Department of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, United States.
Kratos Analytical Limited, Manchester M17 1 GP, U.K.
ACS Comb Sci. 2020 Nov 9;22(11):641-648. doi: 10.1021/acscombsci.0c00097. Epub 2020 Aug 20.
Combinatorial synthesis and high-throughput characterization of a Ni-Ti-Co thin film materials library are reported for exploration of reversible martensitic transformation. The library was prepared by magnetron co-sputtering, annealed in vacuum at 500 °C without atmospheric exposure, and evaluated for shape memory behavior as an indicator of transformation. Composition, structure, and transformation behavior of the 177 pads in the library were characterized using high-throughput wavelength dispersive spectroscopy (WDS), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and four-point probe temperature-dependent resistance (()) measurements. A new, expanded composition space having phase transformation with low thermal hysteresis and Co > 10 at. % is found. Unsupervised machine learning methods of hierarchical clustering were employed to streamline data processing of the large XRD and XPS data sets. Through cluster analysis of XRD data, we identified and mapped the constituent structural phases. Composition-structure-property maps for the ternary system are made to correlate the functional properties to the local microstructure and composition of the Ni-Ti-Co thin film library.
报道了一种 Ni-Ti-Co 薄膜材料库的组合合成和高通量表征，用于探索可逆马氏体相变。该文库通过磁控共溅射制备，在 500°C 真空中退火，不暴露于大气中，并评估其形状记忆行为作为转变的指标。使用高通量波长色散光谱 (WDS)、X 射线光电子能谱 (XPS)、X 射线衍射 (XRD) 和四点探针温度相关电阻 () 测量对文库中的 177 个样品进行了组成、结构和转变行为的表征。发现了具有低热滞后和 Co > 10 at. % 的相变的新的扩展组成空间。采用分层聚类的无监督机器学习方法简化了大型 XRD 和 XPS 数据集的数据处理。通过 XRD 数据的聚类分析，我们确定并绘制了组成结构相。制作三元系的组成-结构-性能图，将功能特性与 Ni-Ti-Co 薄膜库的局部微观结构和组成相关联。
