DeepMixing – Quantification of the mixing and interfacial hetero-characteristics of nanoparticle aggregates forming in an aerosol mixing zone

在包括气体传感器、催化剂和电池在内的各种应用中，需要两种不同纳米材料之间的功能性异质接触，以实现跨界面的有效传输过程。在该项目中，通过在双火焰喷雾热解（DFSP）装置中结合两种纳米颗粒聚集体产生火焰，实现了气相中异质接触的制备和配方。产品功能取决于混合程度，混合程度决定了异质接触的数量和异质接触的质量。异质接触的质量显着取决于接触面积和原子结构的接口，包括晶格应变和缺陷化学，如果界面传输过程管理的功能。DFSP工艺参数的变化使得能够调整混合工艺，并在各种应用中改善功能。然而，在聚集体和颗粒尺度上的异质接触的表征远未被理解，其中约10 nm的初级颗粒的小尺寸需要使用透射电子显微镜（TEM）成像以实现结构的足够分辨率。因此，在本项目中，研究了具有异质接触的聚集体的混合状态的表征和量化，其中术语混合状态明确地包括混合程度和接触质量。我们应用（S）TEM作为间接方法（3D聚集体的2D投影），其使用具有探针形成透镜的球面像差校正器的SPECTRA 300显微镜。STEM模式的分辨率为50 pm，可以在原子尺度上研究界面的结构质量。该设备配备了用于采集2D元素图的现代硅漂移EDX检测器、分析断层扫描保持器和轴上360°旋转断层扫描保持器。该保持器允许在3D中重建成分分布，用于我们的直接表征方法。我们将开发和研究基于监督机器学习算法的混合状态的自动评估，其中所需的训练数据集（>100.000张图像）由合成（计算机生成）3D聚合组成，其中定义的混合状态提供了基础事实。的并行方法的开发断层重建的聚集体将提供实验的聚集体结构和混合物状态的三维验证，并使详细的调查的3D异质接触界面在原子水平。

Functional hetero-contacts between two different nanomaterials are required in various applications including gas sensors, catalysis and batteries to achieve efficient transport processes across the interfaces. In this project, the preparation and formulation of hetero-contacts in the gas phase is realized through the combination of two nanoparticle aggregate producing flames in a double flame spray pyrolysis (DFSP) setup.The product functionality depends on the degree of mixing that determines the number of hetero-contacts and on the hetero-contact quality. The hetero-contact quality significantly depends on the contact area and on the atomic structure of the interface including lattice strain and defect chemistry, if interfacial transport processes govern the functionality. The variation of DFSP process parameters enables the adjustment of the mixing process and resulted in improved functionalities in various applications. However, the characterization of the hetero-contacts on the aggregate and particle scale is far from understood, where the small size of the primary particles of about 10 nm requires the use of transmission electron microscopy (TEM) imaging to achieve a sufficient resolution of the structures. Therefore, the characterization and quantification of the mixture state of aggregates with hetero-contacts are investigated in this project, where the term mixture state explicitly includes the degree of mixing and the contact quality. We apply (S)TEM as indirect method (2D projection of 3D aggregate) using a SPECTRA 300 microscope with a corrector of the spherical aberration of the probe forming lens. The resolution in the STEM mode is 50 pm allowing to study the structural quality of interfaces on an atomic scale. The machine is equipped with a modern silicon drift EDX detector for acquisition of 2D elemental maps, an analytical tomography holder and an on-axis 360° rotation tomography holder. This holder allows reconstruction of composition distribution in 3D for our direct characterization method. We will develop and investigate an automated evaluation of mixture states based on supervised machine learning algorithms, where required training datasets (>100.000 images) consist of synthetic (computer generated) 3D aggregates with defined mixture states providing the ground truth. The parallel method development for tomographic reconstructions of the aggregates will provide the experimental 3D validation of aggregate structures and the mixture states and enable the detailed investigation of the 3D hetero-contact interfaces in the atomistic level.