Agglomeration applying high intensity mixing (mechano-fusion) – An integrated approach towards the synthesis of tailored hetero-aggregates combining experiments with 2D and 3D structural characterization via image analysis and stochastic modeling

异质聚集体的特殊性质源于聚集体内部的颗粒-颗粒接触。接触点的数量以及接触点处的微观材料相互作用对宏观应用性能起决定性作用。例如电导率、离子导电性或导热性等传输特性。第一个资助期研究混合，因此集合体的几何三维结构。该项目采用介于研磨和高强度混合过程之间的机械融合过程，从初级纳米颗粒系统中产生异质聚集体。骨料的合成是由于一个动态的去团聚和再团聚过程而发生的，在这个过程中，机器中的高剪切应力和屈服应力导致骨料中稳定的颗粒-颗粒接触。该工艺能够生成具有异质骨料涂层的核心颗粒和大块异质骨料。研究将量化主要颗粒的分散和材料特性，这是确定骨料设计所必需的。异质聚集体提交了二维和三维表征方法，以量化单个聚集体结构内主要颗粒的混合状态。纳米和微计算机断层扫描（CT）数据的图像分析提供了详细的3d洞察聚集体。分析采用统计数据挖掘的方法确定异质聚集体结构描述符向量的多元概率分布，并利用空间随机建模的方法创建虚拟（但真实）的聚集体，即模拟聚集体，从而生成一个大型数据库来应用机器学习算法。相关表征方法，如原子力显微镜在二维与微ct相结合，允许交叉检查是否也可行，从平面，即混合状态的二维测量预测三维结构集合体的性质。第二个资助期还将着眼于原子水平，特别是不同材料相互作用的异质接触。机械熔合过程中强烈的剪切和屈服应力能够调整材料在这些接触点的焊接，从而产生或增强宏观性能。最后，将推导定量的工艺-结构-性能关系，将工艺参数与真实和模拟的异质聚集体的三维形态及其宏观物理性质相关联。

The special properties of hetero-aggregates derive from the particle-particle contacts within the aggregate. The number of contacts as well as microscopic material interaction at the contacts play a decisive role regarding macroscopic application properties. Examples are transport properties like electric, ionic or thermal conductivity. The first funding period investigates the mixing and therefore the geometric 3D structure of the aggregates. The project applies the mechano-fusion process, which is situated between grinding and high intensity mixing processes, to generate hetero-aggregates from primary nano-particle systems. The aggregate synthesis occurs due to a dynamic de-agglomeration and re-agglomeration process, where the high shear and yield stresses in the machine lead to stable particle-particle contacts in the aggregate. The process is able to generate both core particles with a hetero-aggregate coating and bulk hetero-aggregates. The investigations will quantify the disperse and material properties of the primary particles, which are required for a defined aggregate design. The hetero-aggregates are submitted to 2D- and 3D-characterization methods to quantify the state of mixing of the primary particles within the individual aggregate structures. Image analysis of nano- and µ-computer tomography (CT) data provides detailed 3D-insight into the aggregates. The analysis uses methods of statistical data mining to determine the multivariate probability distribution of structural descriptor vectors of hetero-aggregates, and spatial stochastic modeling to create virtual (but realistic) aggregates, i.e. simulated aggregates, to generate a large data base to apply machine learning algorithms. Correlative characterization methods, e.g. the combination of atomic force microscopy at 2D with µ-CT, allow to cross check whether it is also feasible to forecast 3D structural aggregate properties from planar, i.e. 2D measurements of the mixing state. The second funding period will also have a look at the atomic level, especially at the hetero-contacts, where different materials interact with each other. The intensive shear und yield stressing in the mechano-fusion process is able to tailor the material welding at these contact points generating or enhancing macroscopic properties. Finally, quantitative process-structure-property relationships will be derived, correlating process parameters with the 3D morphology of real and simulated hetero-aggregates and their macroscopic physical properties.