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Record W2246508414 · doi:10.1002/mame.201500317

Electrospinning of Colloidal Lignin in Poly(ethylene oxide)<i>N</i>,<i>N</i>-Dimethylformamide Solutions

2016· article· en· W2246508414 on OpenAlexafffund
Samira Aslanzadeh, Zhengxiang Zhu, Qi Luo, Behzad Ahvazi, Yaman Boluk, Cagri Ayranci

Bibliographic record

VenueMacromolecular Materials and Engineering · 2016
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsNational Institute for NanotechnologyAlberta InnovatesUniversity of Alberta
FundersAlberta Innovates - Technology Futures
KeywordsMacromoleculeColloidMaterials scienceVolume fractionEthylene oxideDimethylformamideChemical engineeringPolymer chemistryLigninElectrospinningPolymerCopolymerComposite materialOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Electrospinning of sulfur-free softwood lignin (SFSL) in N,N-dimethylformamide (DMF) is reported as is and with poly(ethylene oxide) (PEO). SFSL macromolecules behave as rigid spheres, instead of free draining macromolecules in DMF. Hence they are investigated as colloids. Colloidal SFSL generates uniform fibers only at the volume fraction of 0.63. It is due to the sufficiently high longest mean relaxation time at the volume fraction of 0.63. Colloidal SFSL below the volume fraction of 0.63 does not exhibit any measurable viscoelasticity and also does not generate any uniform fibers. Bead-free fibers are generated at volume fractions below 0.63 only by adding PEO. PEO presence brings elasticity to colloidal SFSL and produces bead-free fibers only above the entanglement concentration of PEO in DMF. The presence of SFSL macromolecules does not cause any interactions with PEO molecules, except it reduces the available of free volume for PEO chains in DMF.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.197
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2016
Admission routes2
Has abstractyes

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