Abstract
To predict the service life of polystyrene (PS) under an aggressive environment, the nondimensional expressionZwas established from a data set of multiple properties of PS by principal component analysis (PCA). In this study, PS specimens were exposed to the tropical environment on Xisha Islands in China for two years. Chromatic aberration, gloss, tensile strength, elongation at break, flexural strength, and impact strength were tested to evaluate the aging behavior of PS. Based on different needs of industries, each of the multiple properties could be used to evaluate the service life of PS. However, selecting a single performance variation will inevitably hide some information about the entire aging process. Therefore, finding a comprehensive measure representing the overall aging performance of PS can be highly significant. Herein, PCA was applied to obtain a specific property (Z) which can represent all properties of PS.Zof PS degradation showed a slight decrease for the initial two months of exposure after which it increased rapidly in the next eight months. Subsequently, a slower increase ofZvalue was observed. From the three different stages shown asZvalue increases, three stages have been identified for PS service life.
Highlights
In the last four decades, the use of polymeric materials has rapidly increased but it is well established that these materials are susceptible to rapid photodegradation when exposed to natural weathering [1,2,3,4]
The influences of ultraviolet radiation [9], oxygen [10], chemical composition [11], and degradation products [12, 13] to the aging mechanisms have been demonstrated by previous research, providing a good basis for the prediction of service lives of polymeric materials
PS samples were exposed in natural environment on Xisha Islands for 2 years and their appearance and mechanical properties were analyzed at different exposure time
Summary
In the last four decades, the use of polymeric materials has rapidly increased but it is well established that these materials are susceptible to rapid photodegradation when exposed to natural weathering [1,2,3,4]. The smaller number of uncorrelated variables is called principal components It can be considered as a process for extracting hidden, simplified structures within the data in a way which best explains the variance in the data [20]. Zhao et al evaluated the degradation behavior of EPDM upon exposure to an artificial weathering environment produced by xenon arc lamp using PCA [21]. The authors have used PCA to evaluate the aging behaviors of PE and PP in the natural environment of Xisha Islands [22, 23]. If the effects of all properties are considered, the evaluation might be contradictory sometimes Such dilemma can be solved by using a PCA method which could reduce the redundancy in these properties and extract a smaller number of principal components (artificial variables). The aging of PS in Xisha atmospheric environment was evaluated using a PCA method
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