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Record W2957910531 · doi:10.1002/macp.201900169

Hierarchical and Spiral Polymer Structures: Direct Electrospinning on Porous Anodic Aluminum Oxide Templates

2019· article· en· W2957910531 on OpenAlexaff
Ying‐Hsuan Liu, Yu‐Jing Chiu, Jiun‐Tai Chen

Bibliographic record

VenueMacromolecular Chemistry and Physics · 2019
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsMcGill University
FundersNational Chiao Tung UniversityMinistry of Science and Technology, Taiwan
KeywordsElectrospinningMaterials scienceTemplatePolymerPorosityNanotechnologyNanometreFiltration (mathematics)NanofiberOxideChemical engineeringComposite material

Abstract

fetched live from OpenAlex

Abstract Electrospun fibers with hierarchical structures have attracted a great deal of attention because they have distinct properties, such as large specific surface areas and high surface area‐to‐volume ratios, and can be applied to various fields. Here, a simple and versatile method is demonstrated to prepare electrospun fibers with hierarchical structures by directly electrospinning poly(methyl methacrylate) (PMMA) solutions on porous anodic aluminum oxide (AAO) templates. Hierarchical PMMA structures with micro‐ and nanometer scales can be generated. The two length scales of the hierarchical structures can be independently controlled; the first length scale is controlled by the electrospinning conditions, and the second length scale is controlled by the pore sizes of the AAO templates. By adding water in the polymer solutions, the evaporation of the solvents and the whipping instability of the solution jets can be manipulated. Consequently, spiral and hierarchical structures can be obtained. This study opens a promising avenue in fabricating electrospun polymer fibers with hierarchical structures, which should have great potential in a variety of applications, such as filtration, diagnosis, and tissue regeneration.

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 score0.004
Threshold uncertainty score0.865

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 head0.004
GPT teacher head0.218
Teacher spread0.214 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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