Lin David C, Dimitriadis Emilios K, Horkay Ferenc
Laboratory of Integrative and Medical Biophysics, National Institutes of Health, 9 Memorial Drive, Bldg. 9 Rm. 1E118, Bethesda, MD 20892, USA.
J Biomech Eng. 2007 Jun;129(3):430-40. doi: 10.1115/1.2720924.
The atomic force microscope (AFM) has found wide applicability as a nanoindentation tool to measure local elastic properties of soft materials. An automated approach to the processing of AFM indentation data, namely, the extraction of Young's modulus, is essential to realizing the high-throughput potential of the instrument as an elasticity probe for typical soft materials that exhibit inhomogeneity at microscopic scales. This paper focuses on Hertzian analysis techniques, which are applicable to linear elastic indentation. We compiled a series of synergistic strategies into an algorithm that overcomes many of the complications that have previously impeded efforts to automate the fitting of contact mechanics models to indentation data. AFM raster data sets containing up to 1024 individual force-displacement curves and macroscopic compression data were obtained from testing polyvinyl alcohol gels of known composition. Local elastic properties of tissue-engineered cartilage were also measured by the AFM. All AFM data sets were processed using customized software based on the algorithm, and the extracted values of Young's modulus were compared to those obtained by macroscopic testing. Accuracy of the technique was verified by the good agreement between values of Young's modulus obtained by AFM and by direct compression of the synthetic gels. Validation of robustness was achieved by successfully fitting the vastly different types of force curves generated from the indentation of tissue-engineered cartilage. For AFM indentation data that are amenable to Hertzian analysis, the method presented here minimizes subjectivity in preprocessing and allows for improved consistency and minimized user intervention. Automated, large-scale analysis of indentation data holds tremendous potential in bioengineering applications, such as high-resolution elasticity mapping of natural and artificial tissues.
原子力显微镜（AFM）作为一种纳米压痕工具，已在测量软材料的局部弹性特性方面得到广泛应用。一种用于处理AFM压痕数据的自动化方法，即杨氏模量的提取，对于实现该仪器作为典型软材料弹性探针的高通量潜力至关重要，这些软材料在微观尺度上表现出不均匀性。本文重点介绍适用于线性弹性压痕的赫兹分析技术。我们将一系列协同策略整合到一种算法中，该算法克服了许多以前阻碍将接触力学模型自动拟合到压痕数据的复杂问题。通过测试已知成分的聚乙烯醇凝胶，获得了包含多达1024条单独力 - 位移曲线的AFM光栅数据集以及宏观压缩数据。还通过AFM测量了组织工程软骨的局部弹性特性。所有AFM数据集均使用基于该算法的定制软件进行处理，并将提取的杨氏模量值与通过宏观测试获得的值进行比较。通过AFM获得的杨氏模量值与合成凝胶直接压缩获得的值之间的良好一致性，验证了该技术的准确性。通过成功拟合组织工程软骨压痕产生的截然不同类型的力曲线，实现了稳健性验证。对于适用于赫兹分析的AFM压痕数据，本文提出的方法在预处理中最大限度地减少了主观性，并提高了一致性，同时将用户干预降至最低。压痕数据的自动化大规模分析在生物工程应用中具有巨大潜力，例如对天然和人工组织进行高分辨率弹性映射。
