Our RSC Soft Matter article is now published. The work describes the validation of a new method for the automated analysis of thousands of indentation curves for the 3D mapping of the mechanical properties of soft materials. The work was carried out in collaboration with Dr. Sebastien Rochat and Prof. Richard Trask at the University of Bristol, and funded by the EU Marie Curie Actions and the EPSRC of UK.
The Young’s modulus (an intrinsic measure of stiffness) of soft, hydrated materials is important for many applications, particularly in biomedical fields. Currently, the most commonly applied techniques to obtain this parameter examine either the bulk (compression/tensile testing, rheology) or nanoscale (nanoindentation, AFM) properties of these materials. To complement these techniques, and enable the study of macroscale mechanical heterogeneity, we apply instrumented indentation with a microscale probe to the study of soft materials. In particular, we expect that this technique could provide a unique and highly useful perspective of the mechanical properties of tissue-like biomimetic materials. However, automating the analysis of data obtained by the microindentation of soft materials remains challenging. Appropriate theoretical models are complex, and indentation curves frequently show deviations from ideal behaviour due to inelastic material deformation or influence from the underlying substrate, leading inaccurate mechanical characterization.
In this work, published in Soft Matter, we detail the development of an innovative method to enable automated high-throughput determination of mechanical properties of soft materials obtained from microindentation measurements. Specifically, at the heart of our method is an algorithm that allows for Young’s moduli to be extracted even from imperfect indentation curves by automatic selection of the portion of the data which complies best with a chosen contact mechanics model. By applying this approach in combination with a commercial MEMS-based microindenter, we were able to reliably obtain the Young’s moduli of a range of hydrogel materials with minimal influence of experimental parameters such as indentation depth or speed. Significantly, the high degree of automation of our method enabled rapid analysis of very large datasets (>1600 individual indentation curves) such as two-dimensional property mapping over macroscopic areas of soft materials. This capability was demonstrated by the mechanical characterisation of patterned hydrogel materials.
Overall, we believe that this innovative methodology provides an ideal platform to enable the mechanical characterisation of soft materials displaying heterogeneity over multiple length scales. In addition to hydrogels, these findings are likely to be applicable to a wide range of hydrated soft materials with comparable mechanical properties, including many biological or biomimetic systems.
Full article here.
The Gobbo Group 