Multimodal X-ray nano-spectromicroscopy analysis of chemically heterogeneous systems.
Multimodal X-ray nano-spectromicroscopy analysis of chemically heterogeneous systems.
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DOI：
10.1093/mtomcs/mfac078
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发表时间：
2022-10-20
期刊：
影响因子：
3.4
通讯作者：
Chu, Yong S.
中科院分区：
文献类型：
--
作者：
Pattammattel, Ajith;Tappero, Ryan;Gavrilov, Dmitri;Zhang, Hongqiao;Aronstein, Paul;Forman, Henry Jay;O'Day, Peggy A.;Yan, Hanfei;Chu, Yong S.
了解异质系统在其原生环境中的纳米级化学形态对于生命和环境科学、生物地球化学和材料科学等多个学科至关重要。基于同步加速器的 X 射线光谱显微镜工具因其高穿透深度和灵敏度而被广泛用于了解复杂材料系统的化学和形态。光谱显微镜数据的多维 (4D+) 结构带来了可视化和数据缩减的挑战。本文报告了光谱显微镜数据可视化和分析的策略。我们创建了一个名为 XMIDAS（X 射线多模态图像数据分析软件）的新图形用户界面和数据分析平台，用于可视化图像和光谱表示中的光谱显微镜数据。交互式数据分析工具包将传统分析方法与成熟的机器学习分类算法（例如非负矩阵分解）相结合，以减少数据。然后使用具有已知化学成分的模型颗粒聚集体来定义和优化数据可视化和分析方法。基于纳米探针的 X 射线荧光 (nano-XRF) 和 X 射线吸收近边缘结构 (nano-XANES) 光谱显微镜技术用于探测聚集样品的元素和化学状态信息。我们使用 XMIDAS 阐释了模型粒子的完整化学形态分析方法。接下来，我们演示了这种方法在检测和表征与肺泡巨噬细胞相关的纳米颗粒中的应用。我们的多模态方法结合了纳米 XRF、纳米 XANES 和微分相衬成像，有效地可视化了局部纳米结构的化学和形态。我们相信，优化的数据缩减策略和工具开发将有助于使用 X 射线光谱显微镜技术对复杂的生物和环境样品进行分析。 XMIDAS：光谱显微镜数据分析工具。
Understanding the nanoscale chemical speciation of heterogeneous systems in their native environment is critical for several disciplines such as life and environmental sciences, biogeochemistry, and materials science. Synchrotron-based X-ray spectromicroscopy tools are widely used to understand the chemistry and morphology of complex material systems owing to their high penetration depth and sensitivity. The multidimensional (4D+) structure of spectromicroscopy data poses visualization and data-reduction challenges. This paper reports the strategies for the visualization and analysis of spectromicroscopy data. We created a new graphical user interface and data analysis platform named XMIDAS (X-ray multimodal image data analysis software) to visualize spectromicroscopy data from both image and spectrum representations. The interactive data analysis toolkit combined conventional analysis methods with well-established machine learning classification algorithms (e.g. nonnegative matrix factorization) for data reduction. The data visualization and analysis methodologies were then defined and optimized using a model particle aggregate with known chemical composition. Nanoprobe-based X-ray fluorescence (nano-XRF) and X-ray absorption near edge structure (nano-XANES) spectromicroscopy techniques were used to probe elemental and chemical state information of the aggregate sample. We illustrated the complete chemical speciation methodology of the model particle by using XMIDAS. Next, we demonstrated the application of this approach in detecting and characterizing nanoparticles associated with alveolar macrophages. Our multimodal approach combining nano-XRF, nano-XANES, and differential phase-contrast imaging efficiently visualizes the chemistry of localized nanostructure with the morphology. We believe that the optimized data-reduction strategies and tool development will facilitate the analysis of complex biological and environmental samples using X-ray spectromicroscopy techniques. XMIDAS: A spectromicroscopy data analysis tool.
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影响因子：
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