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屈服应力流体与基本粒子统计

Yield stress fluids and fundamental particle statistics.

作者信息

Mezzasalma Stefano A

机构信息

Materials Physics Division, Ruđer Bošković Institute Bijenička cesta 54 10000 Zagreb Croatia

出版信息

RSC Adv. 2019 Jun 14;9(32):18678-18687. doi: 10.1039/c9ra02150g. eCollection 2019 Jun 10.

DOI:10.1039/c9ra02150g
PMID:35515264
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9064768/
Abstract

Yield stress in complex fluids is described by resorting to fundamental statistical mechanics for clusters with different particle occupancy numbers. Probability distribution functions are determined for canonical ensembles of volumes displaced at the incipient motion in three representative states (single, double, and multiple occupancies). The statistical average points out an effective solid fraction by which the yield stress behavior is satisfactorily described in a number of aqueous (SiN, Ca(PO), ZrO, and TiO) and non-aqueous (AlO/decalin and MWCNT/PC) disperse systems. Interestingly, the only two model coefficients (maximum packing fraction and stiffness parameter) turn out to be correlated with the relevant suspension quantities. The latter relates linearly with (Young's and bulk) mechanical moduli, whereas the former, once represented the Hamaker constant of two particles in a medium, returns a good linear extrapolation of the packing fraction for the simple cubic cell, here recovered within a relative error ≈ 1.3%.

摘要

通过采用针对具有不同粒子占据数的团簇的基本统计力学来描述复杂流体中的屈服应力。确定了在三种代表性状态（单占据、双占据和多占据）下初始运动时位移体积的正则系综的概率分布函数。统计平均值指出了一个有效固体分数，通过该分数可以在许多水性（SiN、Ca(PO)、ZrO和TiO）和非水性（AlO/十氢化萘和MWCNT/PC）分散体系中令人满意地描述屈服应力行为。有趣的是，仅有的两个模型系数（最大堆积分数和刚度参数）结果与相关的悬浮量相关。后者与（杨氏和体积）力学模量呈线性关系，而前者一旦表示介质中两个粒子的哈梅克常数，就会对简单立方晶胞的堆积分数给出良好的线性外推，此处的相对误差约为1.3%时恢复该结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abe2/9064768/16a1323bfdc8/c9ra02150g-f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abe2/9064768/0510d8fe93ab/c9ra02150g-f1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abe2/9064768/3d4af733a0fe/c9ra02150g-f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abe2/9064768/53c916f41654/c9ra02150g-f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abe2/9064768/16a1323bfdc8/c9ra02150g-f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abe2/9064768/0510d8fe93ab/c9ra02150g-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abe2/9064768/9d97a0f09961/c9ra02150g-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abe2/9064768/3895cca9538f/c9ra02150g-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abe2/9064768/fcd2dab51fae/c9ra02150g-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abe2/9064768/bc32ab71d691/c9ra02150g-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abe2/9064768/7032149bcb3e/c9ra02150g-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abe2/9064768/c60200d4cdd6/c9ra02150g-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abe2/9064768/3d4af733a0fe/c9ra02150g-f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abe2/9064768/53c916f41654/c9ra02150g-f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abe2/9064768/16a1323bfdc8/c9ra02150g-f10.jpg

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