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Record W3198385688 · doi:10.1039/d1na00508a

Recent progress in cellulose-based electrospun nanofibers as multifunctional materials

2021· review· en· W3198385688 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNanoscale Advances · 2021
Typereview
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsNanofiberCelluloseMaterials scienceElectrospinningNanotechnologyPolymer scienceComposite materialPolymerChemical engineeringEngineering

Abstract

fetched live from OpenAlex

Cellulose, the most abundant natural polymer, has good biocompatibility, biodegradability, and non-toxicity, which make it and its derivatives promising candidates for the fabrication of multifunctional materials, while maintaining sustainability and environmental friendliness. The combination of electrospinning technology and cellulose (and its derivatives) provides a feasible approach to produce nanostructured porous materials with promising functionalities, flexibility, renewability and biodegradability. At the same time, it enables value-added applications of cellulose and its derivatives that are derived from nature or even biomass waste. This review summarizes and discusses the latest progress in cellulose-based electrospun nanofibers, including their construction methods and conditions, various available raw materials, and applications in multiple areas (water treatment, biomaterials, sensors, electro-conductive materials, active packaging, and so on), which are followed by the conclusion and prospects associated with future opportunities and challenges in this active research area.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.019
GPT teacher head0.327
Teacher spread0.308 · 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