Study of relationships between morphology of polyvinylbutyral nanofibers and solvents properties using a predictive numerical model

dc.contributor.authorLubasová Danielacs
dc.contributor.authorŠpánek Romancs
dc.date.accessioned2018-09-25T12:11:20Z
dc.date.available2018-09-25T12:11:20Z
dc.date.issued2017cs
dc.description.abstractNanofiber membranes can be used for many applications, however, their morphology dramatically influences properties of final products. Moreover producing nanofibers with desired morphology is extremely complicated since even a small change in any of used solvents will result in a different morphology. Engineering of the morphology is nowadays mostly driven by either (i) personal experience of researcher or (ii) trial and error approaches. The paper presents a complex predictive model allowing controlled nanofiber morphology of polyvinylbutyral that overcomes previous works by considering the most important solvent properties such as permittivity, vapor pressure together with the polymer solubility parameter. The proposed predictive model is able to rank solvents or solvent mixtures allowing direct comparison and selection of more or less appropriate solvents for electrospinning of polyvinylbutyral with respect to desired morphology of nanofibers. The predictive model was experimentally verified by electrospinning of polyvinylbutyral dissolved in eight solvents and their mixtures.en
dc.format.extent10cs
dc.identifier.doi10.1166/jctn.2017.6578
dc.identifier.issn1546-1955cs
dc.identifier.urihttps://dspace.tul.cz/handle/15240/30794
dc.identifier.urihttps://www.ingentaconnect.com/content/asp/jctn/2017/00000014/00000006/art00030
dc.language.isoengcs
dc.publisherAmerican Scientific Publisherscs
dc.relation.ispartofseries0cs
dc.relation.urihttp://www.ingentaconnect.com/contentone/asp/jctn/2017/00000014/00000006/art00030cs
dc.subjectelectrospinningcs
dc.subjectnumerical modelcs
dc.subjectsolventscs
dc.subjectnanofiberscs
dc.titleStudy of relationships between morphology of polyvinylbutyral nanofibers and solvents properties using a predictive numerical modelcs
local.citation.epage2802-2811cs
local.citation.spage2802-2811cs
local.identifier.publikace4244
local.identifier.wok999en
local.relation.issue6cs
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