We propose tabular two-dimensional correlation 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
datasets of carbon nanotube (CNTs) films annealed at various temperatures and
revealed the complexity of their hierarchical structures, which include
elements like 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 8 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 like 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.

我们提出了用于从多方位表征数据中提取特征的二维表状相关分析方法，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，结合分层聚类和异步相关。我们将该方法应用于在不同温度退火的碳纳米管（CNTs）薄膜数据集，并揭示了其包括空隙、束缚和非晶碳等元素在内的层次结构的复杂性。我们的分析解决了试图理解结构变化序列的挑战，尤其是在具有由 8 种表征方法得到的 11 个结构参数与复杂行为相互作用的多方位表征数据中。结果显示，相位滞后（从刺激中异步变化）和参数相似性可以阐明材料中结构变化的序列，深入了解退火 CNTs 中非晶碳的去除和石墨化等现象提供了洞察。即使在数据有限的情况下，这种方法也具有益处，并在广泛的材料分析领域具有潜力，展示了阐明复杂材料行为和性质的潜力。