(33c) Towards Rheological Structure-Property Relationships: New Material Functions Based on Recoverable Strain, & Frequency-Sweep Medium?Amplitude Oscillatory Shear (MAOS)

Relating bulk rheological properties of a material to its structure is an active area of research. In recent times, we have taken two approaches to tackle this problem: (a) recoverable strain based new material functions that are protocol-agnostic (the topic of this oral presentation), and (b) frequency‑sweep medium‑amplitude oscillatory shear (MAOS) (covered separately during the poster session).

(a) A significant step towards developing rheological structure-property relationships is to define new material functions that can be directly linked to structural measures. Recent studies have shown such material functions to be defined in terms of the recoverable and unrecoverable strains [Lee et al., Phys. Rev. Lett. (2019); Lee et al., AIChE J (2019); Lee et al., J. Rheol. (2019)]. We explored the consequences of applying these ideas to transient rheological tests, using new material functions including an elastic modulus and a flow viscosity defined in terms of the recoverable strain and the rate of acquisition of unrecoverable strain, respectively [1]. These material functions, based on recoverable and unrecoverable strains (rather than total strain), are defined in the same way independent of the test protocol and provide the viscoelastic properties of a material in a clear and succinct way without requiring a priori knowledge of the constitutive model. At short times, we observed that for a self-assembled wormlike micellar solution, the new material functions exhibit a constant (plateau) modulus and a constant (zero-shear) viscosity independent of the applied shear rate, even under conditions that eventually lead to nonlinear responses. This observation quantitatively corroborates the intuitive picture of material deformation that when the material is close to equilibrium, it responds according to its linear viscoelastic material properties for both linear and nonlinear deformations. The fundamental, universal, and unifying nature of these new transient measures are showing promise in explaining a range of phenomena, including the Payne effect for filled polymers and yield stress materials [2], as well as suggesting new improved dimensionless groups for more accurate flow diagnosis. This work lays the foundation for measuring material functions that are directly related to the material structure and are agnostic of the testing protocol. Future directions include measuring these metrics for materials with non-trivial relaxation spectrum and interpreting them in terms of the material viscoelastic properties.

(b) Weakly nonlinear perturbations from equilibrium such as MAOS provide valuable insights into the material structure. This is due to the extreme sensitivity of MAOS material functions (magnitude and sign) to the underlying microstructure. However, conventional MAOS requires many strain amplitude sweeps, and typically multiple sample loadings, to obtain frequency-dependent properties. As a result, MAOS is not practical for time-sensitive and scarce materials. We developed a new MAOS methodology: Frequency-sweep MAOS [3], which requires frequency‑sweeps at just two suitable strain amplitudes. This method is much faster than the conventional MAOS and typically requires about three loadings, making it more accessible to researchers. Although it shares similarity with conventional small‑amplitude oscillatory shear (SAOS), the choice of the two strain amplitudes requires care so that the MAOS material functions are obtained within the asymptotically nonlinear regime. We proposed and demonstrated confidence metrics for this purpose, which can detect if the data becomes too noisy or too nonlinear. Recently, this method allowed researchers to rapidly generate MAOS data across various compositions in a hydrogel, which they used to uncover the mechanisms underlying the formation of a reversible network [Martinetti et al, Macromolecules (2018)]. Microstructural inferences from MAOS further benefit from proper uncertainty quantification, and robust model selection and fitting methods [4]. Future directions can include generating MAOS signatures for scarce materials like model branched polymers, which can then be used as a detection and differentiation tool for polymer architectures.

References:

[1] Singh P. K., Lee C. W., Patankar K. A., Rogers S. A., Journal of Rheology (2021) https://doi.org/10.1122/8.0000154

[2] Donley G. J., Singh P. K., Shetty A., Rogers S. A., Proceedings of the National Academy of Sciences (2020) https://doi.org/10.1073/pnas.2003869117

[3] Singh P. K., Soulages J. M. and Ewoldt R. H., Journal of Rheology (2018) https://doi.org/10.1122/1.4999795

[4] Singh P. K., Soulages J. M. and Ewoldt R. H., Rheologica Acta (2019) https://doi.org/10.1007/s00397-019-01135-1 ;

Singh, P. K., Ph.D. Thesis (2019) http://hdl.handle.net/2142/105572