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Record W4403827165 · doi:10.1021/acs.nanolett.4c04103

Recyclable Chitosan-Modified Cellulose Fiber Porous Structure for Sensitive and Robust Moisture-Driven Actuators and Automatic Cooling Textiles

2024· article· en· W4403827165 on OpenAlexafffund
Jiaying Zhu, Penghui Zhu, Yuhang Ye, Yifan Zhang, Xia Sun, Pu Yang, Peyman Servati, Feng Jiang

Bibliographic record

VenueNano Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsChina Scholarship CouncilCanada Foundation for Innovation
KeywordsCelluloseChitosanMaterials scienceMoisturePorosityActuatorFiberChemical engineeringComposite materialCellulose fiberNanotechnologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

Moisture-driven actuators featuring programmable stimuli-responsiveness and a rapid response have garnered substantial research attention. Cellulose-based actuators face challenges, including prolonged and unstable responsiveness, along with inadequate interfacial bonding. Herein, we developed a bilayer structured moisture actuator by integrating multiscale cellulose fibers with chitosan. The protonated chitosan forms strong electrostatic attractions with negatively charged cellulose nanofibrils (CNF), achieving a robust interfacial interaction. Leveraging the hierarchically porous structure and varying hygroscopicity of microfibrillated cellulose (MFC) and CNF, the film establishes an effective wettability gradient, enabling a stable and rapid moisture actuation performance. The bilayer film exhibits large deformation toward moisture with a bending angle of 60°, a short response time of 12 s, good stability over 50 wetting and drying cycles, and promising recyclability. Harnessing these advantageous properties, the bilayer film was demonstrated for its applications in automatic cooling textiles, contactless electrical switches, and artificial moisture-activated muscles, showing great potential for practical use.

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 score0.263
Threshold uncertainty score0.725

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 head0.007
GPT teacher head0.201
Teacher spread0.194 · 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

Citations15
Published2024
Admission routes2
Has abstractyes

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