流变学
材料科学
水溶液
纤维素
化学工程
纳米晶
水悬浮液
复合材料
纳米技术
化学
有机化学
工程类
作者
Jiatong Xu,Pengguang Wang,Ziyu Zhou,Baihua Yuan,Hongbin Zhang
出处
期刊:Journal of Rheology
[American Institute of Physics]
日期:2024-05-16
卷期号:68 (4): 491-508
被引量:20
摘要
In this work, the nonlinear rheological behavior of aqueous suspensions composed of two typical nanocellulose [rod-like cellulose nanocrystals (CNCs) and filamentous cellulose nanofibrils (CNFs)] was examined and compared by using various large-amplitude oscillatory shear (LAOS) analysis methods, such as Fourier-transform rheology, stress decomposition, Chebyshev polynomials, and the sequence of physical processes. From our analysis, the nonlinear rheological parameters of higher harmonics, dissipation ratio, strain hardening ratio, shear thickening ratio, transient modulus, and cage modulus were obtained and quantitatively analyzed. CNCs tend to assemble to form anisotropic structures in an aqueous medium while the CNFs are entangled to form gels. The CNF suspensions demonstrated a significant viscous modulus overshoot and had stronger yield stresses, but the yield of CNC suspensions was more ductile. In the case of low concentrations, the CNF suspensions demonstrated stronger intracycle shear thickening behavior in medium-amplitude oscillatory shear region and lower dissipation ratios at small strain amplitudes. Although both nanocellulose suspensions revealed the existence of four intracycle rheological transition processes (viscoplastic deformation, structural recovery, early-stage yielding, and late-stage yielding), the CNF suspensions exhibited a stronger structural recovery ability. Larger strain amplitudes did not invariably result in a broader range of intracycle rheological transitions, which are also affected by the excitation frequency. The application of the various LAOS analysis methods provided valuable intracycle nonlinear rheological insights into nanocellulose suspensions, which are of great importance for enhancing their industrial perspectives.
科研通智能强力驱动
Strongly Powered by AbleSci AI