraft paperChemical engineeringChemistry

Abstract

fetched live from OpenAlex

Since the use cellulose whiskers, cellulose microfibrils, and cellulose fibres of micro- and nano-scale diameters as reinforcing agents in composites is increasing rapidly, there is a need to review the microstructure of cellulose in great detail. In this paper, the concept of "microfibres" of cellulose has been developed based on a consideration of bleached kraft pulp cell wall morphology. First, the structure of a cellulose microfibril in wood cell wall has been elucidated through a structural analysis of wood cell wall. Subsequently, the need to define microfibres as "fibres" for structural applications has been demonstrated, and contrasted with the convential definition of microfibrils, which are attached to the cell wall at one end. It is proposed that microfibres should have a minimum aspect ratio of 50 for adequate stress transfer from the matrix to the fibre. Consequently, microfibres were defined as cellulose strands 0.1-1 μm in diameter, with a corresponding minimum length of 5-50 μm. The reinforcing potential of cellulose microfibres was demonstrated by using them in composites with a polyvinyl alcohol (PVA) matrix.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.004

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 head0.017
GPT teacher head0.269
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2006
Admission routes1
Has abstractyes

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