abstract
The effect of polypropylene (PP) on the Seebeck coefficient (S) of carbon nanofibers (CNFs) in melt-extruded PP composites filled with up to 5 wt. % of CNFs was analyzed in this study. The as-received CNFs present an electrical conductivity of similar to 320 S m(-1) and an interesting phenomenon of showing negative S-values of -5.5 mu VK-1, with 10(-2) mu W/mK(2) as the power factor (PF). In contrast, the PP/CNF composites with 5 wt. % of CNFs showed lower conductivities of similar to 50 S m(-1), less negative S-values of -3.8 mu VK-1, and a PF of 7 x 10(-4) mu W/mK(2). In particular, the change in the Seebeck coefficient of the PP/CNF composites is explained by a slight electron donation from the outer layers of the CNFs to the PP molecules, which could reduce the S-values of the as-received CNFs. Our study indicates that even insulating polymers such as PP may have a quantifiable effect on the intrinsic Seebeck coefficient of carbon-based nanostructures, and this fact should also be taken into consideration to tailor conductive polymer composites with the desired thermoelectric (TE) properties.
keywords
N-TYPE; P-TYPE; CONDUCTIVITY; NANOTUBES; PERFORMANCE; FABRICATION; POWER
subject category
Polymer Science
authors
Paleo, AJ; Krause, B; Cerqueira, MF; Melle-Franco, M; Potschke, P; Rocha, AM
our authors
Manuel Melle-Franco
Principal ResearcherProjects
Projeto de Investigação Exploratória: Manuel Melle (IF Manuel Melle)
acknowledgements
The authors affiliated with 2C2T acknowledge support from FCT-Foundation for Science and Technology within the scope of project UID/CTM/00264/2020. The authors would like to thank the staff of the IPF's Research Technology Department for their support with the TE measuring device, and they appreciate the help of Mrs. Manuela Heber in the SEM study and Dr. Oliver Schraidt from INL in the TEM analysis. In addition, support through project IF/00894/2015 and within the scope of the project CICECO-Aveiro Institute of Materials, UIDB/50011/2020 and UIDP/50011/2020 and access to the Navigator platform (LCA-UC) through the Advanced Computing Project CPCA/A2/2524/2020, financed by national funds through the Portuguese Foundation for Science and Technology I.P./MCTES, is gratefully acknowledged.