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

应用高强度混合（机械融合）进行团聚 â 一种通过图像分析和随机建模将实验与 2D 和 3D 结构表征相结合的合成定制异质聚集体的综合方法

• 批准号: 462365306
• 负责人: Professor Dr.-Ing. Urs Peuker
• 金额: --
• 依托单位: Institut für Stochastik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/462365306?language=en
• 关键词: Agglomeration; applying; intensity; mixing; mechano

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.

异相聚集体的特殊性质来源于聚集体中颗粒与颗粒之间的接触。接触点的数量以及接触点处的微观材料相互作用对于宏观应用性能起着决定性作用。例如传输特性，如电导率、离子电导率或热导率。第一个供资期研究混合，从而研究骨料的几何3D结构。该项目采用机械融合工艺，该工艺位于研磨和高强度混合工艺之间，从初级纳米颗粒系统中产生异质聚集体。聚集体合成由于动态解聚和再团聚过程而发生，其中机器中的高剪切和屈服应力导致聚集体中稳定的颗粒-颗粒接触。该方法能够产生具有异质聚集体涂层的核颗粒和本体异质聚集体。调查将量化一次颗粒的分散和材料特性，这是确定骨料设计所需的。将异质聚集体提交给2D和3D表征方法，以量化单个聚集体结构内的初级颗粒的混合状态。纳米和微米计算机断层扫描（CT）数据的图像分析提供了对聚集体的详细3D洞察。该分析使用统计数据挖掘的方法来确定异质聚集体的结构描述符向量的多变量概率分布，以及空间随机建模来创建虚拟（但现实的）聚集体，即模拟聚集体，以生成大型数据库来应用机器学习算法。相关的表征方法，例如2D原子力显微镜与µ-CT的组合，允许交叉检查是否也可以从平面（即混合状态的2D测量）预测3D结构聚集体特性。第二个资助期也将着眼于原子水平，特别是异质接触，不同的材料相互作用。机械熔合过程中的强烈剪切和屈服应力能够在这些接触点处定制材料焊接，从而产生或增强宏观性能。最后，定量的工艺-结构-性能关系将被导出，将工艺参数与真实的和模拟的异质聚集体的3D形态及其宏观物理性质相关联。