We propose tabular two-dimensional correlation spectroscopy analysis for extracting features from multifaceted characterization data, essential for understanding material properties. This method visualizes similarities and phase lags in structural parameter changes through heatmaps, combining hierarchical clustering and asynchronous correlations. We applied the proposed method to data sets of carbon nanotube (CNT) films annealed at various temperatures and revealed the complexity of their hierarchical structures, which include elements such as voids, bundles, and amorphous carbon. Our analysis addresses the challenge of attempting to understand the sequence of structural changes, especially in multifaceted characterization data where 11 structural parameters derived from eight characterization methods interact with complex behavior. The results show how phase lags (asynchronous changes from stimuli), and parameter similarities can illuminate the sequence of structural changes in materials, providing insights into phenomena such as the removal of amorphous carbon and graphitization in annealed CNTs. This approach is beneficial even with limited data and holds promise for a wide range of material analyses, demonstrating its potential in elucidating complex material behaviors and properties.

中文翻译：

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多方面表征数据的表格二维相关分析

我们提出了表格二维相关光谱分析，用于从多方面表征数据中提取特征，这对于理解材料特性至关重要。该方法通过热图可视化结构参数变化的相似性和相位滞后，结合层次聚类和异步相关性。我们将所提出的方法应用于在不同温度下退火的碳纳米管（CNT）薄膜的数据集，并揭示了其分层结构的复杂性，其中包括空隙、束和无定形碳等元素。我们的分析解决了尝试理解结构变化序列的挑战，特别是在多方面的表征数据中，其中来自八种表征方法的 11 个结构参数与复杂行为相互作用。结果显示了相位滞后（刺激引起的异步变化）和参数相似性如何阐明材料结构变化的顺序，从而深入了解退火碳纳米管中无定形碳的去除和石墨化等现象。即使数据有限，这种方法也是有益的，并且有望进行广泛的材料分析，展示其在阐明复杂材料行为和属性方面的潜力。