Hook Andrew L, Scurr David J, Burley Jonathan C, Langer Robert, Anderson Daniel G, Davies Martyn C, Alexander Morgan R
Laboratory of Biophysics and Surface Analysis , University of Nottingham , UK NG7 2RD . Email:
David H. Koch Institute for Integrative Cancer Research , Massachusetts Institute of Technology , USA 02139.
J Mater Chem B. 2013 Feb 21;1(7):1035-1043. doi: 10.1039/c2tb00379a. Epub 2012 Dec 20.
Polymer microarrays are a key enabling technology for the discovery of novel materials. This technology can be further enhanced by expanding the combinatorial space represented on an array. However, not all materials are compatible with the microarray format and materials must be screened to assess their suitability with the microarray manufacturing methodology prior to their inclusion in a materials discovery investigation. In this study a library of materials expressed on the microarray format are assessed by light microscopy, atomic force microscopy and time-of-flight secondary ion mass spectrometry to identify compositions with defects that cause a polymer spot to exhibit surface properties significantly different from a smooth, round, chemically homogeneous 'normal' spot. It was demonstrated that the presence of these defects could be predicted in 85% of cases using a partial least square regression model based upon molecular descriptors of the monomer components of the polymeric materials. This may allow for potentially defective materials to be identified prior to their formation. Analysis of the PLS regression model highlighted some chemical properties that influenced the formation of defects, and in particular suggested that mixing a methacrylate and an acrylate monomer and/or mixing monomers with long and linear or short and bulky pendant groups will prevent the formation of defects. These results are of interest for the formation of polymer microarrays and may also inform the formulation of printed polymer materials generally.
聚合物微阵列是发现新型材料的关键支撑技术。通过扩大阵列上所代表的组合空间，这项技术可以得到进一步提升。然而，并非所有材料都与微阵列形式兼容，在将材料纳入材料发现研究之前，必须对其进行筛选，以评估它们是否适合微阵列制造方法。在本研究中，通过光学显微镜、原子力显微镜和飞行时间二次离子质谱法对以微阵列形式呈现的材料库进行评估，以识别具有缺陷的成分，这些缺陷会导致聚合物斑点呈现出与光滑、圆形、化学均匀的“正常”斑点显著不同的表面特性。结果表明，使用基于聚合材料单体成分分子描述符的偏最小二乘回归模型，在85%的情况下可以预测这些缺陷的存在。这可能使得潜在有缺陷的材料在形成之前就被识别出来。对偏最小二乘回归模型的分析突出了一些影响缺陷形成的化学性质，特别是表明混合甲基丙烯酸酯和丙烯酸酯单体和/或混合具有长且线性或短且庞大侧基的单体将防止缺陷的形成。这些结果对于聚合物微阵列的形成具有重要意义，也可能为一般印刷聚合物材料的配方提供参考。
