JOURNAL ARTICLE
Non-linear rheological behaviors of polyvinyl alcohol/silver nanowire/silica nanoparticle suspensions under large amplitude oscillatory shear flows.
Published In: Physics of Fluids, 2024, v. 36, n. 8. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Kim, Si Yoon; Song, Hyeong Yong; Lee, Jeonghyeon; Park, Min Seo; Lee, Seung Hak; Park, Jun Dong; Hyun, Kyu 3 of 3
Abstract
This article focuses on the investigation of non-linear rheological behaviors of silver nanowire (AgNW) suspensions containing silica nanoparticles (SiNPs) dispersed in aqueous polyvinyl alcohol (PVA) solutions under large amplitude oscillatory shear (LAOS) flows. Using LAOS moduli, Fourier-transform (FT) rheology, and the sequence of physical processes (SPP) methods, the study classifies the suspensions into three distinct LAOS types (A, B, and C) based on the ratio of SiNP to AgNW concentrations (φSi/φAg) and corresponding microstructures: entangled AgNW networks (type A), mixed networks of AgNWs and AgNW–SiNP bundles (type B), and predominantly stiff AgNW–SiNP bundles (type C). The research demonstrates that linear viscoelastic measurements (SAOS) are insufficient to fully characterize microstructural changes, whereas LAOS combined with FT and SPP analyses effectively reveal complex intracycle and inter-cycle structural transitions related to network formation, yielding, and alignment. These findings provide insights into the rheological behavior and microstructural evolution of PVA/AgNW/SiNP suspensions, which are relevant for optimizing conductive nanomaterial suspensions in industrial applications.
Additional Information
- Source:Physics of Fluids. 2024/08, Vol. 36, Issue 8, p1
- Document Type:Article
- Subject Area:Chemistry
- Publication Date:2024
- ISSN:1070-6631
- DOI:10.1063/5.0228571
- Accession Number:179372976
- Copyright Statement:Copyright of Physics of Fluids is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.