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Record W4309724025 · doi:10.1016/j.carbpol.2022.120355

Cellulose gelation in NaOH(aq) by CO2 absorption: Effects of holding time and concentration on biomaterial development

2022· article· en· W4309724025 on OpenAlexafffund
Guillermo Reyes, Rubina Ajdary, Esko Kankuri, Joice Jaqueline Kaschuk, Harri Kosonen, Orlando J. Rojas

Bibliographic record

VenueCarbohydrate Polymers · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersCanada Excellence Research Chairs, Government of CanadaUniversiti Putra MalaysiaAalto-YliopistoAcademy of Finland
KeywordsUltimate tensile strengthCelluloseChemical engineeringExtrusionMaterials scienceSodium hydroxideAbsorption of waterAbsorption (acoustics)PolymerAqueous solutionSolubilityComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

We address the limited solubility and early onset of gelation of aqueous sodium hydroxide to position it as a preferred green solvent for cellulose. For this purpose, we expand the concentration window (up to 12 wt%) by using a CO2-depleted air and adjusting the time the dope remains in the given atmosphere, before further processing (holding time) and regeneration conditions. Cellulose solutions are extruded following characteristic (rheology and extrusion) parameters to yield aligned filaments reaching tenacities up to 2.3 cN·dtex−1, similar to that of viscose. Further material demonstrations are achieved by direct ink writing of auxetic biomedical meshes (Poisson's ratio of −0.2, tensile strength of 115 kPa) and transparent films, which achieved a tensile strength and toughness of 47 MPa and 590 kJ·m−3, respectively. The results suggest an excellent outlook for cellulose transformation into bioproducts. Key to this development is the control of the gelation ensuing solution flow and polymer alignment, which depend on CO2 absorption, cellulose concentration, and holding time.

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 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 score0.628

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)0.0000.000

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 head0.238
Teacher spread0.229 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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