![]() ![]() ![]() With this, we are able to evaluate large amounts of indentation curves recorded on many different sample positions and can therefore apply statistical methods to overcome deviations due to sample inhomogeneities. This approach is independent of the chosen elastic fitting model and indentation device. The algorithm automatically determines the Young’s modulus in indentation regions where it becomes independent of the indentation depth and furthermore minimizes the error from fitting an elastic model to the data. ![]() We created an algorithm that automates indentation data analysis as a basis for the evaluation of large data sets with consideration of the influence of indentation depth on the measured Young’s modulus. Many of these assumptions are, for soft hydrogels especially, not completely valid and the complexity of hydrogel microindentation demands more sophisticated experimental procedures in order to describe their elastic properties more accurately. At the same time, descriptions of measurement parameters often lack detailed specifications. Measurements of Young’s moduli are mostly evaluated using strong assumptions, such as sample homogeneity and isotropy. ![]()
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