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Record W3186526135 · doi:10.21203/rs.3.rs-689489/v1

Single Fibre Swelling Behavior for Natural and Man-Made Cellulose Fibres Under Industrial Steeping Conditions

2021· preprint· en· W3186526135 on OpenAlexaff
Xiang You, Feng Chen, Yibo Ma, Annariikka Roselli, Eric Enqvist, Heikki Hassi

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsSciencetech (Canada)
FundersJenny ja Antti Wihurin Rahasto
KeywordsSteepingSwellingCelluloseNatural (archaeology)Cellulose fiberMaterials sciencePolymer scienceComposite materialPulp and paper industryBusinessChemistryFood scienceFiberEngineeringBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Swelling behavior of cotton, dissolving wood pulp (DWP), viscose staple fibre (VsF), and Tencel staple fibre (TsF) in varying sodium hydroxide (NaOH) were investigated by means of optical microscopy and were characterized by molecular mass distribution, X-ray diffractometer, and dynamic vapor sorption. Effect of temperature (20-45 °C) and duration (0-120 min) was studied. The results reveal that the swelling ratio of fibre in alkali solution depends on fibre accessibility and NaOH concentration. Among all the materials, VsF exhibited the highest swelling ratio and lowest swelling ratio has been observed for cotton fibre. The results suggest that the swelling is limited by the presence of plant cell wall structures in cotton and DWP, rather from fringed-fibrillar, semi-crystalline sub-structures, which result from the inherent tendency of cellulose molecules to form such structures during the biosynthesis of plant cell walls as well as during the formation of regenerated cellulosic textile fibre in wet-spinning.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 head0.155
GPT teacher head0.415
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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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