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dc.contributor.authorBarghi, L
dc.contributor.authorAsgari, D
dc.contributor.authorBarar, J
dc.contributor.authorValizadeh, H
dc.date.accessioned2018-08-26T07:43:48Z
dc.date.available2018-08-26T07:43:48Z
dc.date.issued2015
dc.identifier.urihttp://dspace.tbzmed.ac.ir:8080/xmlui/handle/123456789/48068
dc.description.abstractPurpose: Polycaprolactone (PCL) is a biodegradable polyester and has attracted attention as a suitable carrier for development of controlled drug delivery due to its non-toxicity and biocompatibility. It has been reported that the biodegradability of PCL can be enhanced by copolymerization with PEG. Molecular weight (Mw) and CL block lengths optimization in a series of synthesized PCEC copolymers was the main purpose of this study. Methods: The composition of copolymers was designed using full factorial methodology. Molecular weight of used PEG (4 levels) and weight ratio of epsilon-caprolactone/PEG (3 levels) were selected as independent variables. The PCEC copolymers were synthesized by ring opening polymerization. Formation of copolymers was confirmed by FT-IR spectroscopy as well as H-NMR. The M-n of PCEC copolymers was calculated from HNMR spectra. The thermal behavior of copolymers was characterized on differential scanning calorimeter. Results: Molecular weight of twelve synthesized copolymers was ranged from 1782 to 9264. In order to evaluate the effect of selected variables on the copolymers composition and Mw, a mathematical model for each response parameter with p-value less than 0.001 were obtained. Average percent error for prediction of total M-n of copolymers and M-n of CL blocks were 13.81% and 14.88% respectively. Conclusion: In conclusion, the proposed model is significantly valid due to obtained low percent error in M-n prediction of test sets.
dc.language.isoEnglish
dc.relation.ispartofADVANCED PHARMACEUTICAL BULLETIN
dc.subjectPCEC
dc.subjectCopolymer
dc.subjectFull factorial methodology
dc.subjectMolecular weight
dc.titleSynthesis of PCEC Copolymers with Controlled Molecular Weight Using Full Factorial Methodology
dc.typeArticle
dc.citation.volume5
dc.citation.issue1
dc.citation.spage51
dc.citation.epage56
dc.citation.indexWeb of science
dc.identifier.DOIhttps://doi.org/10.5681/apb.2015.007


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