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Record W4290929873 · doi:10.1002/macp.202200151

Electrospinning of 1D Fiber‐Like Block Copolymer Micelles with a Crystalline Core

2022· article· en· W4290929873 on OpenAlexaff
Charlotte E. Ellis, Christian Hils, Alex M. Oliver, Andreas Greiner, Holger Schmalz, Ian Manners

Bibliographic record

VenueMacromolecular Chemistry and Physics · 2022
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMicrofiberElectrospinningMicelleCopolymerMaterials sciencePolymerPolystyreneFiberCore (optical fiber)Chemical engineeringPolymer chemistryNanotechnologyComposite materialChemistryAqueous solutionOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Electrospinning is a simple, low‐cost, and high throughput technique that allows for the processing of polymers into fibers. The process can be controlled to allow access for well‐defined continuous fibers that are of interest for a wide range of applications including as tissue scaffolds, as nanowires in optoelectronic devices, and in catalysis. Conventional electrospinning processes use polymer solutions with high molecular weights. Here, the electrospinning of 1D fiber‐like block copolymer micelles containing a crystalline core is reported. The successfully accessed core‐shell microfibers in which 1D micelles containing a crystalline poly(ferrocenyldimethylsilane) (PFS) core are immobilized on a polystyrene microfiber via coaxial electrospinning. Furthermore, efforts to extend this approach to the use of 1D micelles comprising of a crystalline, π ‐conjugated poly(di‐ n ‐hexylfluorene) (PDHF) core are described. Electrospinning is also successfully used to prepare microfibers consisting solely of 1D micelles with a PFS crystalline core, the first examples where a template material is not required.

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 score0.007
Threshold uncertainty score0.597

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)0.0010.000

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 head0.209
Teacher spread0.204 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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