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用于预测锂离子电池正极浆料剪切粘度和记忆效应的物理标度

Physical scaling for predicting shear viscosity and memory effects of lithium-ion battery cathode slurries.

作者信息

Gupta Yoshita, Liu Qingsong, Richards Jeffrey J

机构信息

Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA.

出版信息

Soft Matter. 2025 Feb 19;21(8):1489-1497. doi: 10.1039/d4sm01493f.

Abstract

Lithium-ion battery cathodes are manufactured by coating slurries, liquid suspensions that typically include carbon black (CB), active material, and polymer binder. These slurries have a yield stress and complex rheology due to CB's microstructural response to flow. While optimizing the formulation and processing of slurries is critical to manufacturing defect-free and high-performance cathodes, engineering the shear rheology of cathode slurries remains challenging. In this study, we conducted simultaneous rheo-electric measurements on 3 wt% CB suspensions in -methyl-2-pyrrolidone containing various loadings of active material NMC811 and polyvinylidene difluoride. Accounting for the changes in the infinite shear viscosity, the yield stress, and the medium viscosity due to the presence of NMC and polymers, we defined the differential relative viscosity. This differential relative viscosity, Δ, is a measure of the distance from the infinite shear rate, where carbon black agglomerates are fully broken down. We find that Δ collapses all flow curves regardless of formulation with an empirical relationship Δ = 2.18Mn-0.92f, indicating a quantitative prediction of the flow curve of cathode slurries across a wide range of formulation space. We then used electrical conductivity to identify and quantify shear-induced structure memory, evidenced in the ratio of the under-shear conductivity over the post-shear quiescent conductivity. We find that similar to the changes in the yield stress, increasing NMC concentration increases memory retention, and in contrast, the addition of PVDF erases memory effects. Our findings here will provide valuable insight into engineering the formulation and processing conditions of lithium-ion battery cathodes.

摘要

锂离子电池阴极是通过涂覆浆料来制造的,浆料是一种液体悬浮液,通常包括炭黑(CB)、活性材料和聚合物粘结剂。由于炭黑对流动的微观结构响应,这些浆料具有屈服应力和复杂的流变学特性。虽然优化浆料的配方和加工对于制造无缺陷和高性能的阴极至关重要,但设计阴极浆料的剪切流变学仍然具有挑战性。在本研究中,我们对含有不同负载量活性材料NMC811和聚偏二氟乙烯的N-甲基-2-吡咯烷酮中的3 wt%炭黑悬浮液进行了同步流变-电学测量。考虑到由于NMC和聚合物的存在而导致的无限剪切粘度、屈服应力和介质粘度的变化,我们定义了微分相对粘度。这种微分相对粘度Δ是从无限剪切速率开始计算的距离的度量,在无限剪切速率下炭黑团聚体完全分解。我们发现,无论配方如何,Δ都能使所有流动曲线重合,其经验关系为Δ = 2.18Mn-0.92f,这表明可以对广泛配方空间内的阴极浆料流动曲线进行定量预测。然后,我们使用电导率来识别和量化剪切诱导的结构记忆,这在剪切下电导率与剪切后静态电导率的比值中得到体现。我们发现,与屈服应力的变化类似,增加NMC浓度会增加记忆保留,相反,添加PVDF会消除记忆效应。我们在此的发现将为锂离子电池阴极的配方设计和加工条件提供有价值的见解。

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