Institute of Materials Science, Biocompatible Nanomaterials, Kiel University, Kiel, Germany.
PLoS One. 2019 Aug 2;14(8):e0220281. doi: 10.1371/journal.pone.0220281. eCollection 2019.
Measurements of Young's moduli are mostly evaluated using strong assumptions, such as sample homogeneity and isotropy. At the same time, descriptions of measurement parameters often lack detailed specifications. 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. 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. 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. This approach is independent of the chosen elastic fitting model and indentation device. 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. To prove the applicability of our algorithm, we carried out a systematic analysis of how the indentation speed, indenter size and sample thickness affect the determination of Young's modulus from atomic force microscope (AFM) indentation curves on polyacrylamide (PAAm) samples. We chose the Hertz model as the elastic fitting model for this proof of principle of our algorithm and found that all of these parameters influence the measured Young's moduli to a certain extent. Hence, it is essential to clearly state the experimental parameters used in microindentation experiments to ensure reproducibility and comparability of data.
杨氏模量的测量大多是基于一些强假设,例如样品的均匀性和各向同性。同时,测量参数的描述往往缺乏详细的规范。对于软水凝胶来说,其中许多假设并不完全成立,而且水凝胶微压痕的复杂性要求更复杂的实验程序,以便更准确地描述其弹性性质。我们创建了一个算法,该算法可以自动分析压痕数据,作为评估具有考虑压痕深度对测量杨氏模量影响的大数据集的基础。该算法自动确定在压痕区域中杨氏模量变得不依赖于压痕深度的位置,并且进一步最小化了将弹性模型拟合到数据的误差。这种方法独立于所选的弹性拟合模型和压痕设备。通过这种方法,我们能够评估在许多不同的样品位置记录的大量压痕曲线,因此可以应用统计学方法来克服由于样品不均匀性引起的偏差。为了证明我们的算法的适用性,我们系统地分析了压痕速度、压头尺寸和样品厚度如何影响从原子力显微镜(AFM)压痕曲线上确定聚丙烯酰胺(PAAm)样品的杨氏模量。我们选择赫兹模型作为弹性拟合模型,以证明我们算法的原理,发现所有这些参数都在一定程度上影响测量的杨氏模量。因此,必须明确说明微压痕实验中使用的实验参数,以确保数据的可重复性和可比性。