[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v656y2024ics0378437124007313.html
   My bibliography  Save this article

Shape parameter of Weibull size statistics is a potential indicator of filler geometry in SiO2 reinforced polymer composites

Author

Listed:
  • Jin, Huan
  • Sun, Wenxun
  • Qin, Xianan
Abstract
In a previous study [Physica A, 625 (2023), 129026], a relationship between the filler size distribution and the filler geometry of SiO2 particle reinforced polymer composites has been reported. It has been experimentally demonstrated that the size of hollow and solid SiO2 particles disperse in polymer matrix follows Weibull statistics with shape parameter at 2 and 3, respectively. This mechanism has not yet been verified in the one-dimensional (1D) case. In this paper, we study the length distribution of glass fibers in polymer composites. Our results show that the previous theory still holds for the 1D case. Thus, shape parameter of Weibull size statistics could be a potential indicator of filler geometry in SiO2 reinforced polymer composites. This interesting mechanism can be explained by the scaling nature behind the Weibull statistics. Our study has thus shed new light on the evolution of filler geometry during the fabrication process of polymer composites, and should be useful for the related fields.

Suggested Citation

  • Jin, Huan & Sun, Wenxun & Qin, Xianan, 2024. "Shape parameter of Weibull size statistics is a potential indicator of filler geometry in SiO2 reinforced polymer composites," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 656(C).
  • Handle: RePEc:eee:phsmap:v:656:y:2024:i:c:s0378437124007313
    DOI: 10.1016/j.physa.2024.130222
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437124007313
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2024.130222?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:656:y:2024:i:c:s0378437124007313. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.