Discrimination of adhesion and viscoelasticity from nanoscale maps of polymer surfaces using bimodal atomic force microscopy
Discrimination of adhesion and viscoelasticity from nanoscale maps of polymer surfaces using bimodal atomic force microscopy
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DOI：
10.1039/d1nr03437e
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发表时间：
2021-08-30
期刊：
影响因子：
6.7
通讯作者：
Raman, Arvind
中科院分区：
文献类型：
--
作者：
Rajabifar, Bahram;Bajaj, Anil;Raman, Arvind
在亚微米尺度表面成像过程中，双峰原子力显微镜（AFM）中两个本征模的同时激发和测量增加了与正常轻敲模式相比图像的每个像素处的可观测量的数量。然而，双峰AFM观测值和聚合物样品的表面粘合剂和粘弹性之间的全面联系仍然难以捉摸。为了解决这个差距，我们首先提出了一种算法，系统地容纳表面力和线性粘弹性三维变形计算通过阿塔德的模型到双峰AFM框架。该算法同时满足两个共振本征模的振幅减小公式，并使严格的预测和解释的双峰AFM观测值的第一性原理的方法。我们使用所提出的算法来预测局部粘附和标准线性固体（SLS）的本构参数以及操作条件的双峰AFM观测值的依赖。其次，我们提出了一种逆方法来定量预测局部粘附和SLS粘弹性参数从双峰AFM数据上获得的非均匀样品。我们证明了该方法的实验使用双峰原子力显微镜对聚苯乙烯-低密度聚乙烯（PS-LDPE）聚合物共混物。这种逆方法能够从双峰AFM图的这种样品的粘附性和粘弹性的定量歧视，并打开了大门，先进的计算相互作用模型被用来量化局部纳米力学性能的粘合剂，粘弹性材料使用双峰AFM。
The simultaneous excitation and measurement of two eigenmodes in bimodal atomic force microscopy (AFM) during sub-micron scale surface imaging augments the number of observables at each pixel of the image compared to the normal tapping mode. However, a comprehensive connection between the bimodal AFM observables and the surface adhesive and viscoelastic properties of polymer samples remains elusive. To address this gap, we first propose an algorithm that systematically accommodates surface forces and linearly viscoelastic three-dimensional deformation computed via Attard's model into the bimodal AFM framework. The proposed algorithm simultaneously satisfies the amplitude reduction formulas for both resonant eigenmodes and enables the rigorous prediction and interpretation of bimodal AFM observables with a first-principles approach. We used the proposed algorithm to predict the dependence of bimodal AFM observables on local adhesion and standard linear solid (SLS) constitutive parameters as well as operating conditions. Secondly, we present an inverse method to quantitatively predict the local adhesion and SLS viscoelastic parameters from bimodal AFM data acquired on a heterogeneous sample. We demonstrate the method experimentally using bimodal AFM on polystyrene-low density polyethylene (PS-LDPE) polymer blend. This inverse method enables the quantitative discrimination of adhesion and viscoelastic properties from bimodal AFM maps of such samples and opens the door for advanced computational interaction models to be used to quantify local nanomechanical properties of adhesive, viscoelastic materials using bimodal AFM.