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Record W2062794643 · doi:10.1139/v08-029

Effect of relative humidity on the morphology of electrospun polymer fibers

2008· article· en· W2062794643 on OpenAlexvenueno aff
Eliton S. Medeiros, L. H. C. Mattoso, Richard D. Offeman, Delilah F. Wood, William J. Orts

Bibliographic record

VenueCanadian Journal of Chemistry · 2008
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsVinyl alcoholPolymerChemistryRelative humidityChemical engineeringSolventPolymer chemistryPolystyreneMethyl methacrylateElectrospinningScanning electron microscopeDimethylformamidePorosityTolueneEvaporationMorphology (biology)Composite materialCopolymerMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The effect of relative humidity on the morphology of electrospun fibers of poly(vinyl alcohol), poly(methyl methacrylate), poly(vinyl chloride), polystyrene, and poly(lactic acid) dissolved in solvents such as toluene, N,N-dimethylformamide, 2,2,2-trifluoroethanol, and deionized water was studied by scanning electron microscopy to investigate the factors that may contribute to pore formation. Results showed that the presence of pores depends on factors such as the type of polymers used, the polymer–solvent combination, molecular weight, and the size of the electrospun structure. The final morphology developed implies a competition between the dynamics of phase separation and the rate of solvent evaporation. In addition, continuous solvent evaporation and constant stretching owing to the electric potential difference give rise to the final shape of porous electrospun fibers.Key words: electrospinning, relative humidity, porous fibers, polymers.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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

Citations124
Published2008
Admission routes1
Has abstractyes

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Same venueCanadian Journal of ChemistrySame topicElectrospun Nanofibers in Biomedical ApplicationsFrench-language works237,207