A binder plays a critical role in dispersion of coating liquids and the quality of coating. Poly(vinylidene fluoride) (PVDF) is widely used as a binder in cathode slurries; however, its role as a binder is still under debate. In this paper, we study the role of PVDF on the rheology of cathode battery slurries consisting of Li(Ni_1/3Mn_1/3Co_1/3)O₂ (NCM), carbon black (CB) and N-methyl-2-pyrrolidone (NMP). Rheology and microstructure of cathode slurries are systemically investigated with three model suspensions: CB/PVDF/NMP, NCM/PVDF/NMP and NCM/CB/PVDF/NMP. To highlight the role of PVDF in cathode slurries, we prepare the same model suspensions by replacing PVDF with PVP, and we compare the role of PVDF to PVP in the suspension rheology. We find that PVDF adsorbs neither onto NCM nor CB surface, which can be attributed to its poor affinity to NCM and CB. Rheological measurements suggest that PVDF mainly increases matrix viscosity in the suspension without affecting the microstructure formed by CB and NCM particles. In contrast to PVDF, PVP stabilizes the structure of CB and NCM in the model suspensions, as it is adsorbed on the CB surface. This study will provide a useful insight to fundamentally understand the rheology of cathode slurries.

The nonlinear rheology of a concentrated lithium-ion battery anode slurry was examined under large amplitude oscillatory shear and interpreted with a sequence of physical process (SPP) analysis. A complex interplay of three anode slurry components—graphite (Gr) as an active material, carbon black (CB) as a conductive additive, and carboxymethyl cellulose (CMC) as a binder—leads to a two-step yielding behavior, represented as the secondary plateau in dynamic strain and stress sweep tests. We demonstrate that a two-step yielding behavior is manifested as double deltoids in SPP analysis through the study of intra-cycle rheological transition under oscillatory shear flow. Slurries of Gr-CMC exhibit the two-step yielding behavior; slurries of CB-CMC do not, suggesting that Gr and CMC are the primary causes of two-step yielding in an anode slurry. A sedimentation test on a dilute Gr-CMC solution yielded phase separation between graphite particles, with CMC adsorbed on their surface and graphite particles aggregated via hydrophobic attraction. This indicates two possible types of interactions in a concentrated slurry: a hydrophobic interaction between graphite particles and a physicochemical interaction caused by CMC adsorbed on graphite particles. The first yielding step relates to the hydrophobic attraction between graphite particles, resulting in a network structure that is expected to be brittle and rupture at a small strain. The second yielding step is attributed to the interaction between concentrated CMC, which is corroborated by the overlap of the secondary deltoid of the anode slurry and the single deltoid of the concentrated CMC solution in SPP analysis.

Inspired by the analogy between the application process of cosmetics and large amplitude oscillatory shear (LAOS), we suggest a novel predictive model for the spreadability of cosmetic formulations via LAOS analysis and machine learning techniques. Rheological measurements of cosmetic formulations, including the transient elastic and viscous moduli from the sequence of physical process (SPP) analysis, were selected as features for the predictive models, and the spreadability of each formulation that is quantitatively rated by trained panels was set up as the target variable. First, multiple linear regression prediction models are derived, and it was shown that the LAOS-SPP parameters were more effective features than other rheological parameters that were conventionally related to spreadability of cosmetics. Additionally, a non-linear prediction model was developed based on the random forest regressor algorithm, considering the possibility of the nonlinear correlation between rheological measurements and spreadability. The random forest regressor model showed better performance than the linear regression model, and the LAOS-SPP parameters were found to be more effective features for the random forest regressor model as in the multiple linear regression model. The correlation between the LAOS-SPP parameters and the spreadability is interpreted in terms of the rheological transition during rubbing process of cosmetics. Our findings indicate the importance of the nonlinear rheological behavior in the texture perception mechanism of cosmetics, and how rheological measurements can be combined with machine learning techniques to solve complicated problems.

Polymers adsorbed on nanoparticles (NPs) are important elements that determine the dispersion of NPs in polymer nanocomposite (PNC) films. While previous studies have shown that increasing the number of adsorbed polymers on NPs can improve their dispersion during the drying process, the exact mechanism remained unclear. In this study, we investigated the role of adsorbed polymers in determining the microstructure and dispersion of NPs during the drying process. Investigation of the structural development of NPs using the synchrotron vertical-small-angle X-ray scattering technique revealed that increasing polymer adsorption suppresses bonding between the NPs at later stages of drying, when they approach each other and come in contact. On the particle length scale, NPs with large amounts of adsorbed polymers form loose clusters, whereas those with smaller amounts of adsorbed polymers form dense clusters. On the cluster length scale, loose clusters of NPs with large amounts of adsorbed polymers build densely packed aggregates, while dense clusters of NPs with small amounts of adsorbed polymers become organized into loose aggregates. The potential for the quantitative control of NP dispersion in PNC films via modification of polymer adsorption was established in this study.