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Record W4408168002 · doi:10.1021/acs.nanolett.5c00064

Cold Alkali Treatment Enabled Stretchable yet Mechanically Strong All-Cellulose Composite

2025· article· en· W4408168002 on OpenAlexafffund
Penghui Zhu, Andrea Vo, Xia Sun, Yeling Zhu, Hao Sun, Pu Yang, Zhengyang Yu, Jiaying Zhu, Feng Jiang

Bibliographic record

VenueNano Letters · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersCanada Research ChairsCanada Foundation for Innovation
KeywordsCelluloseAlkali metalComposite numberMaterials scienceComposite materialNanotechnologyChemical engineeringPolymer scienceChemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Biodegradable cellulose films are promising alternatives to plastics, but achieving stretchable all-cellulose composites (ACCs) remains challenging. Here, we present a scalable strategy for creating stretchable yet mechanically strong ACCs. This approach integrates swollen-regenerated microfibers with dissolution-regenerated cellulose to form multiscale architectures, achieved through mechanical pretreatments and cold NaOH treatment of kraft pulp, followed by vacuum filtration and press-drying. Swollen-regenerated microfibers establish preferential sacrificial networks that enhance mechanical strength through nanofiber pull-out, while dissolution-regenerated cellulose matrix facilitates nanoscale load transfer, maintaining ductility. The ACC achieves a tensile strength of 89.0 MPa, a strain to failure of 24.7%, and a work of fracture of 17.3 MJ m –3 ─1.3 times stronger, 1.5 times more stretchable, and 3.8 times tougher than microfibrillated cellulose films. With added benefits of wet strength, grease resistance, oxygen barrier property, and biodegradability, this work demonstrates a scalable approach to engineering multiscale cellulose networks for sustainable packaging.

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)
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 score0.027
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)0.0000.000

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 head0.021
GPT teacher head0.292
Teacher spread0.271 · 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

Citations7
Published2025
Admission routes2
Has abstractyes

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