Scurr David J, Hook Andrew L, Burley Jonathan A, Williams Philip M, Anderson Daniel G, Langer Robert C, Davies Martyn C, Alexander Morgan R
Laboratory of Biophysics and Surface Analysis, University of Nottingham Nottingham, NG7 2RD, UK.
Surf Interface Anal. 2013 Jan;45(1):466-470. doi: 10.1002/sia.5040. Epub 2012 May 22.
Polymer microarrays are a key enabling technology for high throughput materials discovery. In this study, multivariate image analysis, specifically multivariate curve resolution (MCR), is applied to the hyperspectral time of flight secondary ion mass spectroscopy (ToF-SIMS) data from eight individual microarray spots. Rather than analysing the data individually, the data-sets are collated and analysed as a single large data-set. Desktop computing is not a practical method for undertaking MCR analysis of such large data-sets due to the constraints of memory and computational overhead. Here, a distributed memory High-Performance Computing facility (HPC) is used. Similar to what is achieved using MCR analysis of individual samples, the results from this consolidated data-set allow clear identification of the substrate material; furthermore, specific chemistries common to different spots are also identified. The application of the HPC facility to the MCR analysis of ToF-SIMS hyperspectral data-sets demonstrates a potential methodology for the analysis of macro-scale data without compromising spatial resolution (data 'binning'). Copyright © 2012 John Wiley & Sons, Ltd.
聚合物微阵列是高通量材料发现的一项关键支撑技术。在本研究中，多变量图像分析，特别是多变量曲线分辨（MCR），被应用于来自八个单独微阵列点的高光谱飞行时间二次离子质谱（ToF-SIMS）数据。不是单独分析数据，而是将数据集整理并作为一个单一的大数据集进行分析。由于内存和计算开销的限制，桌面计算不是对如此大的数据集进行MCR分析的实用方法。在此，使用了分布式内存高性能计算设施（HPC）。与对单个样品进行MCR分析所获得的结果类似，这个合并数据集的结果允许清晰识别基底材料；此外，还识别出了不同点共有的特定化学组成。将HPC设施应用于ToF-SIMS高光谱数据集的MCR分析，展示了一种在不影响空间分辨率（数据“分箱”）的情况下分析宏观尺度数据的潜在方法。版权所有© 2012约翰·威利父子有限公司。
