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Record W4409091604 · doi:10.1021/acsnano.4c18542

Magnetoresponsive Cellulose Nanofiber Hydrogels: Dynamic Structuring, Selective Light Transmission, and Information Encoding

2025· article· en· W4409091604 on OpenAlexafffund
Junhua Xu, Yujun Zou, Huangjingyi Chen, Zhangmin Wan, Ayako Takagi, Zhiguo Wang, Juan Yu, Liang Liu, Yi Lu, Yimin Fan, Orlando J. Rojas

Bibliographic record

VenueACS Nano · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of ChinaCanada Excellence Research Chairs, Government of CanadaCanada Foundation for Innovation
KeywordsNanofiberStructuringCelluloseSelf-healing hydrogelsEncoding (memory)Materials scienceTransmission (telecommunications)Information transmissionNanotechnologyChemical engineeringComputer sciencePolymer chemistryTelecommunicationsComputer networkEngineeringBusiness

Abstract

fetched live from OpenAlex

Angle-dependent light reflection is a common phenomenon in nature, typically arising from the spatial arrangement of biological or mineral structures. Various strategies have been developed to replicate these assemblies, particularly to achieve structural color through bottom-up self- and directed assembly. However, dynamic control of light reflection remains a significant challenge. In this study, we present TEMPO-oxidized cellulose nanofibers modified with magnetic nanoparticles (approximately 10 nm in size) that exhibit rapid, directional alignment in aqueous media under magnetic fields, resulting in angle-dependent light reflection. By combining magnetic field manipulation with gas-phase hydrogelation, we were able to arrest the hydrogel structure, preserving the nanofibers' spatial and temporal orientation. This system enables the creation of on-demand optical patterns, which we demonstrate through selective light transmission and reflection, offering potential for applications in information coding, storage, and encryption.

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 score0.029
Threshold uncertainty score0.659

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 communication0.0000.001
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 head0.005
GPT teacher head0.253
Teacher spread0.248 · 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
Published2025
Admission routes2
Has abstractyes

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