Cross-Evaluation of Stiffness Measurement Methods for Hydrogels

Hydrogel stiffness is commonly reported across the literature, but direct comparison is frequently confounded by differences in testing approach and analysis assumptions. In this study, the authors created a controlled library of 18 covalently crosslinked poly(vinyl alcohol) (PVA) hydrogel formulations by varying two synthesis-defined structural parameters: initial polymer volume fraction (φ0 = 5, 7.5, 10%) and the ideal degree of polymerization between junctions (Nj = 20–70). They then cross-evaluated five widely used stiffness measurement approaches—uniaxial tension, uniaxial compression, shear rheology, macroindentation, and nanoindentation—by converting each dataset to an effective shear modulus under incompressible neo-Hookean assumptions. Across the library, the different methods produced broadly similar stiffness values (differences typically small compared with formulation-to-formulation changes), but each method provided distinct secondary insights and practical tradeoffs (e.g., tensile testing with digital image correlation (DIC) enabled estimation of Poisson’s ratio and highlighted the importance of local strain measurement; rheology quantified viscoelastic fraction; indentation enabled assessment of surface-scale heterogeneity). Experimentally measured stiffness increased with increasing φ0 and decreased with increasing Nj, consistent with rubberlike elasticity expectations. The authors also implemented an a priori structure-based stiffness prediction workflow using the swollen polymer network model (predict swelling first, then shear modulus) and showed that while trends matched, the model systematically overestimated stiffness, especially for more dilute but highly crosslinked networks—implicating limitations in the equilibrium swelling component rather than the rubberlike elasticity component. Overall, the work provides a practical framework for comparing hydrogel stiffness across measurement modalities and for rational hydrogel design using structure-informed predictions.