Towards predicting the piezoelectricity and physiochemical properties of the electrospun P(VDF-TrFE) nanogenrators using an artificial neural network Abolhasani, Mohammad Mahdi, Shirvanimoghaddam, Kamyar, Khayyam, Hamid, Moosavi, Seyed Masoud, Zohdi, Nima and Naebe, Minoo 2018, Towards predicting the piezoelectricity and physiochemical properties of the electrospun P(VDF-TrFE) nanogenrators using an artificial neural network, Polymer Testing, vol. 66, pp. 178-188, doi: 10.1016/j.polymertesting.2018.01.010. Attached Files Name Description MIMEType Size Downloads Title Towards predicting the piezoelectricity and physiochemical properties of the electrospun P(VDF-TrFE) nanogenrators using an artificial neural network Author(s) Abolhasani, Mohammad Mahdi Shirvanimoghaddam, Kamyar Khayyam, Hamid Moosavi, Seyed Masoud Zohdi, Nima Naebe, Minoo orcid.org/0000-0002-0607-6327 Journal name Polymer Testing Volume number 66 Start page 178 End page 188 Total pages 11 Publisher Elsevier Place of publication London, Eng. Publication date 2018-04-01 ISSN 0142-9418 1873-2348 Keyword(s) nanogenerator piezoelectricity artificial neural network nanofibre science & technology technology physical sciences materials science - characterization & testing polymer science materials science poly(vinylidene fluoride) nanofibers response-surface methodology strengthening mechanisms ferroelectric properties PVDF nanocomposites process parameters carbon nanofibers polymer melts diameter optimization Language eng DOI 10.1016/j.polymertesting.2018.01.010 Field of Research 0303 Macromolecular And Materials Chemistry 0912 Materials Engineering 0913 Mechanical Engineering HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2018, Elsevier Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30106332 Document type: Journal Article Collections: Institute for Frontier Materials Open Access Collection GTP Research Related Links Link Description Link to full-text (open access)   Connect to Elements publication management system Go to link with your DU access privileges   Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 16 Feb 2018, 06:52:11 EST Sat, 17 Feb 2018, 04:00:20 EST Mon, 19 Feb 2018, 07:43:38 EST Sat, 21 Apr 2018, 02:31:18 EST Wed, 21 Nov 2018, 12:21:35 EST Wed, 21 Nov 2018, 12:30:21 EST Wed, 30 Jan 2019, 05:04:10 EST Mon, 11 Feb 2019, 09:54:21 EST Sat, 17 Oct 2020, 08:35:19 EST Filtered Full Citation counts:   Cited 20 times in TR Web of Science Cited 20 times in Scopus Search Google Scholar Access Statistics: 283 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Fri, 16 Feb 2018, 06:52:11 EST