New method for estimating shift factors in time–temperature superposition models View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-08

AUTHORS

Salvador Naya, Antonio Meneses, Javier Tarrío-Saavedra, Ramón Artiaga, Jorge López-Beceiro, Carlos Gracia-Fernández

ABSTRACT

Prediction of polymer properties at short and long observation times is usually performed through time–temperature superposition (TTS) models, which make use of some calculated shift factors. Although TTS principle has been used for many decades, no firm rules have been developed for obtaining the master curves. In the absence of reliable long-term data, it has been a common practice to try to minimize the discrepancy between the individual shifted curves. It was reported that a TTS method is more reliable as that discrepancy is minimized. In this study, a new method for obtaining the shift factors is presented. The optimal shift factors were estimated by minimizing the distance between the single curve derivatives with respect to the derivative of the curve at the reference temperature. That shift factors were tested with some classical models. The data were analyzed by statistical methods, making use of bootstrap resampling and spline estimation. The shift factors obtained from the proposed method allow for obtaining smooth master curves. The accuracy of the estimations was evaluated. More... »

PAGES

453-460

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10973-013-3193-1

DOI

http://dx.doi.org/10.1007/s10973-013-3193-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1009559684


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